A 2026 study found that when women and men both use AI assistance to create identical resumes, the women are viewed as less competent and less trustworthy, while the men are credited with taking initiative. This competence penalty helps explain why women have lower AI use rates than men.
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Why are women 25% less likely to use artificial intelligence tools than men? New research debunks the notion that the gender gap is primarily due to women’s lack of AI skills, interest or access. Women’s hesitancy to use AI is instead a rational response to a competency penalty that women face when using AI in the workplace.
The latest evidence of the competence penalty that women workers incur when using AI comes from a 2026 study by Zehra Chatoo, founder of Code For Good Now and former strategist at Meta. The study found that women who submit resumes created with AI assistance are evaluated as less competent and less trustworthy than men who submit identical AI-assisted resumes. To the evaluators, women’s AI use signaled inability, while men’s AI use signaled initiative.
This study bolsters prior research finding a competency penalty against women who use AI to efficiently perform their jobs. This research indicates that organizations will not close the gender AI gap simply by investing in digital skills training and AI incentives for their workforce. Employers must also address the gender-based perception biases linked to AI use.
In the new study, Chatoo created an AI-supported resume for a marketing position and asked 1,000 adults in the U.K. to evaluate the candidate during April 2026. The evaluators received identical resumes and were told that the candidate had used AI assistance. The only difference among the resumes was the candidate’s name. Half of the evaluators saw Emily Clarke, while half saw James Clark.
Despite identical resume content, the evaluators judged women candidates much more harshly for using AI assistance than men.
The evaluators who attributed the AI-assisted resume to a woman were twice as likely to question the candidate’s competency. “She can’t even write a CV herself—not sure she has the skill to carry out the job,” said one of the evaluators of Emily’s resume.
In contrast, evaluators who attributed the AI-assisted resume to a man were twice as likely to view the candidate as showing initiative. In other words, a woman’s use of AI was evidence of inability, while a man’s use of AI to create the identical product was viewed as pragmatic problem solving.
“The judgment gap revealed is not simply that women are judged more harshly in professional evaluation, a finding already well documented,” said Chatoo. “It is that women who use AI are judged more harshly specifically for using it.”
Evaluators were also 22% more likely to doubt the candidate’s trustworthiness if the AI-assisted resume came from Emily rather than James. “When men use AI, we question their effort. When women use AI, we question their integrity,” said Chatoo. “That difference changes the perceived risk of using AI.”
The study also found that an evaluator’s own familiarity with AI tools did not eliminate gender bias when assessing others’ AI use. Older evaluators showed less gender bias than male Gen Z evaluators, who are more likely to use AI themselves. Among Gen Z males, 97% rated James as a “strong” candidate while only 76% rated Emily as “strong,” representing a 21 percentage point gender gap.
Chatoo warns that the AI use penalty may be even larger for older women, women of color, or other women who face intersectional sources of bias. “This study focused on gender as a single variable,” said Chatoo. “Race, ethnicity, age, and socioeconomic background all interact with gender to produce further differentiated outcomes, and the AI Judgment Penalty is likely to compound across those dimensions.”
Chatoo’s study bolsters prior research documenting a competence penalty faced by women who use AI in the workplace. Women are aware of this gender bias, which rationally contributes to their lower rates of AI use.
“Women’s hesitation is not a skills gap. It is an accurate read of an uneven environment,” said Chatoo. “When the same output is evaluated differently based on the name at the top, caution is the logical response.”
A 2025 study found that women face a competence penalty for using AI at work even in companies that actively encourage their employees to use AI tools.
This prior study was launched after a global technology company found that only 31% of its female software engineers were using an AI tool despite the company’s year-long campaign to incentivize AI adoption. Company leaders contacted researchers from Peking University and Hong Kong Polytechnic University to investigate the disappointing results.
To assess why women engineers were using AI at lower rates than male engineers, the researchers asked 1,026 software engineers at the company to evaluate an identical piece of computer code. While all of the engineers reviewed the exact same code, they received varied instructions about whether the code was written with or without AI assistance, and whether the coder was female or male.
As expected, the reviewers rated the objective quality of the identical computer code similarly in all conditions. However, the reviewers offered very different ratings of the engineers who wrote the code. The reviewers imposed a competence penalty against all of the engineers who purportedly used AI as compared to non-AI users. And the penalty for AI use was much harsher for women than for men.
While male AI users received 6% lower competence ratings than non-AI users, female AI users received 13% lower competence ratings, even though they all produced identical computer code. Male evaluators who did not use AI themselves imposed the most severely gender-biased competence penalties. Male non-AI users rated women engineers who used AI 26% more harshly than men who used AI to create the same work product.
The study further revealed how women’s workplace contributions get devalued when they use AI. When asked to estimate the relative contribution of the engineer versus the AI tool, the evaluators assumed that the AI tool had done more of the work when they believed the coder was a woman rather than a man. “The AI assistance is framed as a ‘proof’ of their inadequacy rather than evidence of their strategic tool use,” concluded the researchers.
Women engineers at the company were aware of this gender-based competence penalty, which helped explain their rational reluctance to use AI. In a follow-up survey of 919 engineers at the company, women were more likely than men to express fear that using AI would decrease their manager’s evaluation of their ability—which turned out to be an accurate concern.
“The fear women have about being judged for using AI is not perception,” said Chatoo. “It is measurable. It is real.” This means that companies will not close the gender gap in employee AI adoption solely through technological training or incentives. Employers must also convince women that they will be evaluated fairly for their AI use.
“You cannot upskill people out of structural bias,” said Chatoo. “Closing the AI adoption gap means addressing not just how people use AI, but how that use is evaluated.”
Three practices can help organizations reduce the competence penalty that women face from using AI in the workplace.
“If your AI adoption data is not broken down by gender, you cannot see the gap, let alone address it,” said Chatoo. “Start measuring.” AI use metrics can also be disaggregated by race, age and other statuses that trigger ability biases.
While gathering AI adoption data is a critical step, employers must also avoid making assumptions about the causes of usage gaps. Anonymized surveys can help identify the extent to which concerns about a competence penalty are driving lower rates of AI use among certain workers.
An effective way to reduce the gender-based competency bias when evaluating AI-assisted work is by using a “blind review” process. This approach ensures that the evaluator is unaware of which employee produced the product that is being assessed. Removing personally identifying information about gender and other potentially biasing characteristics can increase fair and consistent performance evaluations around AI use.
When a blind review process is not feasible, bias may still be reduced by directing evaluators to assess the work product, rather than evaluating the worker. In the 2025 study involving AI-assisted computer code, the evaluators accurately rated the quality of the identical code similarly, regardless of the coder’s gender. Bias against women AI users only appeared when the evaluators were asked to rate the coder’s competence and contributions.
This finding suggests the importance of training managers to assess the quality of AI-driven work product, rather than evaluating qualities of the worker who used an AI tool.
Gender bias is more likely when evaluators use subjective assessment criteria, such as competence, strength or trustworthiness. Employers should base hiring evaluations on specific skills necessary for actual job tasks, and they should base performance evaluations on objective measurements of productivity or the quality of an employee’s work product.
These three practices should not only help organizations reduce the competence penalty against women who use AI but also promote fairness in all contexts where stereotypes may bias workplace evaluations. Ensuring fair evaluations of employees’ AI use will also encourage greater employee adoption of AI tools.
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