Artificial intelligence is increasingly becoming a part of how organizations hire and assess talent. From resume screening to evaluation, these tools promise speed and consistency. But the efficacy of these tools is entirely based on their programming, which can be inherently flawed.
Bias in AI hiring is more than a technology problem when it reflects patterns present in how organizations have historically defined and evaluated talent. These systems learn from human decisions which have not affected groups equally. This is particularly relevant when considering how women are evaluated at work.
The Data Problem Behind AI Decisions
AI systems learn patterns from historical data. In hiring, that data reflect who has been selected, promoted and labeled as “high potential.” Those decisions are shaped not only by performance, but also by access to opportunities and visibility.
Women remain underrepresented in leadership roles, holding only about 5% of CEO positions, per S&P Global. That gap is explained using assumptions about ambition or leadership style. However, research shows men and women are equivalent in characteristics that lead to leadership success such as ambition, decisiveness and resilience.
This creates a disconnect between how women perform and how they are represented in hiring data. AI systems trained on that data learn not only who succeeded, but who was given opportunities. Because women have historically had less access to opportunities, the data can underrepresent their potential.
Why AI Can Reinforce Existing Patterns
AI systems tend to favor candidates whose experiences resemble those of people already hired or promoted. The challenge is that those experiences are not distributed evenly.
Career paths and progression timelines are shaped by opportunity. Women are more likely to have had interrupted careers, fewer stretch assignments or less access to high-visibility roles, according to a report from LeanIn.Org and McKinsey. These differences are not indicators of ability, but they influence how experience is evaluated.
Job titles can indicate past experience and be useful in selection decisions. However, they become more problematic when used as proxies for potential. If leadership roles have historically been less accessible to women, relying heavily on prior leadership titles as a signal of “high potential” can reinforce those gaps. A candidate may have the capability to lead without having been given the formal title.
As a result, systems that rely heavily on past advancement signals may favor candidates with more traditional career trajectories. That dynamic can disadvantage women even when gender is not explicitly included.
Assessments as a More Stable Signal
One way to reduce this issue is to rely more on structured, validated data rather than unstructured proxies for experience.
Well-designed assessments play an important role. Research on the Hogan Assessments shows they function similarly across demographic groups, with negligible differences in how they measure underlying characteristics. This means they provide a more direct measure of behavior and potential, rather than relying on career outcomes shaped by unequal access to opportunity.
Incorporating this type of data into AI systems shifts the focus from what opportunities someone has had to how they are likely to perform. This is important for identifying high potential, where traditional signals can reflect access rather than capability.
Process Design Still Matters
AI is also changing how candidates move through hiring processes, including how assessments are administered. As these tools become more widely used, organizations may increase monitoring or proctoring to maintain assessment integrity. Concerns about candidates using AI to cheat on assessments is frequently discussed, but research suggests the impact may be limited as we found little evidence of meaningful score inflation following the release of ChatGPT.
Even so, changes to assessment delivery do not affect candidates equally.
Women take on a share of caregiving responsibilities, which can limit flexibility in when and how they complete hiring requirements. Rigid testing environments may disadvantage candidates who need flexibility.
Remote proctoring offers a more balanced approach. It allows organizations to maintain assessment integrity while giving candidates control over timing and environment, ensuring that efforts to increase security do not create new barriers.
Designing Fairer AI Hiring Processes
If organizations want AI to support fairer outcomes for women, the focus needs to be on how these systems are designed and used.
Removing identifying details such as names or gender indicators helps ensure evaluations are based on job-relevant criteria. This matters because similar experiences can be interpreted differently depending on the candidate.
It is also important to be intentional about how AI tools are used. The prompts and criteria provided to large language models shape their outputs. Clear guidance helps keep the focus on relevant qualifications.
Ongoing monitoring is critical. Organizations should regularly review hiring outcomes and examine whether certain groups are being disproportionately filtered out. Running basic statistical analyses to check for adverse impact is a straightforward step.
A More Realistic View of the Future
AI will play an increasingly larger role in hiring, but its impact depends on the quality of the data and processes behind it.
For women, the implications are tied to how potential is defined. Systems that rely on past advancement signals risk reinforcing existing gaps because those signals reflect unequal access to opportunity. Systems that incorporate validated, job-relevant data and are designed with fairness in mind are better positioned to identify capability.
Bias in hiring has always existed. What is changing is the ability to examine it more systematically. Organizations that make progress will use that visibility to rethink how they evaluate talent.
