Participants were given a job description and the names and resumes of five candidates: two white men; two men who were either Asian, Black or Hispanic; and one candidate whose resume lacked qualifications for the job, to obscure the purpose of the study. An example from the study is shown here. Credit: Wilson et al./AIES ‘25

An organization drafts a job listing with artificial intelligence. Droves of applicants conjure résumés and cover letters with chatbots. Another AI system sifts through those applications, passing recommendations to hiring managers. Perhaps AI avatars conduct screening interviews. This is increasingly the state of hiring, as people seek to streamline the stressful, tedious process with AI.

Yet research is finding that hiring bias—against people with disabilities, or certain races and genders—permeates large language models, or LLMs, such as ChatGPT and Gemini. We know less, though, about how biased LLM recommendations influence the people making hiring decisions.

In a new University of Washington study, 528 people worked with simulated LLMs to pick candidates for 16 different jobs, from computer systems analyst to nurse practitioner to housekeeper. The researchers simulated different levels of racial biases in LLM recommendations for résumés from equally qualified white, Black, Hispanic and Asian men.

When picking candidates without AI or with neutral AI, participants picked white and non-white applicants at equal rates. But when they worked with a moderately biased AI, if the AI preferred non-white candidates, participants did too. If it preferred white candidates, participants did too. In cases of severe bias, people made only slightly less biased decisions than the recommendations.

The team presented its findings Oct. 22 at the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society in Madrid.

“In one survey, 80% of organizations using AI hiring tools said they don’t reject applicants without human review,” said lead author Kyra Wilson, a UW doctoral student in the Information School. “So this human-AI interaction is the dominant model right now. Our goal was to take a critical look at this model and see how human reviewers’ decisions are being affected. Our findings were stark: Unless bias is obvious, people were perfectly willing to accept the AI’s biases.”

The team recruited 528 online participants from the U.S. through surveying platform Prolific, who were then asked to screen job applicants. They were given a job description and the names and résumés of five candidates: two white men and two men who were either Asian, Black or Hispanic. These four were equally qualified.

To obscure the purpose of the study, the final candidate was of a race not being compared and lacked qualifications for the job. Candidates’ names implied their races—for example, Gary O’Brien for a white candidate. Affinity groups, such as Asian Student Union Treasurer, also signaled race.

In four trials, the participants picked three of the five candidates to interview. In the first trial, the AI provided no recommendation. In the next trials, the AI recommendations were neutral (one candidate of each race), severely biased (candidates from only one race), or moderately biased, meaning candidates were recommended at rates similar to rates of bias in real AI models. The team derived rates of moderate bias using the same methods as in their 2024 study that looked at bias in three common AI systems.

Rather than having participants interact directly with the AI system, the team simulated the AI interactions so they could hew to rates of bias from their large-scale study. Researchers also used AI generated résumés, rather than real résumés, which they validated. This allowed greater control, and AI-written résumés are increasingly common in hiring.

“Getting access to real-world hiring data is almost impossible, given the sensitivity and privacy concerns,” said senior author Aylin Caliskan, a UW associate professor in the Information School. “But this lab experiment allowed us to carefully control the study and learn new things about bias in human-AI interaction.”

Without suggestions, participants’ choices exhibited little bias. But when provided with recommendations, participants mirrored the AI. In the case of severe bias, choices followed the AI picks around 90% of the time, rather than nearly all the time, indicating that even if people are able to recognize AI bias, that awareness isn’t strong enough to negate it.

“There is a bright side here,” Wilson said. “If we can tune these models appropriately, then it’s more likely that people are going to make unbiased decisions themselves. Our work highlights a few possible paths forward.”

In the study, bias dropped 13% when participants began with an implicit association test, intended to detect subconscious bias. So companies including such tests in hiring trainings may mitigate biases. Educating people about AI can also improve awareness of its limitations.

“People have agency, and that has huge impact and consequences, and we shouldn’t lose our critical thinking abilities when interacting with AI,” Caliskan said. “But I don’t want to place all the responsibility on people using AI. The scientists building these systems know the risks and need to work to reduce systems’ biases. And we need policy, obviously, so that models can be aligned with societal and organizational values.”

The paper is published in the Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society.

More information: Kyra Wilson et al, No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Human Autonomy, Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (2025). DOI: 10.1609/aies.v8i3.36749

Citation: AI bias in hiring decisions is often copied by human reviewers, study reveals (2025, November 10) retrieved 10 November 2025 from https://phys.org/news/2025-11-people-mirror-ai-hiring-biases.html

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.