Nearly nine out of ten companies use artificial intelligence tools in employee hiring, according to 2025 World Economic Forum data. At the same time, growing evidence reveals the risk of AI tools replicating human biases from their training materials based on race, gender, age, disability and other characteristics. As the saying goes, “garbage in, garbage out.” But a new study reveals that may be only the tip of the iceberg.

AI tools can also fabricate new and factually baseless biases against groups of individuals, according to researchers out of Princeton University and the University of Chicago. Their new study, presented at the 2026 International Conference on Machine Learning, found that AI tools form group-based biases during hiring tasks even when trained on neutral data.

What’s worse, the AI tools were more likely to form new biases than humans who performed the same hiring task. And newer AI technology performed worse than older versions.

While the researchers acknowledge potential limitations of their controlled study, the findings should still raise concerns for employers that use AI in hiring decisions. AI tools “are not merely passive mirrors of human social biases, but can actively create new ones from experience,” said the researchers, “raising urgent questions about how these systems will shape societies over time.”

2026 Study Reveals AI Capacity To Invent Biases During Hiring Tasks

Prior research has identified various ways in which AI tools replicate biases that are embedded in the information on which the tools are trained.

For example, a 2025 study found that Google image searches depict women as younger than men across virtually all occupations, even though the age makeup of women and men in the U.S. workforce is similar. Because ChatGPT is trained on internet data, the AI incorporates this age-related gender bias. When asked to generate resumes for various jobs, ChatGPT systematically depicted women candidates as younger and less qualified for the job.

The researchers in the current study set out to test whether AI hiring biases could be eliminated by training AI tools on unbiased data. Can AI avoid the human tendency to form stereotypes about demographic groups?

To answer this question, the researchers used a technique for studying stereotype formation in humans. In this technique, participants are asked to make a series of hiring decisions from candidate profiles for a variety of jobs, including doctors, lawyers, janitors, and child-care aides, among others. Each candidate is identified as a member of a fictional demographic group: Tufa, Aima, Reku, or Weki.

For each job, the participant reviews a candidate from each demographic group and selects one to hire. The participant is then told whether the chosen candidate was successful or not, which can be used to help make the next hiring decision. The process is repeated over 40 hires. Participants are motivated to make thoughtful decisions by linking rewards to successful hires.

Unbeknownst to the participants, the study is designed to ensure that there are no differences between members of the demographic groups. Every candidate from every demographic group has an identical probability of succeeding at each job.

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