gender stereotyping in the workplace essay

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Research Roundup: How Women Experience the Workplace Today

Dagny Dukach

gender stereotyping in the workplace essay

New studies on what happens when women reach the top, the barriers they still face, and the (sometimes hidden) stresses they deal with.

What will it take to make gender equity in the workplace a reality? It’s a complicated question, with no easy answers — but research from a wide array of academic disciplines aims to expand our understanding of the unique challenges and opportunities women face today. In this research roundup, we share highlights from several new and forthcoming studies that explore the many facets of gender at work.

In 2021, the gender gap in U.S. workforce participation hit an all-time low . But of course, substantial gender disparities persist in pay, leadership representation, access to resources, and many other key metrics. How can we make sense of all these different dimensions of gender equity in the workplace?

Dagny Dukach is a former associate editor at Harvard Business Review.

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How Gender Stereotypes Kill a Woman’s Self-Confidence

Women make up more than half of the labor force in the United States and earn almost 60 percent of advanced degrees, yet they bring home less pay and fill fewer seats in the C-suite than men, particularly in male-dominated professions like finance and technology.

This gender gap is due in part to “occupational sorting,” with men choosing careers that pay higher wages than women do, labor economists say. For example, women represent only 26 percent of US workers employed in computer and math jobs, according to the Department of Labor.

New research identifies one reason women might be shying away from certain professions: They lack confidence in their ability to compete in fields that men are stereotypically believed to perform more strongly in, such as science, math, and technology.

Women are also more reluctant to share their ideas in group discussions on these subjects. And even when they have talent—and are actually told they are high-achievers in these subjects—women are more likely than men to shrug off the praise and lowball their own abilities.

This weak self-confidence may hold some women back as they count themselves out of pursuing prestigious roles in professions they believe they won’t excel in, despite having the skills to succeed, says Harvard Business School Assistant Professor Katherine B. Coffman .

“Our beliefs about ourselves are important in shaping all kinds of important decisions, such as what colleges we apply to, which career paths we choose, and whether we are willing to contribute ideas in the workplace or try to compete for a promotion,” Coffman says. “If talented women in STEM aren’t confident, they might not even look at those fields in the first place. It’s all about how good we think we are, especially when we ask ourselves, ‘What does it make sense for me to pursue?’”

Coffman has recently co-written an article in the American Economic Review as well as two working papers, all aimed at studying men’s and women’s beliefs about their own abilities.

“Women are more likely than men to shrug off the praise and lowball their own abilities.”

What she found, in essence, is that gender stereotypes distort our views of both ourselves and others—and that may be especially troubling for women, since buying into those stereotypes could be creating a bleak self-image that is setting them back professionally.

Here’s a snapshot of findings from all three research studies:

Women are less confident than men in certain subjects, like math

In a study for the journal article Beliefs about Gender , Coffman and her colleagues asked participants to answer multiple-choice trivia questions in several categories that women are perceived to have a better handle on, like the Kardashians, Disney movies, cooking, art and literature, and verbal skills. Then they were quizzed in categories considered favorable for men, such as business, math, videogames, cars, and sports.

Respondents were asked to estimate how many questions they answered correctly on tests, and to guess the performance of a random partner whose gender was revealed. Both men and women exaggerated the actual gender performance gaps on average, overstating the male advantage in male-typed domains as well as overstating the female advantage in female-typed questions. And in predicting their own abilities, women had much less confidence in their scores on the tests they believed men had an advantage in.

“Gender stereotypes determine people’s beliefs about themselves and others,” Coffman says. “If I take a woman who has the exact same ability in two different categories—verbal and math—just the fact that there’s an average male advantage in math shapes her belief that her own ability in math is lower.”

Women discount positive feedback about their abilities

In an experiment for Coffman’s working paper Stereotypes and Belief Updating , participants completed a timed test of cognitive ability in five areas: general science, arithmetic reasoning, math knowledge, mechanical comprehension, and assembling objects. They were asked to guess their total number of correct answers, as well as how their performance compared to others. A woman who actually had the same score as a man estimated her score to be 0.58 points lower, a statistically significant gap. Even more surprising, even after participants were provided with feedback about how they performed, this gender gap in how well they perceived they did continued.

In a second study participants were asked to guess how they performed on a test in a randomly assigned subject matter and to predict their own rank relative to others completing the same test. The researchers then provided participants with feedback about their performance. They found that both men and women discounted good news about their scores in subjects that their gender was perceived to have more trouble with.

Stereotypes play on our minds so strongly that it becomes tougher to convince people of their talent in fields where they believe their gender is weak, Coffman says.

“A policy prescription to correct a confidence gap in women might be: Let’s find talented women and tell them, ‘Hey, you’re good at math. You got a really good score on this math test,'" she says. “But our results suggest that this feedback is less effective in closing the gender gap than we might hope. It’s harder than we thought to convince women in male-typed fields that they’ve performed well in these fields.”

It’s unclear whether women would feel better about their abilities if they received repeated rounds of positive feedback, rather than one piece of good news. “I’d be interested to find out if the gender bias gets smaller over time, once a woman has heard that she’s good at math over and over again,” Coffman says. “You might have to encourage women a few times if you want to close these gaps.”

"Our work suggests a need for structuring group decision-making in a way that assures the most talented members both volunteer and are recognized for their contributions, despite gender stereotypes.”

It's important to note, Coffman says, that these studies also show that men have less confidence than women in their ability to shine in fields dominated by women. “It’s not that women are simply less confident; what we find consistently is that individuals are less confident in fields that are more stereotypically outside of their gender’s domain,” Coffman says.

Women hold back on expressing ideas on ‘male topics’

In a third paper, Gender Stereotypes in Deliberation and Team Decisions, Coffman and colleagues studied how teams discuss, decide on, and reward ideas in a group.

The research team compared the behavior of two groups that had free-form discussions in response to questions that varied in the amount of “maleness” of the topic. In one group, the gender of each participant was known, and in the other group, the gender of speakers was not identifiable. They found that men and women had the same ability to answer the questions, yet once again, gender stereotypes warped people’s responses.

As the “maleness” of the question increased, women were significantly less likely than men to self-promote their ideas within the group when their gender was known, particularly in cases where only one woman was talking with a bunch of men. But in the groups where gender was unknown, no gender differences were found in terms of how much women and men talked up their ideas or were recognized by others for their input.

The researchers even found that stereotypes seemed to play a role in the way outside evaluators rated the contributions of each group member after reading transcripts of the conversations. Without knowing the gender of speakers, these evaluators were significantly more likely to guess that participants who came across in the transcripts as “warm,” or friendly, were female and that a negative or critical participant was male—even though researchers found no actual differences in how men and women in the group communicated. Male raters also were significantly less likely to believe that speakers who were judged as “competent” were female. In addition, warmer participants, particularly warmer women, were less likely to be rewarded for their input in the discussions.

Speak up for success

To achieve professional success, people must voice opinions and advocate for their ideas while working in decision-making teams, so it’s a problem if women are staying quiet when it comes to male-typed subjects—and if their ideas are appreciated less when they do express them, Coffman says.

“Our work suggests a need for structuring group decision-making in a way that assures the most talented members both volunteer and are recognized for their contributions, despite gender stereotypes,” the paper says.

It’s also important for managers to be aware of how confidence gaps may impact the workplace, particularly in professions long dominated by men, and to realize that women may need extra encouragement to express their ideas or to throw their hat in the ring for a promotion, Coffman says.

“I would encourage business leaders to think about how [workers’ confidence levels] impact the processes in their organizations,” Coffman says. “I would say providing extra feedback is a good start. If you as an employer see talent somewhere, reaching out to make sure the person is encouraged, recognized, and rewarded—not just once, but repeatedly—could be a helpful thing to do.”

With this new data on gender stereotyping, Coffman and her colleagues hope their work will help inform future research to piece together answers to some puzzling questions, like why men and women alike believe that men will perform better than women in some domains and what interventions can be considered to close this gender gap in self-confidence.

“Stereotypes are pervasive, widely-held views that shape beliefs about our own and others’ abilities, likely from a very young age,” Coffman says. “Until we can change these stereotypes, it’s essential to think about how we can better inoculate individuals from biases induced by stereotypes, helping people to pursue fulfilling careers in the areas where their passions and talents lie.”

Dina Gerdeman is senior editor at Harvard Business School Working Knowledge.

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Women in the Workplace 2023

Women in the Workplace

This is the ninth year of the Women in the Workplace report. Conducted in partnership with LeanIn.Org , this effort is the largest study of women in corporate America and Canada. This year, we collected information from 276 participating organizations employing more than ten million people. At these organizations, we surveyed more than 27,000 employees and 270 senior HR leaders, who shared insights on their policies and practices. The report provides an intersectional look at the specific biases and barriers faced by Asian, Black, Latina, and LGBTQ+ women and women with disabilities.

About the authors

This year’s research reveals some hard-fought gains at the top, with women’s representation in the C-suite at the highest it has ever been. However, with lagging progress in the middle of the pipeline—and a persistent underrepresentation of women of color 1 Women of color include women who are Asian, Black, Latina, Middle Eastern, mixed race, Native American/American Indian/Indigenous/Alaskan Native, and Native Hawaiian or Pacific Islander. Due to small sample sizes for other racial and ethnic groups, reported findings on individual racial and ethnic groups are restricted to Asian women, Black women, and Latinas. —true parity remains painfully out of reach.

The survey debunks four myths about women’s workplace experiences and career advancement. A few of these myths cover old ground, but given the notable lack of progress, they warrant repeating. These include women’s career ambitions, the greatest barrier to their ascent to senior leadership, the effect and extent of microaggressions in the workplace, and women’s appetite for flexible work. We hope highlighting these myths will help companies find a path forward that casts aside outdated thinking once and for all and accelerates progress for women.

The rest of this article summarizes the main findings from the Women in the Workplace 2023 report and provides clear solutions that organizations can implement to make meaningful progress toward gender equality.

State of the pipeline

Over the past nine years, women—and especially women of color—have remained underrepresented across the corporate pipeline (Exhibit 1). However, we see a growing bright spot in senior leadership. Since 2015, the number of women in the C-suite has increased from 17 to 28 percent, and the representation of women at the vice president and senior vice president levels has also improved signiﬁcantly.

These hard-earned gains are encouraging yet fragile: slow progress for women at the manager and director levels—representation has grown only three and four percentage points, respectively—creates a weak middle in the pipeline for employees who represent the vast majority of women in corporate America. And the “Great Breakup” trend we discovered in last year’s survey continues for women at the director level, the group next in line for senior-leadership positions. That is, director-level women are leaving at a higher rate than in past years—and at a notably higher rate than men at the same level. As a result of these two dynamics, there are fewer women in line for top positions.

To view previous reports, please visit the Women in the Workplace archive

Moreover, progress for women of color is lagging behind their peers’ progress. At nearly every step in the pipeline, the representation of women of color falls relative to White women and men of the same race and ethnicity. Until companies address this inequity head-on, women of color will remain severely underrepresented in leadership positions—and mostly absent from the C-suite.

“It’s disheartening to be part of an organization for as many years as I have been and still not see a person like me in senior leadership. Until I see somebody like me in the C-suite, I’m never going to really feel like I belong.”

—Latina, manager, former executive director

Four myths about the state of women at work

This year’s survey reveals the truth about four common myths related to women in the workplace.

Myth: Women are becoming less ambitious Reality: Women are more ambitious than before the pandemic—and flexibility is fueling that ambition

At every stage of the pipeline, women are as committed to their careers and as interested in being promoted as men. Women and men at the director level—when the C-suite is in closer view—are also equally interested in senior-leadership roles. And young women are especially ambitious. Nine in ten women under the age of 30 want to be promoted to the next level, and three in four aspire to become senior leaders.

Women represent roughly one in four C-suite leaders, and women of color just one in 16.

Moreover, the pandemic and increased ﬂexibility did not dampen women’s ambitions. Roughly 80 percent of women want to be promoted to the next level, compared with 70 percent in 2019. And the same holds true for men. Women of color are even more ambitious than White women: 88 percent want to be promoted to the next level. Flexibility is allowing women to pursue their ambitions: overall, one in ﬁve women say ﬂexibility has helped them stay in their job or avoid reducing their hours. A large number of women who work hybrid or remotely point to feeling less fatigued and burned out as a primary beneﬁt. And a majority of women report having more focused time to get their work done when they work remotely.

The pandemic showed women that a new model of balancing work and life was possible. Now, few want to return to the way things were. Most women are taking more steps to prioritize their personal lives—but at no cost to their ambition. They remain just as committed to their careers and just as interested in advancing as women who aren’t taking more steps. These women are defying the outdated notion that work and life are incompatible, and that one comes at the expense of the other.

Myth: The biggest barrier to women’s advancement is the ‘glass ceiling’ Reality: The ‘broken rung’ is the greatest obstacle women face on the path to senior leadership

For the ninth consecutive year, women face their biggest hurdle at the ﬁrst critical step up to manager. This year, for every 100 men promoted from entry level to manager, 87 women were promoted (Exhibit 2). And this gap is trending the wrong way for women of color: this year, 73 women of color were promoted to manager for every 100 men, down from 82 women of color last year. As a result of this “broken rung,” women fall behind and can’t catch up.

Progress for early-career Black women remains the furthest behind. After rising in 2020 and 2021 to a high of 96 Black women promoted for every 100 men—likely because of heightened focus across corporate America—Black women’s promotion rates have fallen to 2018 levels, with only 54 Black women promoted for every 100 men this year.

While companies are modestly increasing women’s representation at the top, doing so without addressing the broken rung oﬀers only a temporary stopgap. Because of the gender disparity in early promotions, men end up holding 60 percent of manager-level positions in a typical company, while women occupy 40 percent. Since men signiﬁcantly outnumber women, there are fewer women to promote to senior managers, and the number of women decreases at every subsequent level.

Myth: Microaggressions have a ‘micro’ impact Reality: Microaggressions have a large and lasting impact on women

Microaggressions are a form of everyday discrimination that is often rooted in bias. They include comments and actions—even subtle ones that are not overtly harmful—that demean or dismiss someone based on their gender, race, or other aspects of their identity. They signal disrespect, cause acute stress, and can negatively impact women’s careers and health.

Years of data show that women experience microaggressions at a signiﬁcantly higher rate than men: they are twice as likely to be mistaken for someone junior and hear comments on their emotional state (Exhibit 3). For women with traditionally marginalized identities, these slights happen more often and are even more demeaning. As just one example, Asian and Black women are seven times more likely than White women to be confused with someone of the same race and ethnicity.

As a result, the workplace is a mental mineﬁeld for many women, particularly those with traditionally marginalized identities. Women who experience microaggressions are much less likely to feel psychologically safe, which makes it harder to take risks, propose new ideas, or raise concerns. The stakes feel just too high. On top of this, 78 percent of women who face microaggressions self-shield at work, or adjust the way they look or act in an eﬀort to protect themselves. For example, many women code-switch—or tone down what they say or do—to try to blend in and avoid a negative reaction at work. Black women are more than twice as likely as women overall to code-switch. And LGBTQ+ women are 2.5 times as likely to feel pressure to change their appearance to be perceived as more professional. The stress caused by these dynamics cuts deep.

Women who experience microaggressions—and self-shield to deflect them—are three times more likely to think about quitting their jobs and four times more likely to almost always be burned out. By leaving microaggressions unchecked, companies miss out on everything women have to oﬀer and risk losing talented employees.

“It’s like I have to act extra happy so I’m not looked at as bitter because I’m a Black woman. And a disabled Black woman at that. If someone says something offensive to me, I have to think about how to respond in a way that does not make me seem like an angry Black woman.”

—Black woman with a physical disability, entry-level role

Myth: It’s mostly women who want—and benefit from—flexible work Reality: Men and women see flexibility as a ‘top 3’ employee benefit and critical to their company’s success

Most employees say that opportunities to work remotely and have control over their schedules are top company beneﬁts, second only to healthcare (Exhibit 4). Workplace ﬂexibility even ranks above tried-and-true beneﬁts such as parental leave and childcare.

As workplace ﬂexibility transforms from a nice-to-have for some employees to a crucial beneﬁt for most, women continue to value it more. This is likely because they still carry out a disproportionate amount of childcare and household work. Indeed, 38 percent of mothers with young children say that without workplace flexibility, they would have had to leave their company or reduce their work hours.

But it’s not just women or mothers who benefit: hybrid and remote work are delivering important beneﬁts to most employees. Most women and men point to better work–life balance as a primary beneﬁt of hybrid and remote work, and a majority cite less fatigue and burnout (Exhibit 5). And research shows that good work–life balance and low burnout are key to organizational success. Moreover, 83 percent of employees cite the ability to work more eﬃciently and productively as a primary beneﬁt of working remotely. However, it’s worth noting companies see this diﬀerently: only half of HR leaders say employee productivity is a primary beneﬁt of working remotely.

For women, hybrid or remote work is about a lot more than flexibility. When women work remotely, they face fewer microaggressions and have higher levels of psychological safety.

Employees who work on-site also see tangible beneﬁts. A majority point to an easier time collaborating and a stronger personal connection to coworkers as the biggest beneﬁts of working on-site—two factors central to employee well-being and eﬀectiveness. However, the culture of on-site work may be falling short. While 77 percent of companies believe a strong organizational culture is a key beneﬁt of on-site work, most employees disagree: only 39 percent of men and 34 percent of women who work on-site say a key beneﬁt is feeling more connected to their organization’s culture.

Not to mention that men benefit disproportionately from on-site work: compared with women who work on-site, men are seven to nine percentage points more likely to be “in the know,” receive the mentorship and sponsorships they need, and have their accomplishments noticed and rewarded.

A majority of organizations have started to formalize their return-to-office policies, motivated by the perceived benefits of on-site work (Exhibit 6). As they do so, they will need to work to ensure everyone can equally reap the benefits of on-site work.

Recommendations for companies

As companies work to support and advance women, they should focus on five core areas:

tracking outcomes for women’s representation
empowering managers to be effective people leaders
addressing microaggressions head-on
unlocking the full potential of flexible work
fixing the broken rung, once and for all

Sixty percent of companies have increased their financial and staffing investments in diversity, equity, and inclusion over the past year. And nearly three in four HR leaders say DEI is critical to their companies’ future success.

1. Track outcomes to improve women’s experience and progression

Tracking outcomes is critical to any successful business initiative. Most companies do this consistently when it comes to achieving their financial objectives, but few apply the same rigor to women’s advancement. Here are three steps to get started:

Measure employees’ outcomes and experiences—and use the data to fix trouble spots. Outcomes for drivers of women’s advancement include hiring, promotions, and attrition. Visibility into other metrics—such as participation in career development programs, performance ratings, and employee sentiments—that influence career progression is also important, and data should be collected with appropriate data privacy protections in place. Then, it’s critically important that companies mine their data for insights that will improve women’s experiences and create equal opportunities for advancement. Ultimately, data tracking is only valuable if it leads to organizational change.

Take an intersectional approach to outcome tracking. Tracking metrics by race and gender combined should be table stakes. Yet, even now, fewer than half of companies do this, and far fewer track data by other self-reported identifiers, such as LGBTQ+ identity. Without this level of visibility, the experiences and career progression of women with traditionally marginalized identities can go overlooked.

Share internal goals and metrics with employees. Awareness is a valuable tool for driving change—when employees are able to see opportunities and challenges, they’re more invested in being part of the solution. In addition, transparency with diversity, equity, and inclusion (DEI) goals and metrics can send a powerful signal to employees with traditionally marginalized identities that they are supported within the organization.

2. Support and reward managers as key drivers of organizational change

Managers are on the front lines of employees’ experiences and central to driving organizational change. As companies more deeply invest in the culture of work, managers play an increasingly critical role in fostering DEI, ensuring employee well-being, and navigating the shift to flexible work. These are all important business priorities, but managers do not always get the direction and support they need to deliver on them. Here are three steps to get started:

Clarify managers’ priorities and reward results. Companies need to explicitly communicate to managers what is core to their roles and motivate them to take action. The most effective way to do this is to include responsibilities like career development, DEI, and employee well-being in managers’ job descriptions and performance reviews. Relatively few companies evaluate managers on metrics linked to people management. For example, although 61 percent of companies point to DEI as a top manager capability, only 28 percent of people managers say their company recognizes DEI in performance reviews. This discrepancy may partially explain why not enough employees say their manager treats DEI as a priority.

Equip managers with the skills they need to be successful. To effectively manage the new demands being placed on them, managers need ongoing education. This includes repeated, relevant, and high-quality training and nudges that emphasize specific examples of core concepts, as well as concrete actions that managers can incorporate into their daily practices. Companies should adopt an “often and varied” approach to training and upskilling and create regular opportunities for coaching so that managers can continue to build the awareness and capabilities they need to be effective.

Make sure managers have the time and support to get it right. It requires significant intentionality and follow-through to be a good people and culture leader, and this is particularly true when it comes to fostering DEI. Companies need to make sure their managers have the time and resources to do these aspects of their job well. Additionally, companies should put policies and systems in place to make managers’ jobs easier.

3. Take steps to put an end to microaggressions

Microaggressions are pervasive, harmful to the employees who experience them, and result in missed ideas and lost talent. Companies need to tackle microaggressions head-on. Here are three steps to get started:

Make clear that microaggressions are not acceptable. To raise employee awareness and set the right tone, it’s crucial that senior leaders communicate that microaggressions and disrespectful behavior of any kind are not welcome. Companies can help with this by developing a code of conduct that articulates what supportive and respectful behavior looks like—as well as what’s unacceptable and uncivil behavior.

Teach employees to avoid and challenge microaggressions. Employees often don’t recognize microaggressions, let alone know what to say or do to be helpful. That’s why it’s so important that companies have employees participate in high-quality bias and allyship training and receive periodic refreshers to keep key learnings top of mind.

Create a culture where it’s normal to surface microaggressions. It’s important for companies to foster a culture that encourages employees to speak up when they see microaggressions or other disrespectful behavior. Although these conversations can be difficult, they often lead to valuable learning and growth. Senior leaders can play an important role in modeling that it is safe to surface and discuss these behaviors.

4. Finetune flexible working models

The past few years have seen a transformation in how we work. Flexibility is now the norm in most companies; the next step is unlocking its full potential and bringing out the best of the benefits that different work arrangements have to offer. Here are three steps to get started:

Establish clear expectations and norms around working flexibly. Without this clarity, employees may have very different and conflicting interpretations of what’s expected of them. It starts with redefining the work best done in person, versus remotely, and injecting flexibility into the work model to meet personal demands. As part of this process, companies need to find the right balance between setting organization-wide guidelines and allowing managers to work with their teams to determine an approach that unlocks benefits for men and women equally.

Measure the impact of new initiatives to support flexibility and adjust them as needed. The last thing companies want to do is fly in the dark as they navigate the transition to flexible work. As organizations roll out new working models and programs to support flexibility, they should carefully track what’s working, and what’s not, and adjust their approach accordingly—a test-and-learn mentality and a spirit of co-creation with employees are critical to getting these changes right.

Few companies currently track outcomes across work arrangements. For example, only 30 percent have tracked the impact of their return-to-office policies on key DEI outcomes.

Put safeguards in place to ensure a level playing field across work arrangements. Companies should take steps to ensure that employees aren’t penalized for working flexibly. This includes putting systems in place to make sure that employees are evaluated fairly, such as redesigning performance reviews to focus on results rather than when and where work gets done. Managers should also be equipped to be part of the solution. This requires educating managers on proximity bias. Managers need to ensure their team members get equal recognition for their contributions and equal opportunities to advance regardless of working model.

5. Fix the broken rung for women, with a focus on women of color

Fixing the broken rung is a tangible, achievable goal and will set off a positive chain reaction across the pipeline. After nine years of very little progress, there is no excuse for companies failing to take action. Here are three steps to get started:

Track inputs and outcomes. To uncover inequities in the promotions process, companies need to track who is put up for and who receives promotions—by race and gender combined. Tracking with this intersectional lens enables employers to identify and address the obstacles faced by women of color, and companies can use these data points to identify otherwise invisible gaps and refine their promotion processes.

Work to de-bias performance reviews and promotions. Leaders should put safeguards in place to ensure that evaluation criteria are applied fairly and bias doesn’t creep into decision making. Companies can take these actions:

Send “bias” reminders before performance evaluations and promotion cycles, explaining how common biases can impact reviewers’ assessments.
Appoint a “bias monitor” to keep performance evaluations and promotions discussions focused on the core criteria for the job and surface potentially biased decision making.
Have reviewers explain the rationale behind their performance evaluations and promotion recommendations. When individuals have to justify their decisions, they are less likely to make snap judgments or rely on gut feelings, which are prone to bias.

Invest in career advancement for women of color. Companies should make sure their career development programs address the distinct biases and barriers that women of color experience. Yet only a fraction of companies tailor career program content for women of color. And given that women of color tend to get less career advice and have less access to senior leaders, formal mentorship and sponsorship programs can be particularly impactful. It’s also important that companies track the outcomes of their career development programs with an intersectional lens to ensure they are having the intended impact and not inadvertently perpetuating inequitable outcomes.

Practices of top-performing companies

Companies with strong women’s representation across the pipeline are more likely to have certain practices in place. The following data are based on an analysis of top performers—companies that have a higher representation of women and women of color than their industry peers (Exhibit 7).

This year’s survey brings to light important realities about women’s experience in the workplace today. Women, and particularly women of color, continue to lose the most ground in middle management, and microaggressions have a significant and enduring effect on many women—especially those with traditionally marginalized identities. Even still, women are as ambitious as ever, and flexibility is contributing to this, allowing all workers to be more productive while also achieving more balance in their lives. These insights can provide a backdrop for senior leaders as they plan for the future of their organizations.

Emily Field is a partner in McKinsey’s Seattle office; Alexis Krivkovich and Lareina Yee are senior partners in the Bay Area office, where Nicole Robinson is an associate partner; Sandra Kügele is a consultant in the Washington, D.C., office.

The authors wish to thank Zoha Bharwani, Quentin Bolton, Sara Callander, Katie Cox, Ping Chin, Robyn Freeman, James Gannon, Jenn Gao, Mar Grech, Alexis Howard, Isabelle Hughes, Sara Kaplan, Ananya Karanam, Sophia Lam, Nina Li, Steven Lee, Anthea Lyu, Tess Mandoli, Abena Mensah, Laura Padula, David Pinski, Jane Qu, Charlie Rixey, Sara Samir, Chanel Shum, Sofia Tam, Neha Verma, Monne Williams, Lily Xu, Yaz Yazar, and Shirley Zhao for their contributions to this article.

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Gender stereotypes and workplace bias

Research output : Contribution to journal › Review article › peer-review

This paper focuses on the workplace consequences of both descriptive gender stereotypes (designating what women and men are like) and prescriptive gender stereotypes (designating what women and men should be like), and their implications for women's career progress. Its central argument is that gender stereotypes give rise to biased judgments and decisions, impeding women's advancement. The paper discusses how descriptive gender stereotypes promote gender bias because of the negative performance expectations that result from the perception that there is a poor fit between what women are like and the attributes believed necessary for successful performance in male gender-typed positions and roles. It also discusses how prescriptive gender stereotypes promote gender bias by creating normative standards for behavior that induce disapproval and social penalties when they are directly violated or when violation is inferred because a woman is successful. Research is presented that tests these ideas, considers specific career consequences likely to result from stereotype-based bias, and identifies conditions that exaggerate or minimize the likelihood of their occurrence.

ASJC Scopus subject areas

Social Psychology
Experimental and Cognitive Psychology
Organizational Behavior and Human Resource Management

Access to Document

10.1016/j.riob.2012.11.003

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Gender Stereotypes Business & Economics 100%
Work Place Business & Economics 74%
Workplace Medicine & Life Sciences 70%
stereotype Social Sciences 60%
workplace Social Sciences 53%
gender Social Sciences 36%
trend Social Sciences 35%
Sexism Medicine & Life Sciences 33%

T1 - Gender stereotypes and workplace bias

AU - Heilman, Madeline E.

N2 - This paper focuses on the workplace consequences of both descriptive gender stereotypes (designating what women and men are like) and prescriptive gender stereotypes (designating what women and men should be like), and their implications for women's career progress. Its central argument is that gender stereotypes give rise to biased judgments and decisions, impeding women's advancement. The paper discusses how descriptive gender stereotypes promote gender bias because of the negative performance expectations that result from the perception that there is a poor fit between what women are like and the attributes believed necessary for successful performance in male gender-typed positions and roles. It also discusses how prescriptive gender stereotypes promote gender bias by creating normative standards for behavior that induce disapproval and social penalties when they are directly violated or when violation is inferred because a woman is successful. Research is presented that tests these ideas, considers specific career consequences likely to result from stereotype-based bias, and identifies conditions that exaggerate or minimize the likelihood of their occurrence.

AB - This paper focuses on the workplace consequences of both descriptive gender stereotypes (designating what women and men are like) and prescriptive gender stereotypes (designating what women and men should be like), and their implications for women's career progress. Its central argument is that gender stereotypes give rise to biased judgments and decisions, impeding women's advancement. The paper discusses how descriptive gender stereotypes promote gender bias because of the negative performance expectations that result from the perception that there is a poor fit between what women are like and the attributes believed necessary for successful performance in male gender-typed positions and roles. It also discusses how prescriptive gender stereotypes promote gender bias by creating normative standards for behavior that induce disapproval and social penalties when they are directly violated or when violation is inferred because a woman is successful. Research is presented that tests these ideas, considers specific career consequences likely to result from stereotype-based bias, and identifies conditions that exaggerate or minimize the likelihood of their occurrence.

UR - http://www.scopus.com/inward/record.url?scp=84880038775&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84880038775&partnerID=8YFLogxK

U2 - 10.1016/j.riob.2012.11.003

DO - 10.1016/j.riob.2012.11.003

M3 - Review article

AN - SCOPUS:84880038775

SN - 0191-3085

JO - Research in Organizational Behavior

JF - Research in Organizational Behavior

Home — Essay Samples — Life — Women in The Workforce — Gender Stereotypes and Women in the Workplace

Gender Stereotypes in The Workplace: a Research

Categories: Gender Stereotypes Women in The Workforce

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Words: 4158 |

21 min read

Published: Oct 2, 2020

Words: 4158 | Pages: 9 | 21 min read

Introduction, gender roles in the workplace (essay), pressure and sexual harassment, works cited, perception of women, humanities and arts vs stem, gap in workplace, contribution of media, roles in movies, language used, sexual harassment, counter argument.

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Gartzke, K. A. (2021). Gender stereotypes and work: The impact of gender stereotypes on women in the workplace. Journal of Business and Psychology, 36(1), 87-100.
Hays, S., & Erford, B. T. (2014). Developing multicultural counseling competence: A systems approach (3rd ed.). Pearson.
Heilman, M. E., & Okimoto, T. G. (2007). Why are women penalized for success at male tasks?: The implied communality deficit. Journal of Applied Psychology, 92(1), 81-92.
Koeske, G. F., & Koeske, R. D. (1993). A preliminary investigation of the impact of gender-role stereotyping on counseling process and outcome. Journal of Counseling Psychology, 40(1), 69-74.
López‐Zafra, E., García‐Rodríguez, O., & García‐Izquierdo, M. (2019). The impact of gender stereotypes on job opportunities for women in the Spanish labor market. International Journal of Psychology, 54(6), 775-784.
Niederle, M., & Vesterlund, L. (2010). Explaining the gender gap in math test scores: The role of competition. Journal of Economic Perspectives, 24(2), 129-144.
Ridgeway, C. L. (2001). Gender, status, and leadership. Journal of Social Issues, 57(4), 637-655.
Rudman, L. A., & Phelan, J. E. (2010). The effect of priming gender roles on women's implicit gender beliefs and career aspirations. Social Psychology, 41(3), 192-202.
Wharton, A. S., & Erickson, R. J. (1993). Managing emotions on the job and at home: Understanding the consequences of multiple emotional roles. Academy of Management Review, 18(3), 457-486.

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The Layers of Sexism: Understanding its Complexity and Impact

This essay about the intricate layers of sexism, highlighting its pervasive nature and impact on individuals across gender spectrums. It explores how sexism manifests through stereotypes, discrimination, and power imbalances, affecting various aspects of society. The essay emphasizes the importance of recognizing intersectionality and challenging ingrained biases to foster a more equitable future. Through education, advocacy, and inclusive practices, society can work towards dismantling systemic barriers and promoting gender equality for all individuals.

How it works

Sexism, a multifaceted social phenomenon, permeates various facets of human interaction, often unnoticed or downplayed. At its core, sexism entails prejudice, discrimination, or stereotyping based on one’s gender. While commonly associated with the oppression of women, sexism affects individuals across the gender spectrum, albeit in differing ways. Understanding its nuances is paramount to fostering a more equitable society.

Central to sexism is the perpetuation of gender stereotypes, manifesting in beliefs about the inherent capabilities, roles, and characteristics of individuals based on their gender.

This pervasive stereotyping not only influences societal perceptions but also seeps into institutional structures, shaping policies, and practices. For instance, the persistent underrepresentation of women in leadership positions in corporate settings reflects ingrained biases that hinder their advancement.

Moreover, sexism operates on both explicit and implicit levels, with overt acts of discrimination coexisting alongside subtler forms of bias. While blatant instances, such as unequal pay or harassment in the workplace, are more readily identifiable, implicit biases subtly influence decision-making processes, perpetuating systemic inequalities. These biases often go unnoticed, underscoring the need for introspection and education to combat ingrained prejudices.

Intersectionality further complicates the landscape of sexism, highlighting how individuals experience discrimination based on the intersection of their gender identity with other social categories such as race, class, sexuality, and ability. Women of color, for example, may face compounded forms of discrimination, experiencing racism and sexism simultaneously. Recognizing these intersecting identities is crucial for addressing the diverse experiences of marginalization within the broader framework of sexism.

Moreover, the perpetuation of gender norms and expectations perpetuates harmful dynamics that restrict individuals’ autonomy and agency. From childhood, societal norms dictate rigid gender roles, prescribing certain behaviors and interests based on one’s assigned gender at birth. This socialization process not only limits personal expression but also reinforces harmful power dynamics, perpetuating inequalities between genders.

Addressing sexism necessitates a multifaceted approach encompassing societal, institutional, and individual levels of intervention. Education plays a pivotal role in challenging stereotypes, fostering empathy, and promoting gender equality from an early age. Additionally, advocating for inclusive policies and practices within institutions can help dismantle systemic barriers to equality, ensuring fair treatment and opportunities for all individuals.

In conclusion, sexism is a complex social phenomenon rooted in gender stereotypes, discrimination, and power imbalances. Recognizing its multifaceted nature is essential for devising effective strategies to combat it. By challenging ingrained biases, fostering inclusivity, and promoting gender equality, society can work towards creating a more equitable future for all.

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What Are Gender Stereotypes?

How They Develop
How to Combat

Gender stereotypes are preconceived, usually generalized views about how members of a certain gender do or should behave, or which traits they do or should have. They are meant to reinforce gender norms, typically in a binary way ( masculine vs. feminine ).

Gender stereotypes have far-reaching effects on all genders.

Read on to learn about how gender stereotypes develop, the effects of gender stereotypes, and how harmful gender stereotypes can be changed.

Davin G Photography / Getty Images

Meaning of Gender Stereotypes

Gender stereotypes are ideas about how members of a certain gender do or should be or behave. They reflect ingrained biases based on the social norms of that society. Typically, they are considered as binary (male/female and feminine/masculine).

By nature, gender stereotypes are oversimplified and generalized. They are not accurate and often persist even when there is demonstrable evidence that contradict them. They also tend to ignore the fluidity of gender and nonbinary gender identities.

Classification of Gender Stereotypes

Gender stereotypes have two components, which are:

Descriptive : Beliefs about how people of a certain gender do act, and their attributes
Prescriptive : Beliefs about how people of a specific gender should act and attributes they should have

Gender stereotypes can be positive or negative. This doesn’t mean good or bad—even stereotypes that seem “flattering” can have harmful consequences.

Positive gender stereotypes : Describe behaviors or attributes that align with accepted stereotypical ideas for that gender, and that people of that gender are encouraged to display (for example, girls should play with dolls and boys should play with trucks)
Negative gender stereotypes : Describe behaviors or attributes that are stereotypically undesirable for that gender and that people from that gender are discouraged from displaying (such as women shouldn’t be assertive, or men shouldn’t cry)

The attribute is undesirable for all genders but more accepted in a particular gender than others. For example, arrogance and aggression are unpleasant in all genders but are tolerated more in men and boys than in women, girls, or nonbinary people .

Gender stereotypes tend to be divided into these two generalized themes:

Communion : This stereotype orients people to others. It includes traits such as compassionate, nurturing, warm, and expressive, which are stereotypically associated with girls/women/femininity.
Agency : This stereotype orients people to the self and is motivated by goal attainment. It includes traits such as competitiveness, ambition, and assertiveness, which are stereotypically associated with boys/men/masculinity.

Basic types of gender stereotypes include:

Personality traits : Such as expecting women to be nurturing and men to be ambitious
Domestic behaviors : Such as expecting women to be responsible for cooking, cleaning, and childcare, while expecting men to do home repairs, pay bills, and fix the car
Occupations : Associates some occupations such as childcare providers and nurses with women and pilots and engineers with men
Physical appearance : Associates separate characteristics for women and men, such as women should shave their legs or men shouldn’t wear dresses

Gender stereotypes don’t exist in a vacuum. They can intersect with stereotypes and prejudices surrounding a person’s other identities and be disproportionately harmful to different people. For example, a Black woman experiences sexism and racism , and also experiences unique prejudice from the intersectionality of sexism and racism that a White woman or Black man would not.

Words to Know

Gender : Gender is a complex system involving roles, identities, expressions, and qualities that have been given meaning by a society. Gender is a social construct separate from sex assigned at birth.
Gender norms : Gender norms are what a society expects from certain genders.
Gender roles : These are behaviors, actions, social roles, and responsibilities a society views as appropriate or inappropriate for certain genders.
Gender stereotyping : This ascribes the stereotypes of a gender group to an individual from that group.
Self-stereotyping vs. group stereotyping : This is how a person views themselves compared to how they view the gender group they belong to (for example, a woman may hold the belief that women are better caregivers than men, but not see herself as adept in a caregiving role).

How Gender Stereotypes Develop

We all have unconscious biases (assumptions our subconscious makes about people based on groups that person belongs to and our ingrained associations with those groups). Often, we aren’t even aware we have them or how they influence our behavior.

Gender stereotyping comes from unconscious biases we have about gender groups.

We aren’t preprogrammed at birth with these biases and stereotypes. Instead, they are learned through repeated and ongoing messages we receive.

Gender roles, norms, and expectations are learned by watching others in our society, including our families, our teachers and classmates, and the media. These roles and the stereotypes attached to them are reinforced through interactions starting from birth. Consciously or not, adults and often other children will reward behavior or attributes that are in line with expectations for a child’s gender, and discourage behavior and attributes that are not.

Some ways gender stereotypes are learned and reinforced in childhood include:

How adults dress children
Toys and play activities offered to children
Children observing genders in different roles (for example, a child may see that all of the teachers at their daycare are female)
Praise and criticism children receive for behaviors
Encouragement to gravitate toward certain subjects in school (such as math for boys and language arts for girls)
Anything that models and rewards accepted gender norms

Children begin to internalize these stereotypes quite early. Research has shown that as early as elementary school, children reflect similar prescriptive gender stereotypes as adults, especially about physical appearance and behavior.

While all genders face expectations to align with the stereotypes of their gender groups, boys and men tend to face harsher criticism for behavior and attributes that are counterstereotypical than do girls and women. For example, a boy who plays with a doll and wears a princess dress is more likely to be met with a negative reaction than a girl who wears overalls and plays with trucks.

The Hegemonic Myth

The hegemonic myth is the false perception that men are the dominant gender (strong and independent) while women are weaker and need to be protected.

Gender stereotypes propagate this myth.

Effects of Gender Stereotypes

Gender stereotypes negatively impact all genders in a number of ways.

Nonbinary Genders

For people who are transgender / gender nonconforming (TGNC), gender stereotypes can lead to:

Feelings of confusion and discomfort
A low view of self-worth and self-respect
Transphobia (negative feelings, actions, and attitudes toward transgender people or the idea of being transgender, which can be internalized)
Negative impacts on mental health
Struggles at school

Unconscious bias plays a part in reinforcing gender stereotypes in the classroom. For example:

Educators may be more likely to praise girls for being well-behaved, while praising boys for their ideas and comprehension.
Boys are more likely to be viewed as being highly intelligent, which influences choices. One study found girls as young as 6 avoiding activities that were labeled as being for children who are “really, really smart.”
Intentional or unintentional steering of children toward certain subjects influences education and future employment.

In the Workforce

While women are in the workforce in large numbers, gender stereotypes are still at play, such as:

Certain occupations are stereotypically gendered (such as nursing and teaching for women and construction and engineering for men).
Occupations with more female workers are often lower paid and have fewer opportunities for promotion than ones oriented towards men.
More women are entering male-dominated occupations, but gender segregation often persists within these spaces with the creation of female-dominated subsets (for example, pediatrics and gynecology in medicine, or human resources and public relations in management).
Because men face harsher criticism for displaying stereotypically feminine characteristics than women do for displaying stereotypically male characteristics, they may be discouraged from entering female-dominated professions such as early childhood education.

Despite both men and women being in the workforce, women continue to be expected to (and do) perform a disproportionate amount of housework and taking care of children than do men.

Gender-Based Violence

Gender stereotypes can contribute to gender-based violence.

Men who hold more traditional gender role beliefs are more likely to commit violent acts.
Men who feel stressed about their ability to meet male gender norms are more likely to commit inter-partner violence .
Trans people are more likely than their cisgender counterparts to experience discrimination and harassment, and they are twice as likely to engage in suicidal thoughts and actions than cisgender members of the Queer community.

Stereotypes and different ways of socializing genders can affect health in the following ways:

Adolescent boys are more likely than adolescent girls to engage in violent or risky behavior.
Mental health issues are more common in girls than boys.
The perceived “ideal” of feminine slenderness and masculine muscularity can lead to health issues surrounding body image .
Gender stereotypes can discourage people from seeking medical help or lead to missed diagnosis (such as eating disorders in males ).

Globally, over 575 million girls live in countries where inequitable gender norms contribute to a violation of their rights in areas such as:

Employment opportunities
Independence
Safety from gender-based violence

How to Combat Gender Stereotypes

Some ways to combat gender stereotypes include:

Examine and confront your own gender biases and how they influence your behavior, including the decisions you make for your children.
Foster more involvement from men in childcare, both professionally and personally.
Promote and support counterstereotypical hirings (such as science and technology job fairs aimed at women and campaigns to gain interest in becoming elementary educators for men).
Confront and address bias in the classroom, including education for teachers on how to minimize gender stereotypes.
Learn about each child individually, including their preferences.
Allow children to use their chosen name and pronouns .
Avoid using gender as a way to group children.
Be mindful of language (for example, when addressing a group, use “children” instead of “boys and girls” and “families” instead of “moms and dads,”).
Include books, toys, and other media in the classroom and at home that represent diversity in gender and gender roles.
View toys as gender neutral, and avoid ones that promote stereotypes (for example, a toy that has a pink version aimed at girls).
Ensure all children play with toys and games that develop a full set of social and cognitive skills.
Promote gender neutrality in sports.
Be mindful of advertising and the messaging marketing sends to children.
Talk to children about gender, including countering binary thinking and gender stereotypes you come across.
Take a look at the media your child engages with. Provide media that show all genders in a diversity of roles, different family structures, etc. Discuss any gender stereotyping you see.
Tell children that it is OK to be themselves, whether that aligns with traditional gender norms or not (for example, it’s OK if a woman wants to be a stay-at-home parent, but it’s not OK to expect her to).
Give children equal household chores regardless of gender.
Teach all children how to productively handle their frustration and anger.
Encourage children to step out of their comfort zone to meet new people and try activities they aren’t automatically drawn to.
Put gender-neutral bathrooms in schools, workplaces, and businesses.
Avoid assumptions about a person’s gender, including children.
Take children to meet people who occupy counterstereotypical roles, such as a female firefighter.
Speak up and challenge someone who is making sexist jokes or comments.

Movies That Challenge Gender Stereotypes

Not sure where to start? Common Sense Media has compiled a list of movies that defy gender stereotypes .

Gender stereotypes are generalized, preconceived, and usually binary ideas about behaviors and traits specific genders should or should not display. They are based on gender norms and gender roles, and stem from unconscious bias.

Gender stereotypes begin to develop very early in life through socialization. They are formed and strengthened through observations, experiences, and interactions with others.

Gender stereotypes can be harmful to all genders and should be challenged. The best way to start combating gender stereotypes is to examine and confront your own biases and how they affect your behavior.

A Word From Verywell

We all have gender biases, whether we realize it or not. That doesn’t mean we should let gender stereotypes go unchecked. If you see harmful gender stereotyping, point it out.

YWCA Metro Vancouver. Dating safe: how gender stereotypes can impact our relationships .

LGBTQ+ Primary Hub. Gender stereotyping .

Stanford University: Gendered Innovations. Stereotypes .

Koenig AM. Comparing prescriptive and descriptive gender stereotypes about children, adults, and the elderly . Front Psychol . 2018;9:1086. doi:10.3389/fpsyg.2018.01086

United Nations Office of the High Commissioner for Human Rights. Gender stereotypes .

Hentschel T, Heilman ME, Peus CV. The multiple dimensions of gender stereotypes: a current look at men’s and women’s characterizations of others and themselves . Front Psychol . 2019;10:11. doi:10.3389/fpsyg.2019.00011

Eagly AH, Nater C, Miller DI, Kaufmann M, Sczesny S. Gender stereotypes have changed: a cross-temporal meta-analysis of U.S. public opinion polls from 1946 to 2018 . Am Psychol . 2020;75(3):301-315. doi:10.1037/amp0000494

Planned Parenthood. What are gender roles and stereotypes?

Institute of Physics. Gender stereotypes and their effect on young people .

France Stratégie. Report – Gender stereotypes and how to fight them: new ideas from France .

Bian L, Leslie SJ, Cimpian A. Gender stereotypes about intellectual ability emerge early and influence children’s interests . Science . 2017;355(6323):389-391. doi:10.1126/science.aah6524

Save the Children. Gender roles can create lifelong cycle of inequality .

Girl Scouts. 6 everyday ways to bust gender stereotypes .

UNICEF. How to remove gender stereotypes from playtime .

Save the Children. Tips for talking with children about gender stereoptypes .

By Heather Jones Jones is a freelance writer with a strong focus on health, parenting, disability, and feminism.

The Gender Stereotypes in the Workplace

Martin and Barnard (2013) employ the grounded theory approach to explore females’ experiences in male-dominated professions. The use of the approach is evident as the researchers used unstructured interviews to collect data. The data were transcribed, and later initial codes were developed. After that, the axial coding was implemented. The researchers identified recurrent themes in each interview and compared the interviews as well as field notes and memos. The theory was developed on the basis of these themes.

Martin and Barnard (2013) focused on discriminatory practices in male-dominated professions and coping strategies used by females to overcome such issues. The data collection procedure involved unstructured interviews with five females working in a male-dominated environment. The researchers played an active role in the process as they carried out the interviews and transcribed the data.

In terms of the grounded theory approach, the researchers try to identify themes that recurrently appear in the participants’ accounts. These themes are used to develop a theory. In this study, the researchers manage to develop a sound theory concerning major obstacles females meet in a male-dominated working environment and the coping strategies they used. They note that gender stereotypes and discrimination are major challenges, and the utilization of femininity, adoption of some male characteristics, intrinsic motivation as well as mentorship are central coping strategies.

The researchers aimed at identifying central challenges females face and coping strategies they use to address the issues. The grounded theory approach is appropriate to meet the researcher’s goals. One of the major merits of the approach is the ability to identify recurrent themes, which are the answer to the researchers’ questions. It is possible to enhance the study with the help of the mixed method approach. It could be effective to test the theory by implementing a survey among females employed at several companies.

The gender stereotypes in the workplace were the focus of the discussion. Qualitative and quantitative studies exploring issues related to gender stereotypes in the working environment were analyzed. Some studies were based on the experimental while others were based on quasi-experimental approaches. Thus, qualitative studies identified particular challenges women face, discriminatory practices and stereotypes that exist, and so on. Quantitative studies revealed the extent to which these trends affect people’s lives as well as the way different trends and opinions correlate.

The qualitative methodology allowed researchers to identify some persistent trends and opinions while quantitative methods provided quantifiable evidence to support some theories. The strength of the qualitative research method is its depth and focus. However, its major limitation is the inability to generalize the findings to wide populations. The strength of the quantitative method is that it provides particular numerical data to support or refute a hypothesis.

These data are generalizable which is another advantage. Nonetheless, the quantitative method can often provide too general data that cannot be applicable in all situations. For instance, such instrument as tests or surveys limit participants answers to particular options, and their ideas may remain concealed.

It is difficult to identify the most appropriate approach as each of them is beneficial in its own way. When it comes to gender issues, it is almost impossible to state that only one of the methods can be effective. Both approaches are applicable and can reveal different facets of the problem. Researchers should employ both methods to address issues related to gender stereotyping in the workplace and at home. It is vital to identify people’s ideas on the matter and reveal various trends existing in the society. It is also essential to identify the extent to which these trends affect people’s behavior. Numerical data can provide the necessary evidence to support the data obtained from qualitative studies.

Ethics is important in three domains that include academic writing, participants’ treatment, and research conduct. Each of these aspects is crucial for psychology as well as any other field. It is possible to note that ethics ensures the validity of the research. Thus, academic writing should be ethical as such issues as plagiarism or violations of copyright policies decreases the validity of the study (Leedy & Ormrod, 2012). Unethical treatment of the participants can lead to distorted data as participants may be afraid to share their true opinions if their confidentiality is not ensured (Iphofen, 2016). It is also crucial to make sure that the research is conducted with a focus on such ethical values as integrity, openness, objectivity.

People involved in the sphere of psychology have to pay a lot of attention to ethical issues. Working with people may lead to various issues related to ethics. Thus, clients often face various challenges and the development of rapport is crucial as the practitioner may help a client who trusts him/her. Confidentiality is one of the most burning issues related to this area as people are reluctant to share if they suspect that their secrets can be revealed.

At that, even the development of family relations depends on the ability to make ethical decisions. Again, unethical behavior destroys the trust that is essential. Thus, ethical behavior is of a paramount importance of any individual.

It is possible to note that the three ethical contexts mentioned above contribute significantly to scientific merit. One of the major ways this contribution is manifested is the ensured validity of the research. Ethical conduct in all the three domains helps the researcher to collect relevant data and come to valid conclusions that are likely to be proved during further studies.

Reference List

Iphofen, R. (2016). Ethical decision making in social research: A practical guide . New York, NY: Springer.

Leedy, P.D., & Ormrod, J.E. (2012). Practical research: Planning and Design . Upper Saddle River, NJ: Pearson Education.

Martin, P., & Barnard, A. (2013). The experience of women in male-dominated occupations: A constructivist grounded theory inquiry . SA Journal of Industrial Psychology/SA Tydskrif vir Bedryfsielkunde , 39 (2), 1-12. Web.

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Progress for women in the workplace stagnating in four key areas, global study reveals

Around 60% of women do not feel able to switch off from their work. Image: Getty Images

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A new report looking at women's experiences in the workplace across ten countries suggests there is cause for concern.
Mental health, unbalanced domestic responsibilities and non-inclusive behaviours are current red flags.
More organizations must graduate to becoming 'Gender Equality Leaders'.

Despite much talk about the importance of gender equality in the workplace, many women are facing mounting pressures at work and in their personal lives, according to Deloitte’s Women @ Work 2024: A Global Outlook annual report , the fourth in the series.

The report gathers insights from 5,000 women in 10 countries about their views and experiences in the workplace and examines the societal factors that may be impacting their careers.

Across the countries surveyed (Australia, Brazil, Canada, China, Germany, India, Japan, South Africa, the United Kingdom and the United States), a clear trend emerges: Despite widespread cultural and contextual differences, many women around the world are experiencing similar challenges in and out of the workplace. At best, workplace progress when it comes to gender equality appears to be stagnating.

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1. stress and long working hours take a toll on mental health.

Half of women say their stress levels are higher than they were a year ago, and a similar number say they’re concerned or very concerned about their mental health. Mental health is among the top concerns for women globally, with an average of 48% of women citing this as their top concern, falling only behind their financial security (51%) and women’s rights (50%). Around half of women do not believe that their employer provides adequate support for their mental health at work.

Amid a number of potential factors behind this concerning picture on mental health is an inability to disconnect from work. Around 60% of women do not feel able to switch off from their work, a trend that holds true across countries. While half of women who typically just work their contracted hours describe their mental health as good, this declines to just 23% for those who regularly work extra hours.

2. Household responsibilities affect women’s careers

Women are feeling the weight of misbalanced caregiving and domestic responsibilities. Notably, 50% of women globally who live with a partner and have children say they take the most responsibility for childcare – up from 46% in 2023, with only 12% saying this falls to their partner. This year also saw a concerning increase in women taking the greatest responsibility for caring for another adult: 57% said they are primarily responsible for this, while only 6% say this falls to their partner. This imbalance continues even for those women who are the primary household earners.

The result of this disproportionate allocation of responsibilities not only makes it more challenging for women professionally but also potentially creates a vicious cycle reinforcing the gender pay gap. Only 27% of women who bear the most significant responsibility at home say they can disconnect from their personal lives and focus on their careers. And many women are prioritizing their partners’ careers over their own, mainly because their partner earns more.

Meanwhile, fewer than half of women feel supported by their employers in balancing their work responsibilities with commitments outside work. Nearly all women (95%) believe that requesting or taking advantage of flexible work opportunities will negatively affect their chances of promotion. Notably, a lack of flexible working hours is among the top reasons women have changed jobs over the past year.

3. Many women experience threats and non-inclusive behaviours at work

Nearly half of the women say they are worried about their personal safety at work or when commuting or travelling for work. Once again, while there are varying degrees of concern among respondents in the countries surveyed, the trend is largely consistent across markets.

These concerns may be founded on actual incidents: One in 10 women who are concerned about their personal safety say they have been harassed while commuting or travelling for work; 16% deal with customers or clients who have harassed them or behaved in a way that has made them feel uncomfortable. Nearly one in 10 have been harassed by a colleague, and a quarter of women say that people in senior positions have made inappropriate comments or actions towards them.

More than four in 10 (43%) survey respondents report having experienced either micro-aggressions or harassment (or both) at work in the past 12 months. Despite this, only one in 10 women think they can make a complaint to their employer about non-inclusive behaviours without affecting their career.

4. More 'Gender Equality Leaders' are needed

As with previous years, our research found a small number of women who work for organizations that enable inclusion, support work/life balance and focus on meaningful career development – we refer to these organizations as the Gender Equality Leaders.

Women who work for these organizations report higher levels of loyalty toward their employer and productivity, feel safer, are more comfortable talking about their mental health at work, and can work flexibly without damaging their careers. However, Gender Equality Leaders remain few and far between: Only 6% of women across all countries surveyed work for these organizations—only a one percentage point increase over last year.

The COVID-19 pandemic and recent social and political unrest have created a profound sense of urgency for companies to actively work to tackle inequity.

The Forum's work on Diversity, Equality, Inclusion and Social Justice is driven by the New Economy and Society Platform, which is focused on building prosperous, inclusive and just economies and societies. In addition to its work on economic growth, revival and transformation, work, wages and job creation, and education, skills and learning, the Platform takes an integrated and holistic approach to diversity, equity, inclusion and social justice, and aims to tackle exclusion, bias and discrimination related to race, gender, ability, sexual orientation and all other forms of human diversity.

The Platform produces data, standards and insights, such as the Global Gender Gap Report and the Diversity, Equity and Inclusion 4.0 Toolkit , and drives or supports action initiatives, such as Partnering for Racial Justice in Business , The Valuable 500 – Closing the Disability Inclusion Gap , Hardwiring Gender Parity in the Future of Work , Closing the Gender Gap Country Accelerators , the Partnership for Global LGBTI Equality , the Community of Chief Diversity and Inclusion Officers and the Global Future Council on Equity and Social Justice .

The data from this year’s survey provides insight into the challenges that women face both within and outside the workplace—and it provides data-driven insight into solutions. These can include recognizing the importance of normalizing conversations around mental health in the workplace, understanding and addressing the causes of workplace stress, embedding family-friendly policies and enabling work/life balance, understanding and addressing women’s workplace safety concerns, or addressing non-inclusive behaviours and enabling women to feel able to speak up without concern.

It is clear that now is the time to act if we are to see meaningful and sustained change.

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The views expressed in this article are those of the author alone and not the World Economic Forum.

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Gender equity in hiring: examining the effectiveness of a personality-based algorithm

Emeric kubiak.

1 AssessFirst, Paris, France

Maria I. Efremova

2 King’s College London, Institute of Psychiatry, Psychology and Neuroscience, University of London, London, United Kingdom

Simon Baron

Keely j. frasca.

3 Birkbeck Business School, Faculty of Business and Law, Birkbeck, University of London, London, United Kingdom

Associated Data

The datasets presented in this article are not readily available because even if the data used has been anonymized by AssessFirst, the participants may choose to delete their data at any time: while the data were accessible for scientific purposes during the analysis and publication, it cannot be ensured that the same data will remain legally available in the future. For legal reasons supporting data is, therefore, not available. Requests to access the datasets should be directed to EK, moc.tsrifssessa@kaibuke .

Introduction

Gender biases in hiring decisions remain an issue in the workplace. Also, current gender balancing techniques are scientifically poorly supported and lead to undesirable results, sometimes even contributing to activating stereotypes. While hiring algorithms could bring a solution, they are still often regarded as tools amplifying human prejudices. In this sense, talent specialists tend to prefer recommendations from experts, while candidates question the fairness of such tools, in particular, due to a lack of information and control over the standardized assessment. However, there is evidence that building algorithms based on data that is gender-blind, like personality - which has been shown to be mostly similar between genders, and is also predictive of performance, could help in reducing gender biases in hiring. The goal of this study was, therefore, to test the adverse impact of a personality-based algorithm across a large array of occupations.

The study analyzed 208 predictive models designed for 18 employers. These models were tested on a global sample of 273,293 potential candidates for each respective role.

Mean weighted impact ratios of 0.91 (Female-Male) and 0.90 (Male-Female) were observed. We found similar results when analyzing impact ratios for 21 different job categories.

Our results suggest that personality-based algorithms could help organizations screen candidates in the early stages of the selection process while mitigating the risks of gender discrimination.

1. Introduction

Research dating back as far as the 1970s (see Davison and Burke, 2000 for a review, which covers multiple countries) has shown that gender discrimination in hiring occurs, and continues to be a prevalent issue in today’s hiring practices–despite the findings from cross-temporal meta-analysis indicating that belief in competence equality has grown over time ( Eagly et al., 2020 ). Yet, a recent meta-analysis of hiring discrimination experiments conducted between 2005 and 2020 ( Lippens et al., 2023 ) reveals that gender discrimination is highly complex and varied, with instances of both males and females facing discrimination in certain cases. The relative advantages of male and female candidates hinge on demand-side factors. These may include the impact of certain job characteristics that are traditionally associated with one gender over the other on selection criteria, as well as how closely a candidate aligns with the typical characteristics of their gender category. Further substantiating these findings, a comprehensive meta-re-analysis of over 70 employment audit experiments conducted across more than 26 countries and five continents concluded that in male-dominated professions, which are typically higher-paying, being female can be a disadvantage. Conversely, in female-dominated professions, which tend to be lower-paying, being female is viewed positively ( Galos and Coppock, 2023 ), thus confirming gender-role congruity bias. Besides, this bias consistently manifests in hiring decisions. For instance, Koch et al. (2015) meta-analysis concluded that it is more pronounced among male raters, and it does not diminish even when raters are provided with additional information about the candidate. However, despite both males and females facing discrimination based on occupation characteristics, the price paid by females is often higher than that of their male counterparts, as they often have limited access to higher-paying jobs and roles with greater responsibilities. In addition, in line with the backlash effect, which refers to a social and psychological phenomenon where individuals are penalized for violating societal norms or expectations regarding gender ( Williams and Tiedens, 2016 ), females are in a double-bind. As concluded by Castaño et al. (2019) in a systematic review, “if women adopt masculine roles they are perceived as cold and instrumental, whereas if women adopt feminine roles they are perceived as less competent” (p. 0.14) –an effect that men do not typically experience. As a consequence, in highly prestigious occupations, even if females perform equally, they are rewarded significantly lower ( Joshi et al., 2015 ). It results that the representation of females progressively declines higher up the hierarchy. Data from LinkedIn’s Economic Graph indicates that obstacles for females begin to appear as early as at the managerial level. Globally, only 25% of female ascend to the C-Suite level, even though the ratio of male to female is nearly equal at the individual contributor level ( LinkedIn, 2022 ).

Addressing gender bias in hiring is not only a matter of ethical responsibility, but it is also crucial due to the harmful consequences such biases can engender. For example, a meta-analysis by Triana et al. (2019) found that perceived gender discrimination is negatively related to job attitudes, physical health outcomes and behaviors, psychological health, and work-related outcomes. Interestingly, even minimal biases can lead to substantial instances of hiring discrimination and losses in productivity, underscoring the significant practical impact of these biases. In a series of simulations, Hardy et al. (2022) established that a slight bias of 2.2% led to disparate treatment rates that were 13.5% higher than those observed in a bias-free model. Furthermore, the chances of a woman receiving a favorable hiring decision were almost halved, being 49% lower than the odds for their male counterparts with similar qualifications. The financial repercussions of this were significant, with bias accounting for 16.1% of new hire failure rates, ultimately leading to a utility loss per hiring due to bias amounting to -$710.54 per hire. The negative impact started to manifest with as little as 1% bias, which resulted in 8.7% disparate treatment and a utility loss of -$355.36. This effect escalated under the simulation of a higher 4% bias, where it led to 20.3% disparate treatment and a staggering utility loss of -$2,125.64. Furthermore, the authors found that contextual factors alter, but cannot obviate the consequences of biased evaluations. Consequently, it’s crucial to identify strategies that reduce bias in hiring decisions.

2. Intervention for mitigating gender bias

Efforts have been made to implement interventions for reducing gender discrimination, but yielded mixed outcomes. According to a recent systematic review, half of the intervention measuring social change in gender equality did not achieve beneficial results ( Guthridge et al., 2022 ), leading the authors to conclude that “in the past 30 years we have not uncovered the keys to social change in order to enhance gender equality and non-discrimination against girls and women” (p. 0.335). In the specific context of recruitment, studies have consistently underlined the pervasive nature of unconscious stereotypes and the ease with which these biases can be triggered. For instance, Isaac et al. (2009) , in a comprehensive review spanning over 30 years of research, found that conventional interventions, such as diversity training and employment equity programs, fail to guarantee gender equity in hiring despite their widespread use, and can even prove to be counterproductive. Similarly, counter-stereotype training appears to be effective only under specific conditions. Nevertheless, their review pinpointed several institutional interventions that could be promising to foster gender equity in hiring. They also highlighted actions that female applicants themselves could undertake. While their research suggests viable interventions to promote gender equity in hiring, it also underscores the issue’s complexity. Some recommendations appear as desperate “Hail Mary” attempts to combat gender bias. For instance, the advice, “If you are visibly pregnant, it might be wise to obscure it with your clothing” (p. 0.6), while effective, exposes the depth of societal bias we are grappling with. Also, it is important to recognize that such advice could be considered as pernicious, as it perpetuates and reinforces societal bias, rather than addressing the root causes of gender inequity in hiring practices.

Traditional interventions include diversity or counter-stereotype training ( Bezrukova et al., 2012 ), the introduction of gender quotas ( Krook and Zetterberg, 2014 ), lean-in approach ( Chrobot-Mason et al., 2019 ), and equity guidelines. Yet, according to the International Labor Organization, even though 75 percent of companies worldwide have embraced policies of equal opportunity, diversity, and inclusion, gender biases stubbornly linger in selection ( International Labour Organization [ILO], 2019 ). Indeed, despite good intentions, such interventions can have unintended consequences and potentially generate new issues. Caleo and Heilman (2019) synthesized the potential ways in which these interventions could backfire, including (1) promoting gender stereotyping, (2) reducing personal responsibility for bias, (3) fueling the perception of undeserved preference, (4) prompting negative trickle-down effects, (5) creating tokens, (6) encouraging discriminatory behavior, (7) depleting cognitive resources, and (8) doing harm to those who lead bias-reducing initiatives. Their analysis highlights the complex nature of gender bias in hiring, and the importance of carefully considering the unintended consequences of interventions. To this end, we’ll briefly explore the impact of conventional interventions in the next paragraphs.

Diversity training has been shown to be effective at reducing the extent to which assessors assign stereotypic labels to male candidates (e.g., determined and competitive) and female candidates (e.g., submissive and helpful) ( Kawakami et al., 2007 ). However, this effect did not emerge if assessors engaged in the stereotyping task immediately after their counter-stereotype training. Despite the potential impact of training interventions, their effectiveness tends to be short-lived, with discrimination resurfacing as early as 3 months after the interventions ( Derous et al., 2020 ). This finding emphasizes the limited long-term sustainability of such interventions in combating bias. Also, other research showed that training claiming to limit unconscious bias is ineffective, and ironically contributes to activating stereotypes ( Madera and Hebl, 2013 ). For instance, Dobbin et al. (2007) conducted a study revealing that despite the adoption of sensitivity and diversity training by big corporations, there was no significant increase in gender diversity. This finding raises questions about the effectiveness of such training programs in achieving meaningful change. As argued by Noon (2018) , unless the everyday discriminatory acts are effectively addressed, the adoption of such unconscious bias training in the workplace and in hiring may have limited utility to mitigate biases.

Gender quotas have been implemented as a means to address gender inequality, but research suggests that their effectiveness is not without drawbacks. While they aim to promote gender diversity, there are concerns that quotas may inadvertently reinforce stereotypes and fuel the perception that women are less competent. This is supported by findings from Leibbrandt et al. (2018) , who found evidence of a severe backlash against women under gender quotas, leading to sabotage and undermining their success. Furthermore, the impact of quotas on corporate boards has also been examined. Yu and Madison (2021) conducted research showing that quotas for women on corporate boards have primarily resulted in decreased company performance. This raises questions about the direct correlation between quotas and improved outcomes. In addition, the introduction of gender quotas may intensify the negative effects of second-generation bias and perpetuate gender inequality in the workplace, as suggested by recent work by Loumpourdi (2023) . This highlights the complex dynamics at play when implementing quotas and emphasizes the need for comprehensive approaches that address underlying biases and promote gender equality in a more nuanced and systemic manner.

Regarding equity guidelines, Ng and Wiesner (2007) found that implementing basic employment equity messages only had a positive impact when underrepresented group members were equally or more qualified than the majority group. However, when preferential treatment was given to less qualified candidates, men who were underrepresented in the profession tended to be favored over underrepresented women. Similarly, stronger employment directives typically led to detrimental outcomes whereby such perceived coercive employment equity messages resulted in men being favored over women. As a result, Castilla and Benard (2010) draws a paradoxical conclusion that in organizations that strongly advocate for meritocracy, decision-makers tend to exhibit a preference for men over equally qualified female employees. This finding highlights a discrepancy between the professed value of meritocracy and the actual biases that can influence decision-making processes.

Considering the evidence of gender biases in selection and assessment, in conjunction with interventions that are shown to be largely ineffective, there is a need to explore alternative ways of mitigating these issues. This is particularly important since gender diversity is positively related with higher employee wellbeing and positive job appraisal ( Clark et al., 2021 ), as well as with productivity in contexts where gender diversity is viewed as normatively accepted ( Zhang, 2020 ). To address these challenges, an important step is to enhance the structure of the evaluation and selection procedure. For example, Wolgast et al. (2017) showed that using tools for systematizing information about the applicants could help in mitigating biases and in selecting more competent applicants.

3. Algorithms as a solution against bias

One solution to alleviate biases could be the use of hiring algorithms, which allow us to go beyond our intuition and cognitive biases, by bringing standardization and structure to hiring decisions. An algorithm could be defined as a set of operations or tasks to be carried out following a certain logic, with the aim of answering a question or solving a problem ( Jean, 2019 ). In other words, an algorithm acts like a set of instructions, turning the information we feed into it into recommendations. The utilization of algorithms at different steps of the hiring pipeline is becoming increasingly prevalent in today’s workplace ( Tambe et al., 2019 ), and systematic review point out the potential for these algorithms to revolutionize HR management ( França et al., 2023 ). In the hiring process, algorithms learn from past data of old applicants to predict how suitable future applicants might be for a job. Basically, they figure out what attributes from past successful applicants led to good job performance, and then use this understanding to predict which future applicants might be the best fit for the job. The increasing adoption of algorithms is largely driven by their efficiency. For example, in a meta-analysis comparing mechanical and clinical data combination in selection, Kuncel et al. (2013) showed that, in predicting job performance, the difference in the validity between mechanical and clinical data combination methods resulted in an enhancement of prediction accuracy exceeding 50%. Other studies showed that algorithms make better hiring decisions in terms of the employee’s performance outcomes ( Sajjadiani et al., 2019 ; Li et al., 2020 ) and in hiring fill rate ( Horton, 2017 ). Taken together in a recent systematic literature review, these results regarding performance suggest that algorithmic hiring methods are equal or better than human when selecting the best candidates ( Will et al., 2022 ).

Recently, scholars have also been advocating for the use of such algorithms to reduce implicit biases in hiring processes and have proposed frameworks to evaluate AI-assisted interventions ( Lin et al., 2021 ). According to Leutner et al. (2022) , by using AI for hiring purposes, employers will be able to control for not only gender bias but other discriminatory characteristics as AI technology is able to be trained in a way to filter through the necessary characteristics required for candidates and to ignore other features. This is supported by several studies, showing that machine learning has no adverse impact on gender ( Sajjadiani et al., 2019 ) or that a fair ranking algorithm could increase the selection of female candidates ( Sühr et al., 2020 ). In regards to empirical evidence, Li et al. (2020) revealed that some algorithms could increase the share of women selected, up to a balance of 50%, compared to 35% for hiring decisions made by humans. Similarly, Avery et al. (2023) conducted a comparative analysis between human-evaluation and AI-evaluation treatments. The authors found that human evaluators consistently rated males higher than females by a substantial 0.15 standard deviations. This gender discrepancy was most noticeable at the higher end of the distribution, with men being 6.8 percentage points more likely to rank in the top 25%, and 7.73 percentage points more likely to land in the top 10%. In contrast, when AI was employed, the gender difference shrank considerably to just a 0.04 standardized difference. Furthermore, the representation of males and females in the top 50%, 25%, and 10% categories under the AI condition was nearly equal, showcasing the potential for AI to mitigate human biases in evaluation processes. Hiring algorithms could also benefit by increasing the perceived equity of the hiring process. For example, (1) women prefer to be judged by an algorithm because of its perceived objectivity over a human ( Pethig and Kroenung, 2023 ), (2) algorithms are perceived as less discriminatory than humans, which increases people’s comfort toward their usage ( Jago and Laurin, 2021 ), and (3) applicants with prior discrimination experiences deem algorithm-based decisions more positively than those without such experiences ( Koch-Bayram et al., 2023 ).

Despite these findings, many researchers have sounded the alarm. For instance, Drage and Mackereth (2022) , in their review of assertions made by AI providers, suggested that endeavors to eradicate gender and race from AI frequently misinterpret these concepts as discrete characteristics rather than broader structures of power, or that that using AI as a fix for gender diversity issues, an example of technosolutionism, fails to address the inherent systemic issues within organizations. Others raise concerns that algorithms could unintentionally exacerbate existing biases within recruitment processes ( Kelly-Lyth, 2021 ). Algorithmic bias takes on a discriminatory aspect when it results in consistent disparities linked to factors legally protected, such as gender. For instance, Dastin (2022) documented a case involving Amazon’s hiring algorithm, which persistently gave higher employability scores to men than to women, while Chen et al. (2018) , testing the adverse effects of candidates search engines, showed that female candidates were ranked statistically lower than male candidates. This circumstance has prompted scholars to delve into the exploration of algorithmic biases in hiring and strategies to mitigate them ( De Cremer and De Schutter, 2021 ). From a psychological perspective, research shows that, while individuals view algorithm-driven decisions as less prone to bias, they also generally regard it as less fair ( Feldkamp et al., 2023 ). Moreover, algorithmic decisions resulting in gender disparities are less likely to be perceived as biased compared to human decisions, because people tend to believe that algorithms make decisions devoid of context, thereby disregarding individual characteristics ( Bonezzi and Ostinelli, 2021 ). From a technical perspective, Rieskamp et al. (2023) identified four types of strategies aimed at reducing discrimination in these systems, namely pre-process, in-process, post-process, and feature selection. This review implies that interventions can be implemented at various stages of the algorithm development process to effectively mitigate bias. This is supported by van Giffen et al. (2022) , who listed different types of biases in algorithm and in machine learning, distinguishing, for example, biases related to the use of historical biased data ( Mehrabi et al., 2019 ), data which are not representative for the relevant population, or measurement biases. However, intervening to reduce subgroup differences in selection often presents a trade-off regarding accuracy. This situation represents what is known as the validity-diversity dilemma, which involves maintaining a balance between selecting valid performance predictors and minimizing adverse impact. While interventions aimed at reducing subgroup disparities could decrease model accuracy ( Zhang et al., 2023 ), strategies employing multi-penalty optimization are promising in addressing this issue ( Rottman et al., 2023 ).

In summary, these findings suggest that training an algorithm to predict the preferences of a recruiter and mimic human intuition will inevitably surface and amplify biases. On the other hand, training an algorithm to predict genuine success, using more gender-blind data that accurately forecast job performance, will likely mitigate biases in hiring decisions. This understanding underpins the guidelines on AI-Based Employee Selection Assessments provided by the Society for Industrial and Organizational Psychology (SIOP). The organization strongly urges providers to generate scores that (1) are considered fair and unbiased, (2) are clearly related to the job, (3) predict future job performance, (4) produce consistent scores that measure job-related characteristics, and (5) documented for verification and auditing ( Society for Industrial and Organizational Psychology [SIOP], 2023 ). In other words, “from both research and workplace law perspectives, a clear and theoretically founded link should be established between the outcome (e.g., predicted job performance) and the algorithmic features” ( Society for Industrial and Organizational Psychology [SIOP], 2020 ).

Considering these guidelines, there is great potential for using algorithms to reduce gender discrimination in hiring if it is personality-focused since theory proposes that personality predicts job performance ( Schmitt, 2014 ), and does not vastly differ between genders. For instance, the gender similarities hypothesis ( Hyde, 2005 ) suggests that males and females are similar in most psychological variables. With respect to empirical evidence, when personality facets are examined separately, the effect sizes are close to zero in most cases ( Zell et al., 2015 ). Still, other scholars suggested that some differences between genders exist, with the most impacted facets being those related to agreeableness and neuroticism ( Weisberg et al., 2011 ; Kajonius and Johnson, 2018 ). Thus, the extent of gender differences observed in research findings is still a subject of debate among scientists: some argue that these findings are more commonly characterized by similarities, while others assert that substantial differences are frequently observed. Interestingly, new findings show that gender differences or similarities are reflecting differing ways of organizing the same data, leading Eagly and Revelle (2022) to recommend “recognizing the forest and the trees of sex/gender differences and similarities. It is necessary to step away from the individual trees, perhaps to a hilltop, to observe the patterning of trees in a forest” (p. 1355). While minor differences may exist on particular facets, it is, therefore, essential to transcend a one-dimensional understanding and view the broader picture, observing how the aggregation of various personality facets can highlight distinct differences between genders, or potentially offset certain differences observed within a single facet. For example, while larger differences emerge from averaging multiple indicators that differ by gender ( Eagly and Revelle, 2022 ), one could expect that such differences will be lowered by aggregating a facet that differs by gender with others that do not. Contextualizing the measure of personality ( Judge and Zapata, 2015 ), in order to benefit from the information brought by facet-level ( Soto and John, 2017 ), as well as reducing adverse impact, is, therefore, an intriguing path to explore.

More precisely, it is interesting to look at whether or not personality facets aggregates will lead to bias and adverse impact in personality-based hiring algorithms. Recent research has examined the accuracy of personality prediction in AI-based hiring systems and found that certain tools demonstrate significant instability in measuring key facets. Consequently, these tools cannot be considered valid assessment instruments ( Rhea et al., 2022 ). However, it is still uncertain whether alternative personality-based hiring algorithms, designed to predict job performance based on personality facets, could potentially result in adverse impacts or biases. Indeed, training an algorithm based on personality data, and teaching it to identify relevant and non-gendered cues of performance for a role, could probably help (1) in hiring people who perform better, as personality is predictive of job performance ( Schmitt, 2014 ) and who turnover less ( Kubiak et al., 2023b ), and in (2) achieving natural gender balance for different roles, because even though differences in specific personality facets between genders exist, these differences are smaller compared to other attributes currently used in hiring decisions ( Kuhn and Wolter, 2022 ). Initial findings provide support for this hypothesis, demonstrating that specific personality-based algorithms exhibit gender fairness ( Kubiak et al., 2023a ). However, these studies were limited in their scope and examined a small number of roles.

Therefore, our study introduces a new breed of algorithms for multiple reasons. Firstly, it employs a personality-centric approach, which stands in stark contrast to conventional algorithms that aim to digitize existing hiring procedures by training on data from candidates’ resumes. Such data is riddled with bias ( Parasurama et al., 2022 ), which inevitably trickles down into the algorithmic results ( Houser, 2019 ). Secondly, our algorithm strives to predict future job performance, a marked departure from other algorithms that merely assess personality without making job performance projections. Thus, our study’s algorithms primarily target the identification of personality aspects that drive job performance in a specific occupation, subsequently scoring candidates by juxtaposing their personality, gauged through a personality assessment, against these predictive factors. Finally, to counteract the often-criticized “black box” effect ( Ajunwa, 2020 ), our algorithms are based on explainable regression methods, in order to ensure efficiency but also transparency of the operations.

Our study expands the current knowledge, with the objective of testing whether we can adopt a personality-based algorithm to make hiring recommendations, whilst eliminating any adverse impact with regards to gender. Therefore, we hypothesize that a personality-based hiring algorithm would recommend hiring female and male candidates in (almost) similar proportions for different roles.

4. Materials and methods

This study involved the use of diverse samples. Firstly, training samples were utilized to construct predictive models for each occupation. Predictive modeling, as defined by Kuhn and Johnson (2013) , is “a process of developing a mathematical tool or a model that generates an accurate prediction” (p. 2). In our study, a predictive model is defined as a combination of personality facets that generates an accurate prediction of job performance for a specific occupation. Secondly, a global analysis sample was employed to evaluate any potential adverse impact. For the sake of convenience, these samples will be referred to as training samples and analysis sample in the following sections.

4.1. Participants

Training samples were based on data from 18 employers, all clients of a specialized online assessment platform called “AssessFirst,” dedicated to predictive hiring and personality assessments. These employers specialized in different industries, including retail, technology, consulting, finance and banking, engineering or transportation. Furthermore, the size of the companies varied significantly within the selected group. The range included small-sized companies with approximately 100 employees, as well as large international corporations with over 50,000 employees. Most were located in France (39.90%), followed by Russia (22.60%), the USA (13.46%), and the United Kingdom (9.62%). Other countries included Brazil, Austria, Chile, Germany, Hungary, Morocco, Poland, Portugal, Romania, South Africa and Ukraine. These countries provided a broad geographical base that further enhanced the generalization of the results. These employers were using the platform in a high-stake hiring context, in order to enhance their selection and assessment processes with a heightened degree of objectivity. By using the online recruitment platform, these organizations endeavored to refine their hiring practices. They utilized the platform’s capabilities to construct predictive models for the occupations they sought to fill. The process for developing predictive models is described in the next section. This approach facilitated the comparison of prospective candidates’ personality profiles against the established predictive model, providing a comprehensive analysis of how well a candidate’s personality aligns with the specific requirements of the occupation. This thorough evaluation offered them deep insights, enabling them to make well-informed and objective hiring decisions. The selection of employers for this study was based on their active usage of the online platform during the period between 2021 and 2022. The primary criterion for inclusion was their utilization of the algorithmic-driven predictive model generation feature offered by the online platform. We only integrated into the samples employers who have undergone extensive training on platform usage and have demonstrated their proficiency by creating multiple predictive models. This stringent approach guaranteed that employers who were part of the sample were utilizing the platform correctly. The selection process focused solely on these aspects, without any commercial considerations involved. The purpose of this sampling approach was to ensure that the employers chosen had experience with and utilized the specific feature being investigated, allowing for targeted analysis of the predictive models generated through the platform.

4.2. Models generation

Our study hinged on data provided by these 18 employers, involving 208 unique occupations they were recruiting for. A total of 21 job categories were represented, predominantly sales (26.92%), financial services (13.46%), customer service (10.58%) and business development (7.69%). For each occupation, a distinct predictive model was designed, totaling 208 predictive models. In our study, we focused on developing predictive models that specifically considered the personality facets relevant to job performance in a given occupation. For example, Company 1, which was recruiting for a human resource role in Hungary, generated a predictive model that incorporated the personality facets of Extraversion, Agreeableness, and Openness. These facets were selected by the algorithm based on their statistical associations with job performance in that particular role. It is important to highlight that our algorithm exclusively relies on personality-related data (scores ranging from 1 to 10 on 20 personality facets) and performance-related data (scores ranging from 1 to 5). Our approach represents a departure from traditional hiring algorithms, which typically rely solely on data extracted from the CV or resume of candidates. Instead, we introduced a novel methodology that goes beyond CV data and incorporates personality facets relevant to job performance. Predictive models were generated directly by the employers using a dedicated feature on the online platform. The online platform provider describes the feature as an algorithm-based contact analysis tool that empowers employers to autonomously analyze their data and generate data-driven predictive models for the specific occupations they are hiring for. This tool leverages algorithms to extract insights from the data provided by employers, allowing them to uncover valuable patterns and relationships between personality facets and job performance. The process of predictive model creation in our study was, therefore, characterized by two distinct data collection stages. This was subsequently followed by the application of an algorithm, which selected the relevant personality facets to predict performance in the role. This approach ensured a well-rounded, scientific basis for all the predictive models devised in the study. The process of predictive modeling in the online platform works as follows:

– First, employers selected a representative sample of current employees in the occupation they were recruiting for. For instance, Company 1 chose a sample of 20 employees in the Human Resources role. To accomplish this, employers simply sent invitation emails to the selected employees through the online platform. In this study, it is important to note that the authors did not have direct contact with the employees involved. Instead, the employees were invited by their respective employers to participate in the study. The responsibility of explaining the purpose of the invitation to the selected employees rested with the employers. Subsequently, each employee independently created an account on the platform and provided their consent for their data to be utilized by the employer specifically for the purpose of predictive modeling. Once their account on the online platform was created, employees were asked to complete a forced-choice personality questionnaire. On average, it took approximately 12 min to complete the questionnaire, which consisted of 90 items. The personality questionnaire utilized a hierarchical model of personality based on the Five-Factor Model (FFM). It assessed 20 facets, with each personality trait being evaluated through four distinct facets. The scoring of each facet was done using Item Response Theory (IRT) modeling, and calibrated on a scale from 1 to 10, according to a Gaussian distribution. Following the completion of the assessment, each employee was, therefore, positioned and evaluated in terms of the 20 personality facets. This positioning allows us to understand and quantify the individual’s characteristics and tendencies across the various personality facets. Extensive research has demonstrated the questionnaire’s strong predictive validity ( x ¯ = 0.63), as well as its reliability, as measured by Cronbach’s alpha (α = 0.79) and test–retest reliability ( r = 0.80). Additionally, the questionnaire exhibits high sensitivity (δ = 0.96). The number of employees across the 208 training samples of our study varied from 20 employees to 151 ( M = 41).
– Secondly, the performance of each employee was assessed by their respective direct manager. Managers autonomously accessed the online platform and assigned a rating to each employee within their respective training sample using a standardized scale ranging from 1 (indicating very poor performance) to 5 (reflecting excellent performance). A standardized scale was privileged to ensure objectivity, consistency, comparability and easiness of data collection. During the rating process, managers were prompted to consider the employee’s proficiency and objective job performance, such as revenue generation in the case of a sales occupation. To ensure accuracy of the performance ratings, definitions for each score were directly proposed within the online platform as guidance. This allowed for a comprehensive evaluation of each employee’s performance based on the manager’s insights and observations.

Summary of validity metrics of predictive models ( N = 208).

The predictive models used in this study were autonomously created by individual employers. All relevant data pertaining to each model, including the training sample used, performance score, and results of the regression analysis (i.e., the facets taken into account in the models and score’s expectation for each), were securely stored in the database, following GDPR regulations, of the online recruitment platform. This database served as a repository for the information related to the predictive models created by each employer and was re-used for the purpose of this study.

4.3. Procedure

The initial step of the procedure involved collecting data related to the 208 predictive models from the database. During this process, no filters or selection criteria other than previously mentioned were applied, and all the predictive models created by trained clients of the online recruitment platform between 2021 and 2022 were included. This approach ensured that a comprehensive dataset was obtained, encompassing all available models within the specified timeframe, without any exclusion or bias in the selection process. Author 1 and 3, being affiliated with the online recruitment platform, had convenient access to facilitate the data collection process. For each predictive model, a dataframe consisting of various variables was available. These variables encompassed the following information: (1) name of the company, (2) job category associated with the occupation, (3) specific occupation name, (4) country, (5) data regarding the users included in the training sample, including scores for each personality facet and performance score, in a JSON format (6) facets incorporated in the predictive model and the score expected on each facet, either a high score or a low score, and (7) performance metrics of the predictive model, including accuracy, recall, precision, and ROC AUC.

The second step of the procedure was to assess the potential adverse impact of the predictive models created regarding gender. For this, we constituted a global analysis sample of “potential candidates” who had already taken the personality assessment and had profiles on the online recruitment platform was utilized. These participants have registered on the platform at different times, motivated by various reasons such as receiving invitations from companies or simply wanting to explore and learn more about themselves through the assessments. This approach was chosen for several reasons: (1) utilizing the existing pool of individuals who already had profiles and had taken the personality assessment on the online platform allowed for convenient access to a substantial sample size, (2) although these individuals may not have applied to one of the 208 specific occupations studied, they represented a global population of individuals who could potentially apply for those occupations, broadening the scope of the analysis, and (3) by utilizing this approach, we were able to explore a wider range of predictive models compared to the limitations imposed by using real candidates or specific samples for all 208 occupations. By adopting this methodology on a global scale, we were able to successfully conduct this study on a large and diverse participant pool. The testing sample, therefore, consisted of individuals who met the following criteria: (1) created an account on the online assessment platform in 2022, (2) completed the same personality assessment as described earlier, and (3) provided consent for their anonymized data to be used for scientific and publication purposes. In this specific research, individuals were not directly informed or contacted. However, their data was used with their consent once they registered on the online platform. The analysis sample comprised 273,293 individuals, with 51% identifying as females and 49% as males. The majority of the sample was primarily from France ( n = 210,364, 77%) and held either a master’s degree ( n = 97,584, 36%) or a bachelor’s degree ( n = 96,405, 35%). The study involved access to specific information for each individual in the analysis sample. The available information included the following data points: (1) a score ranging from 1 to 10, representing the measurement of 20 personality facets through the use of a personality assessment employed in this research, and (2) the gender of the individual, categorized as either male or female. By utilizing this global sample, the methodology aimed to assess the potential impact of gender, providing a comprehensive understanding of how males and females scored in relation to each predictive model and the corresponding recommendations made by the scoring algorithm. For each individual within the analysis sample, a fit score was calculated, representing the level of alignment between their personality profile and the predictive model utilized. The fit score were ranging from 0 to 100%. For the purpose of the analysis, this fit score was calculated in a simple way. If the score of the candidate on a facet aligns with the expectation of the predictive model, the candidate was attributed a maximum score for the facet. If the score of the candidate on a facet is opposite to the expectation in the predictive model, the candidate was attributed a minimum score for the facet. If the score of the candidate on a facet taken into account is neutral, the candidate was attributed a medium score for the facet. Then, a simple formula calculates the fit score by summing the individual facet scores and dividing it by the total number of facets in the predictive model. The total result is then multiplied by 100 to express it as a percentage. A higher fit score, closer to 100%, indicated a stronger alignment between the candidate’s profile and the facets that explained performance in the occupation, suggesting a higher likelihood of success on the role. Overall, individuals in the analysis sample who scored above 60% or above the 70th percentile on a predictive model were considered as recommended candidates by the online recruitment platform. Others who fell below these thresholds were not considered recommended. The choice of this threshold was based on studies conducted by the online platform, which demonstrated that individuals scoring above the 70th percentile had better performance and retention rates in the months following their hiring ( Kubiak et al., 2023b ). Following this procedure, we obtained the fit scores of the 273,293 individuals composing the analysis sample for each of the 208 predictive models. The average fit score for females was 52.97 ( SD = 11.48), and 53.22 for males ( SD = 11.56).

4.4. Analysis

To analyze fairness and adverse impact, we applied the concept of impact ratio. The impact ratio is a statistical measure used to assess adverse impact in employment practices, particularly in the context of equal employment opportunity and fair hiring practices. It generally compares the selection rate of a protected group to the selection rate of a reference group (typically the group with the highest selection rate) within a specific job or employment process. The impact ratio is calculated by dividing the selection rate of the protected group by the selection rate of the reference group. In our first analysis, the impact ratio was calculated by dividing the recommendation rate for females (proportion of females who scored above 70th percentile or fit score above 60%) by the recommendation rate for males. Instead of using the selection rate as the metric, the recommendation rate was chosen for evaluation. This decision was made because the algorithm functions as a tool to provide recommendations to employers, rather than making independent decisions. The clients utilizing the online platform are the ultimate decision-makers. Therefore, in order to assess the fairness of the algorithm’s recommendations, rather than the fairness of human decisions, the recommendation rate was deemed more relevant for the research’s objective. While the focus of the analysis was on females as the protected class, considering evidence of discrimination against them, a reverse analysis was also conducted with males as the protected class. This allowed for a comprehensive evaluation of fairness across both genders. Guidelines from the Equal Employment Opportunity Commission ( U.S. Equal Employment Opportunity Commission [EEOC], 1979 ), specifically the four-fifths rule, were followed to assess fairness. According to the rule, a selection tool, or a predictive model in the context of our study, with an impact ratio between 0.8 and 1.0 is generally considered fair. Impact ratios below the threshold of 0.8 indicate a disparate impact, meaning the algorithm or selection method tends to recommend more of one gender over the other. The impact ratios were examined using two approaches: (1) mean weighted impact ratio across all predictive models, and (2) impact ratios broken down by job category, providing a detailed analysis of fairness in each specific job category. By considering these measures, the study aimed to evaluate the fairness of the predictive models and identify any potential disparities in recommendation rates between genders, in accordance with EEOC standards.

In the first analysis, which considered females as the protected class when calculating the impact ratio (Female-Male), we identified 124 predictive models with impact ratios ranging from 0.74 to 1 (mean weighted impact ratio = 0.91; SD = 0.06). The remaining 84 models had impact ratios higher than 1, and were, therefore, considered in the second analysis, with males as the protected class. It is worth mentioning that only eight models from the 124 had impact ratios below 0.8, and were really close to the threshold defined by EEOC. Also, Cohen’s d showed no significant effect on average (mean | d | = 0.11). Results of analysis 1 are presented in Table 2 . In the second analysis (Male-Female), as expected, 84 models were identified, with impact ratios ranging from 0.71 to 0.99 (mean weighted impact ratio = 0.90; SD = 0.06). Only 3 models missed the 0.8 threshold, and Cohen’s d showed no significant effect on average (mean | d | = 0.11). Results of analysis 2 are presented in Table 3 .

Summary of results for analysis 1 (female-male; N = 124).

Summary of results for analysis 2 (male-female; N = 84).

To examine potential impact further, we examined the average impact ratio by job category. Results are presented in Table 4 and show that the average impact ratios are above the 0.8 threshold for every category. The lowest results were for the categories “human resources” and “management board” when males were considered as the minority group, with mean weighted impact ratios of 0.82. Even so, this is in line with EECO standards and supports our study hypothesis.

Mean impact ratios by job category ( N = 208).

Also, to simulate and test each predictive model, we chose to test them on a neutral sample composed of so-called “potential candidates” who were people derived from a global population. In practice, however, candidates who will be scored by the algorithm have higher chances of holding a similar and specific position, which is related to the predictive model (e.g., salespeople for a sales representative predictive model). We ran a preliminary analysis to estimate how testing the algorithm on a specific sample would impact the results. This analysis was conducted on three different occupations: project manager, customer service representative and technician. Overall, impact ratios did not differ significantly and were still matching the EEOC requirements. Results are presented in Table 5 . While promising, these results were obtained through analyzing three jobs only, and further investigation at a larger scale is required to ensure that results replicate with specific samples.

Comparison of impact ratios depending on the type of sample.

Results in brackets are those observed using a global sample.

6. Discussion

This research focus stemmed from alarmingly high gender discrimination that is ongoing in selection and assessment, despite legislation that should prevent discrimination on the basis of gender. To overcome such biases and improve selection, recent years have seen an increase in the use of algorithms in hiring decisions. Nevertheless, little is known about how these kinds of algorithms are used in practice, and some vendors of algorithmic pre-employment assessments are too opaque about the fairness of their solution ( Raghavan et al., 2020 ). Also, while these systems are increasingly subject to technical audits regarding their performance, there is still a lack of proof to support the claims being made by such tools ( Sloane et al., 2022 ). Still, new evidence has shown that using hiring algorithms could help in making better hiring and reducing human bias in selection ( Lakkaraju et al., 2017 ; Li et al., 2020 ; Will et al., 2022 ). These examples should not, however, hide other widely publicized and criticized practices, where the use of algorithms has contributed to exacerbating gender discrimination. Instead, it must open the way to the development and usage of more ethical algorithms, where the beneficial effects prevail. To address this issue, one must rely on data which are mostly gender-blind and are truly predictive of performance. Even if they are widely used in current hiring algorithms, pieces of information from the CV do not meet this double requirement, and force the reproduction of gender bias in selection. There is, indeed, a lot of gendered data in someone’s CV ( Parasurama et al., 2022 ), and simple algorithms can differentiate gender from CV with high accuracy, even after removing the most gendered data like the names, hobbies or gendered words ( Parasurama and Sedoc, 2021 ). On the contrary, data related to personality facets seems better suited for a hiring algorithm’s training, mostly because they are less impacted by gender compared to other data traditionally used in the hiring process and are valid predictors of job performance.

Drawing upon these conclusions, our study examined the gender equity of a novel personality-based hiring algorithm. The overarching aim was to establish whether the algorithm would recommend equal numbers of males and females for several occupations; thus, not being biased toward one gender or another. As hypothesized, results demonstrate that the algorithm does not show gender inequalities when recommending the best-suited candidates for the role, meaning there is no adverse impact. In this sense, impact ratios were in the recommended standard by the EEOC for 95% of the predictive models created. Only 5% of the predictive models fell short below and are considered as having a slight impact. These results illustrate that, when they are trained with the right data, algorithms could help in building more efficient selection processes, which are also fairer for women.

From a theoretical perspective, this work improves our knowledge about how to build gender-blind hiring algorithms by using data related to personality. Also, it complements other studies, by showing that biases and adverse impacts can be reduced even when screening facet-level. Our study demonstrates that while certain distinct differences may exist between males and females concerning specific facets, these disparities become less impactful when viewed within a broader constellation of multiple facets. By aggregating these characteristics with other facets that display similarity across genders, we effectively mitigate the potential for adverse impacts. This approach ensures a more balanced and fair assessment, underscoring the fact that individual variations do not necessarily lead to gender-based discrimination when considered in a comprehensive personality algorithm framework. Ultimately, the crucial question is not about these algorithms achieving perfect fairness in their predictions. Instead, it is about determining whether they enhance existing methods and surpass the current human-driven status quo . While the use of algorithms does raise essential and legitimate concerns, their potential for fostering more efficient and fairer decision-making processes cannot be overlooked, especially when they are trained with appropriate data. In particular, their potential to ensure a more balanced playing field for women is a significant step forward in achieving equity. In addition, our study provides evidence that even simple algorithms can effectively reduce gender discrimination. Many individuals have expressed concerns about using algorithmic hiring processes due to a lack of understanding ( Liem et al., 2018 ). However, our findings demonstrate that explainable algorithms can have a significant positive impact. By showcasing the potential of such algorithms, we aim to encourage the adoption of fair and unbiased decision-making tools in hiring.

Moreover, our conclusions are opening the way for future research about personality-based hiring algorithms. First, an interesting question arising from this work is about the capacity of such algorithms to be applied in practice, where they will probably be trained on male-dominated samples, as many could be forced to do due to the current disparities in the workplace. However, even if an algorithm is trained on a male-dominated sample, it could still provide fair outcomes when applied to a balanced or neutral sample, if it leverages data equally representative of both genders. This potential fairness arises from the algorithm’s reliance on well-distributed data, where the features it uses for prediction are equally prevalent in both males and females. For example, if an algorithm is trained to predict job performance based on facets like imagination, trust or self-efficacy. Although these traits might be learned from a male-dominated sample, they are not exclusive to any gender ( Kajonius and Johnson, 2018 ). Males and females alike can exhibit high levels of imagination, trust or self-efficacy. Therefore, if the algorithm focuses on these universally applicable facets rather than gender-specific features, it should provide fair and unbiased predictions when applied to a gender-balanced sample ( Kubiak et al., 2023a ). Second, it is still unclear whether these kinds of algorithms could display the same results for other kinds of discrimination, for example, disability-based discrimination, which remains intense ( Lippens et al., 2023 ). Third, even if our study showed that there was no adverse impact for 95% of the predictive models tested, we still need to address the 5% remaining: while their impact ratios are really close to the EECO requirements and do not fall lower than 0.71, some adjustments are required in order to use them in high-stakes hiring practice and be confident that they will not harm any group based on gender. For these models, future research could focus on addressing the diversity-validity dilemma, which concerns the tradeoff between selecting valid predictors of performance while minimizing adverse impact ( Pyburn et al., 2008 ; Rupp et al., 2020 ; Rottman et al., 2023 ). As such, it seems necessary to identify strategies to target facets within the predictive model that lower the impact ratio, and to propose alternatives. It could also foster the algorithm’s explainability, by being transparent about the predictive model limitation, and how one could improve it to make it fairer regarding gender while making the smallest compromises possible about validity. For example, studies could use a feature importance framework to iteratively prune biased features with the lowest predictive power from the model.

Our work also has several practical implications. First, given the prevailing talent shortage, employers are increasingly finding it challenging to fill roles effectively. As such, it is imperative they shift focus and explore alternate indicators of potential, beyond traditional markers like academic degrees, to truly uncover and understand the essence of talent and assess the employability of their candidates ( Chamorro-Premuzic, 2017 ). Employers can consider personality as a compelling alternative to traditional CV-based assessments, as it relates performance while being less susceptible to gender bias ( Schmidt et al., 2016 ; Sackett et al., 2022 ). In addition, it is becoming increasingly imperative for employers to demonstrate that their hiring practices and tools are devoid of biases, ensuring that no particular group is unfairly disadvantaged based on their gender ( Hunkenschroer and Kriebitz, 2023 ). Our study shows great potential in helping employers to accurately identify the underlying mechanisms of performance for a specific occupation and to reduce gender biases. That way, they might be able to hire people who are better suited for the role and perform better, and who are more diverse in terms of gender. Secondly, personality-based algorithms, by increasing the fairness of the hiring process, could probably promote organizational attractiveness. Indeed, considering the existing labor talent shortages and the significant role of an organization’s recruitment process perception in determining a candidate’s decision to accept a job offer ( Hausknecht et al., 2004 ), enhancing the perceived fairness of algorithmic recruitment tools carries substantial implications. Recent research showed that algorithm-driven hiring processes are perceived as less fair compared to human-only decisions by candidates ( Lavanchy et al., 2023 ) and that people feel less capable of influencing the outcome of an algorithm compared to human judgment ( Li et al., 2021 ; Hilliard et al., 2022 ). Interestingly, fairness mediates the association between an algorithm-based selection process and organizational attractiveness and the intention to further proceed with the selection process ( Köchling and Wehner, 2022 ). Consequently, it is in the best interest of employers to utilize personality-based algorithms, due to their increased fairness, to improve their attractiveness among potential candidates. This ensures that candidates are not discouraged or deterred from the process due to the perception of algorithmic unfairness. Thirdly, implementing an algorithm-based evaluation system could potentially boost the number of female applicants for a company and enhance the completion rates for the assessment process. This is due to the observed tendency of women being more inclined to complete an assessment when informed that the evaluation is conducted by an algorithm, rather than a human recruiter ( Avery et al., 2023 ). Such a shift could play a pivotal role in fostering gender diversity within organizations by expanding the pool of female candidates applying for jobs. Heilman (1980) found that both male and female evaluators made significantly more favorable personnel decisions when females constituted 25% or more of the total candidate pool. Thus, increasing the representation of females in the candidates pool through algorithm-based evaluations could lead to more balanced hiring outcomes. Fourthly, our study serves as a useful guide for employers navigating forthcoming legislation such as New York’s AI hiring law. Recently enacted, the NYC Automated Employment Decision Tool law mandates employers using AI in hiring to disclose its use to candidates. Further, it necessitates annual independent audits to demonstrate the absence of discriminatory practices in their systems. Moreover, candidates are granted the right to request information from potential employers about what data the technology collects and analyzes. Non-compliance with these regulations could result in fines of up to $1,500. Our study helps employers align their processes with these requirements, paving the way for transparent, accountable, and unbiased algorithm-driven hiring.

7. Limitations

Several limitations of this research should be taken into consideration. First, while the strength of our study was that it considered 208 occupations across 21 categories, we did not include occupations that are more stereotypically judged as being gender specific. Therefore, future research can aim to retest our algorithm on an even wider array of job categories and focus specifically on occupations which are perceived to be predominately feminine. For example, studies showed that occupations related to caregiving are seen as being more feminine ( Couch and Sigler, 2001 ), or that it persists presumptions about the gender of people employed in healthcare, notably nurses ( Ekberg and Ekberg, 2017 ). Our sample unfortunately did not include occupations from these highly stereotypical categories. We could not include these occupations in our study, as none of the participating employers were recruiting for such roles. In fact, the employers utilizing the online platform were primarily focused on filling business-related positions (see Table 4 ).

Secondly, our study’s scope was limited to gender as a characteristic, which leaves room for further exploration. Recent research indicates the existence of intersectional effects between various attributes. For instance, Derous and Pepermans (2019) uncovered a “double jeopardy” situation for Maghreb/Arab female applicants applying for high-cognitive demand roles–an issue not apparent in applications for low-cognitive demand jobs. Such findings emphasize the necessity for more nuanced investigations that consider the interactions between multiple characteristics. Future research could delve into the potential adverse impacts of personality-based algorithms by examining intersectionality, such as the combined effect of gender and ethnicity. This could pave the way for more comprehensive understanding and better refinement of fair algorithmic-based hiring practices.

Third, our study tested the algorithm on males and females, as data collection for these genders was simpler and more easily accessible. However, we acknowledge that there are numerous gender non-conforming categories. Unfortunately, we did not find any satisfactory published research which studied how personality differs between males, females and people identifying as gender diverse. The only evidence we have drawn upon is the analysis proposed by Anzani et al. (2020) , which delved into the personality patterns of a transgender cohort compared with normative samples of cisgender females and males. Their findings revealed that transgender women scored lower than cisgender women on two primary domains (Negative Affectivity and Psychoticism) and on seven facets. Transgender men, meanwhile, scored lower than cisgender men on Antagonism and five other facets. However, these results were derived from relatively small sample sizes of transgender individuals, all of whom were pursuing medical treatments. Consequently, these findings may not accurately represent the broader transgender and gender-non-conforming population. This indicates the necessity for future investigation into the algorithm’s gender neutrality, especially when considering the inclusion of diverse groups beyond the traditional gender binary.

Finally, we should also mention potential bias in how the rating of each employee (from 1 to 5) was made by their manager. Indeed, even though managers were prompted to reflect on the employee’s productivity and objective performance, no other specific guideline was proposed. As a result, there is a chance that different managers could have reflected upon different types of performance when making their ratings. Gender bias has also frequently been identified in performance appraisal. For example, (1) Correll et al. (2020) showed that it exists differences in the language used to describe females and males performance and that the same behaviors could impact performance ratings differently depending on the employee’s gender, (2) Benson et al. (unpublished) revealed differences in potential ratings between gender, and, (3) Rivera and Tilcsik (2019) showed that the number of scale points used for the evaluations significantly affect the size of the gender gap in male-dominated fields. Still, there are reasons to believe that the ratings made were accurate estimates of objective performance: (1) as shown by Jackson and Furnham (2001) , biases such as halo do not necessarily reduce rating accuracy, and supervisor ratings are useful measures of overall performance, (2) managerial ratings have a good corrected mean correlation with objective performance for salesperson job performance ( Jaramillo et al., 2005 ), which is a type of role composing one-third of our total sample, (3) ratings have been shown to be more accurate for unskilled, skilled and professional workers compared to managerial occupations ( Miller and Thornton, 2006 ), and these three levels of occupations are the most represented in our sample, and (4) each of the scale’s point were clearly defined in the rating form. Other studies should, however, try to measure performance in a more structured and controlled manner. Furthermore, future research should also incorporate a more comprehensive understanding of job performance, considering a wide range of relevant factors. For instance, Rotundo and Sackett (2002) pinpointed three broad components of job performance: task performance, citizenship behavior, and counterproductive performance. They further demonstrated that two primary elements of performance–tasks performance and counterproductive performance–were the more weighted by raters. Recent research also suggests an increasing interest in other types of performance. Contextual performance, for example, includes behaviors that contribute to the social and psychological environment ( Ramos-Villagrasa et al., 2022 ). Adaptive performance, on the other hand, pertains to an employee’s ability to modify their thoughts, behaviors, and emotions to adapt to their evolving work environment. Such adaptations can encompass adjustments to new technologies, procedures, business processes, or work roles ( Baard et al., 2014 ). Given that meta-analyses have revealed that traits have differential relationships with contextual ( He et al., 2019 ) and adaptive performance ( Huang et al., 2014 ), it would be prudent to incorporate these insights in future research.

8. Conclusion

Gender stereotypes are incredibly stable. For example, Offermann and Coats (2018) showed that ILTs (Implicit Leadership Theories) did not change during the last 20 years, despite organizational and societal changes. Also, large-scale cross-national field experiments highlight occupational gender composition ( Birkelund et al., 2022 ; Adamovic and Leibbrandt, 2023 ), showing disparate proportions of individuals of a particular gender working in specific occupations. This is particularly salient in online hiring, which triggers the use of cognitive shortcuts about the role-specific abilities of each gender ( Galperin, 2021 ). This persistence of gender discrimination in hiring, despite all the efforts made for so many years, calls for the identification of strategies that will lead to an effective and lasting response. The findings from our research suggest that personality-based hiring algorithms serve as an effective solution, demonstrating non-adverse impact in most instances. In other words, they do not unfairly disadvantage certain groups of people based on their gender. Properly trained and used, these algorithms could help organizations to build fairer decision-making processes.

Data availability statement

Ethics statement.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements because written informed consent from the participants was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author contributions

EK and SB: conceptualization, methodology, data collection, and data analysis. EK: supervision. EK and ME: writing—original draft. All authors contributed to the writing—review and editing and read and agreed to the current version of the manuscript.

Conflict of interest

ME, EK, and SB were employed by AssessFirst. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Hilary cass says u.s. doctors are ‘out of date’ on youth gender medicine.

Dr. Hilary Cass published a landmark report that led to restrictions on youth gender care in Britain. U.S. health groups said it did not change their support of the care.

Hilary Cass standing near a bush with her hands clasped before her on a sunny day. She wears a colorful shirt and black slacks.

By Azeen Ghorayshi

After 30 years as one of England’s top pediatricians, Dr. Hilary Cass was hoping to begin her retirement by learning to play the saxophone.

Instead, she took on a project that would throw her into an international fire: reviewing England’s treatment guidelines for the rapidly rising number of children with gender distress, known as dysphoria.

At the time, in 2020, England’s sole youth gender clinic was in disarray. The waiting list had swelled, leaving many young patients waiting years for an appointment. Staff members who said they felt pressure to approve children for puberty-blocking drugs had filed whistle-blower complaints that had spilled into public view. And a former patient had sued the clinic, claiming that she had transitioned as a teenager “after a series of superficial conversations with social workers.”

The National Health Service asked Dr. Cass, who had never treated children with gender dysphoria but had served as the president of the Royal College of Pediatrics and Child Health, to independently evaluate how the agency should proceed.

Over the next four years, Dr. Cass commissioned systematic reviews of scientific studies on youth gender treatments and international guidelines of care. She also met with young patients and their families, transgender adults, people who had detransitioned, advocacy groups and clinicians.

Her final report , published last month, concluded that the evidence supporting the use of puberty-blocking drugs and other hormonal medications in adolescents was “remarkably weak.” On her recommendation, the N.H.S. will no longer prescribe puberty blockers outside of clinical trials. Dr. Cass also recommended that testosterone and estrogen, which allow young people to develop the physical characteristics of the opposite sex, be prescribed with “extreme caution.”

Dr. Cass’s findings are in line with several European countries that have limited the treatments after scientific reviews . But in America, where nearly two dozen states have banned the care outright, medical groups have endorsed the treatments as evidence-based and necessary .

The American Academy of Pediatrics declined to comment on Dr. Cass’s specific findings, and condemned the state bans. “Politicians have inserted themselves into the exam room, which is dangerous for both physicians and for families,” Dr. Ben Hoffman, the organization’s president, said.

The Endocrine Society told The New York Times that Dr. Cass’s review “does not contain any new research” that would contradict its guidelines. The federal health department did not respond to requests for comment.

Dr. Cass spoke to The Times about her report and the response from the United States. This conversation has been edited and condensed for clarity.

What are your top takeaways from the report?

The most important concern for me is just how poor the evidence base is in this area. Some people have questioned, “Did we set a higher bar for this group of young people?” We absolutely didn’t. The real problem is that the evidence is very weak compared to many other areas of pediatric practice.

The second big takeaway for me is that we have to stop just seeing these young people through the lens of their gender and see them as whole people, and address the much broader range of challenges that they have, sometimes with their mental health, sometimes with undiagnosed neurodiversity. It’s really about helping them to thrive, not just saying “How do we address the gender?” in isolation.

You found that the quality of evidence in this space is “remarkably weak.” Can you explain what that means?

The assessment of studies looks at things like, do they follow up for long enough? Do they lose a lot of patients during the follow-up period? Do they have good comparison groups? All of those assessments are really objective. The reason the studies are weak is because they failed on one or more of those areas.

The most common criticism directed at your review is that it was in some way rigged because of the lack of randomized controlled trials, which compare two treatments or a treatment and a placebo, in this field. That, from the get-go, you knew you would find that there was low-quality evidence.

People were worried that we threw out anything that wasn’t a randomized controlled trial, which is the gold standard for study design. We didn’t, actually.

There weren’t any randomized controlled trials, but we still included about 58 percent of the studies that were identified, the ones that were high quality or moderate quality. The kinds of studies that aren’t R.C.T.s can give us some really good information, but they have to be well-conducted. The weakness was many were very poorly conducted.

There’s something I would like to say about the perception that this was rigged, as you say. We were really clear that this review was not about defining what trans means, negating anybody’s experiences or rolling back health care.

There are young people who absolutely benefit from a medical pathway, and we need to make sure that those young people have access — under a research protocol, because we need to improve the research — but not assume that that’s the right pathway for everyone.

Another criticism is that this field is being held to a higher standard than others, or being exceptionalized in some way. There are other areas of medicine, particularly in pediatrics, where doctors practice without high-quality evidence.

The University of York, which is kind of the home of systematic reviews, one of the key organizations that does them in this country, found that evidence in this field was strikingly lower than other areas — even in pediatrics.

I can’t think of any other situation where we give life-altering treatments and don’t have enough understanding about what’s happening to those young people in adulthood. I’ve spoken to young adults who are clearly thriving — a medical pathway has been the right thing for them. I’ve also spoken to young adults where it was the wrong decision, where they have regret, where they’ve detransitioned. The critical issue is trying to work out how we can best predict who’s going to thrive and who’s not going to do well.

In your report, you are also concerned about the rapid increase in numbers of teens who have sought out gender care over the last 10 years, most of whom were female at birth. I often hear two different explanations. On the one hand, there’s a positive story about social acceptance: that there have always been this many trans people, and kids today just feel freer to express who they are. The other story is a more fearful one: that this is a ‘contagion’ driven in large part by social media. How do you think about it?

There’s always two views because it’s never a simple answer. And probably elements of both of those things apply.

It doesn’t really make sense to have such a dramatic increase in numbers that has been exponential. This has happened in a really narrow time frame across the world. Social acceptance just doesn’t happen that way, so dramatically. So that doesn’t make sense as the full answer.

But equally, those who say this is just social contagion are also not taking account of how complex and nuanced this is.

Young people growing up now have a much more flexible view about gender — they’re not locked into gender stereotypes in the way my generation was. And that flexibility and fluidity are potentially beneficial because they break down barriers, combat misogyny, and so on. It only becomes a challenge if we’re medicalizing it, giving an irreversible treatment, for what might be just a normal range of gender expression.

What has the response to your report been like in Britain?

Both of our main parties have been supportive of the report, which has been great.

We have had a longstanding relationship with support and advocacy groups in the U.K. That’s not to say that they necessarily agree with all that we say. There’s much that they are less happy about. But we have had an open dialogue with them and have tried to address their questions throughout.

I think there is an appreciation that we are not about closing down health care for children. But there is fearfulness — about health care being shut down, and also about the report being weaponized to suggest that trans people don’t exist. And that’s really disappointing to me that that happens, because that’s absolutely not what we’re saying.

I’ve reached out to major medical groups in the United States about your findings. The American Academy of Pediatrics declined to comment on your report, citing its own research review that is underway . It said that its guidance, which it reaffirmed last year, was “grounded in evidence and science.”

The Endocrine Society said “we stand firm in our support of gender-affirming care,” which is “needed and often lifesaving.”

I think for a lot of people, this is kind of dizzying. We have medical groups in the United States and Britain looking at the same facts, the same scientific literature, and coming to very different conclusions. What do you make of those responses?

When I was president of the Royal College of Pediatrics and Child Health, we did some great work with the A.A.P. They are an organization that I have enormous respect for. But I respectfully disagree with them on holding on to a position that is now demonstrated to be out of date by multiple systematic reviews.

It wouldn’t be too much of a problem if people were saying “This is clinical consensus and we’re not sure.” But what some organizations are doing is doubling down on saying the evidence is good. And I think that’s where you’re misleading the public. You need to be honest about the strength of the evidence and say what you’re going to do to improve it.

I suspect that the A.A.P., which is an organization that does massive good for children worldwide, and I see as a fairly left-leaning organization, is fearful of making any moves that might jeopardize trans health care right now. And I wonder whether, if they weren’t feeling under such political duress, they would be able to be more nuanced, to say that multiple truths exist in this space — that there are children who are going to need medical treatment, and that there are other children who are going to resolve their distress in different ways.

Have you heard from the A.A.P. since your report was published?

They haven’t contacted us directly — no.

Have you heard from any other U.S. health bodies, like the Department of Health and Human Services, for example?

Have you heard from any U.S. lawmakers?

No. Not at all.

Pediatricians in the United States are in an incredibly tough position because of the political situation here. It affects what doctors feel comfortable saying publicly. Your report is now part of that evidence that they may fear will be weaponized. What would you say to American pediatricians about how to move forward?

Do what you’ve been trained to do. So that means that you approach any one of these young people as you would any other adolescent, taking a proper history, doing a proper assessment and maintaining a curiosity about what’s driving their distress. It may be about diagnosing autism, it may be about treating depression, it might be about treating an eating disorder.

What really worries me is that people just think: This is somebody who is trans, and the medical pathway is the right thing for them. They get put on a medical pathway, and then the problems that they think were going to be solved just don’t go away. And it’s because there’s this overshadowing of all the other problems.

So, yes, you can put someone on a medical pathway, but if at the end of it they can’t get out of their bedroom, they don’t have relationships, they’re not in school or ultimately in work, you haven’t done the right thing by them. So it really is about treating them as a whole person, taking a holistic approach, managing all of those things and not assuming they’ve all come about as a result of the gender distress.

I think some people get frustrated about the conclusion being, well, what these kids need is more holistic care and mental health support, when that system doesn’t exist. What do you say to that?

We’re failing these kids and we’re failing other kids in terms of the amount of mental health support we have available. That is a huge problem — not just for gender-questioning young people. And I think that’s partly a reflection of the fact that the system’s been caught out by a growth of demand that is completely outstripping the ability to provide it.

We don’t have a nationalized health care system here in the United States. We have a sprawling and fragmented system. Some people have reached the conclusion that, because of the realities of the American health care system, the only way forward is through political bans. What do you make of that argument?

Medicine should never be politically driven. It should be driven by evidence and ethics and shared decision-making with patients and listening to patients’ voices. Once it becomes politicized, then that’s seriously concerning, as you know well from the abortion situation in the United States.

So, what can I say, except that I’m glad that the U.K. system doesn’t work in the same way.

When asked after this interview about Dr. Cass’s comments, Dr. Hoffman, the A.A.P.’s president, said that the group had carefully reviewed her report and “added it to the evidence base undergoing a systematic review.” He also said that “Any suggestion the American Academy of Pediatrics is misleading families is false.”

Azeen Ghorayshi covers the intersection of sex, gender and science for The Times. More about Azeen Ghorayshi

Talking to science and health leaders.

Sammy Ramsey : The entomologist wants to change people’s perception of cicadas , casting their mass emergence as a love story, not an insect apocalypse.

Edward Dwight : Six decades ago, the pilot’s shot at becoming the first Black astronaut in space was thwarted by racism and politics. At 90, he finally made it .

Beth Linker: The historian and sociologist of science re-examines the “posture panic” of the early 20th century. You’ll want to sit down for this .

Lisa Kaltenegger: The director of the Carl Sagan Institute at Cornell University hunts for aliens by studying Earth across time .

Nora Volkow : The director of the National Institute on Drug Abuse would like the public to know that drug-use trends among teens are getting better . Mostly.

UN Women HQ

New UN research reveals impact of AI and cybersecurity on women, peace and security in South-East Asia

Date: Tuesday, 21 May 2024

[Joint press release]

Bangkok, Thailand — Systemic issues can put women’s security at risk when artificial intelligence (AI) is adopted, and gender biases across widely used AI-systems pose a significant obstacle to the positive use of AI in the context of peace and security in South-East Asia.

Moreover, women human rights defenders (WHRDs) and women’s Civil Society Organisations (WCSOs) in the region are at high risk of experiencing cyber threats and, while largely aware of these risks, are not necessarily able to prepare for, or actively recover from, cyber-attacks.

These are among the key findings of groundbreaking research released today by UN Women and the United Nations University Institute in Macau (UNU Macau) examining the connections between AI, digital security and the women, peace and security (WPS) agenda in South-East Asia.

The research was made possible with support from the Government of Australia, under the Cyber and Critical Tech Cooperation Program (CCTCP) of the Department for Foreign Affairs and Trade (DFAT), and the Government of the Republic of Korea through the UN Women initiative, Women, Peace and Cybersecurity: Promoting Women, Peace and Security in the Digital World.

With AI projected to add USD 1 trillion to the gross domestic product of South-East Asian countries by 2030, understanding the impact of these technologies on the WPS agenda is critical to supporting these countries to regulate the technologies and mitigate their risks.

The report Artificial Intelligence and the Women, Peace and Security Agenda in South-East Asia , examines the opportunities and risks of AI from this unique perspective by focusing on four types of gender biases in AI – discrimination, stereotyping, exclusion, and insecurity – which need to be addressed before the region can fully benefit from new technological developments.

This research examines the relationship between AI and WPS according to three types of AI and its applications: AI for peace, neutral AI, and AI for conflict.

This report notes that across these categories, there are favourable and unfavourable effects of AI for gender-responsive peace and women’s agency in peace efforts.

While using AI for peace purposes can have multiple benefits, such as improving inclusivity and the effectiveness of conflict prevention and tracking evidence of human rights breaches, it is used unequally between genders, and pervasive gender biases render women less likely to benefit from the application of these technologies.

The report also highlights risks related to the use of these technologies for military purposes.

This research identifies two dimensions to improving the dynamics of AI and the WPS agenda in the region: mitigating the risks of AI systems to advancing the WPS agenda, especially on social media, but also on other tools, such as chatbots and mobile applications; and fostering the development of AI tools built explicitly to support gender-responsive peace in line with WPS commitments.

The second report, Cybersecurity Threats, Vulnerabilities and Resilience among Women Human Rights Defenders and Civil Society in South-East Asia , explores cybersecurity risks and vulnerabilities in this context with the goal of promoting cyber-resilience and the human and digital rights of women in all their diversity.

While there is increasing awareness of the risks women and girls face in cyberspace, there is little understanding of the impacts of gender on cybersecurity, or of the processes and practices used to protect digital systems and networks from cyber risks and their harms.

This work differs from previous research into cybersecurity as it focuses on human-centric as compared to techno-centric cybersecurity and emphasises human factors rather than technical skills as well as the centralisation of gender as critical to cybersecurity.

Furthermore, cyber threats are understood to be gendered in nature, whereby WCSOs and WHRDs are specifically targeted due to the focus of their work and are likely to be attacked with misogynistic and sexualised harassment.

The results highlight that digital technologies are central to the work of WCSOs and WHRDs, while simultaneously noting that WCSOs had higher threat perceptions and threat experiences compared to CSOs that do not work on gender and women’s rights, carrying disproportionate risks of disrupting their work, damaging their reputation, and even creating harm or injury, all of which contribute to marginalising women’s voices.

The largest differences of experienced threats between the groups were for online harassment, trolling (deliberately provoking others online to incite reactions) and doxxing (when private or identifying information is distributed about someone online without their permission).

This report’s recommendations include fostering inclusive and collaborative approaches in cybersecurity policy development and engagement, and building the knowledge of civil society, government, private-sector actors and other decision makers to develop appropriate means of prevention and response to cyberattacks and their disproportionate impacts on WCSOs and WHRDs.

Specific attention should be given to at-risk individuals and organizations, such as women’s groups operating in politically volatile and conflict and crisis-affected contexts and situations where civic space is shrinking.

The launch took place during a UN Women youth conference, Gen-Forum 2024: Young Leaders for Women, Peace and Security in Asia and the Pacific which commenced today in Bangkok, Thailand.

UNU Macau and UN Women aim for this research, conducted over 12 months, to contribute to the global discourse on ethics and norms surrounding AI and digital governance at large.

Next, training materials based on the research findings and consultations with women’s rights advocates in the region will be rolled out, initially in Thailand and Vietnam, with e-learning modules and training handbooks to be publicly available in English, Thai and Vietnamese for interested stakeholders from mid-2024.

More information

Download full reports and research summaries:

Artificial Intelligence and the Women, Peace and Security Agenda in South-East Asia
Cybersecurity Threats, Vulnerabilities and Resilience among Women Human Rights Defenders and Civil Society in South-East Asia

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