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PHOENIX (AZFamily) — Companies are increasingly letting artificial intelligence take the first look at job applicants. New research from Princeton University and the University of Chicago suggests those systems can develop their own prejudices even faster than people do.
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Researchers have already shown that AI models can absorb human bias from their training data. This study points to something different: models inventing bias from scratch, from their own limited experience.
To test it, the researchers built a virtual hiring game set in a fictional city, adapting a study paradigm from psychology research. The paper was presented at the International Conference on Machine Learning in Seoul earlier this month.
“It’s a multi-round hiring game where you have around 40 jobs,” said Ryan Liu, a Princeton PhD student and one of the study’s authors. “Each round, there’s one job presented and the model needs to choose between four different candidates, each from one of the demographics available.”
The roles ranged from doctors and lawyers to childcare aides and janitors. After each hire, the model learned whether the candidate succeeded, then moved on. It didn’t take long for the models to start sorting people by their fictional ethnicities.
“Almost immediately,” Liu said. “Once they see like one single failure example — for example, demographic A performs badly when assigned to a doctor — that demographic job pairing is almost never assigned again.”
The effect spread. A single failure in one role also pushed the model away from assigning that group to similar jobs, like teachers and professors. Humans played the same game, and the models showed more bias than they did.
The researchers ran ChatGPT, Claude and Gemini through the simulated hiring game, and the models scored about 1.83 on a segregation scale compared to 0.84 for humans.
Liu said the reason has to do with how these systems are built. They’re designed to draw conclusions from very few examples.
“Normally that’s fine. It even helps them for logic puzzles and math,” he said. “But it ends up doing really badly when this sort of hiring game or any socially salient type of task comes up.”
That also explains a counterintuitive finding: the more capable reasoning models performed worse, not better.
Only one fix worked
The team tried more than 10 interventions. They tried telling the model to reason more, adjusting how randomly it responded, reframing the task, even instructing it directly to be unbiased. None stuck.
The only thing that worked was changing the model’s incentives. They added a diversity incentive to the model’s assignments.
“After that, the model is very fair to all the different demographics and no longer creates a stratified society,” Liu said. “Anything less than that, these models stick to their existing behavior.”
Liu cautioned that the study doesn’t perfectly capture the dynamics of hiring in the real world. Real employers can evaluate more metrics than the study’s models were given, and companies rarely learn quickly whether a hire worked out. That means the feedback loop that produced the AI bias runs much slower in real life.
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In Arizona, AI interviews are already routine
Companies aren’t just using AI to screen résumés. Coinbase screens applicants through conversations with an AI voice, and Zapier uses AI avatars to interview candidates. Zapier says the technology lets it interview up to five times as many applicants, and the hiring platform Greenhouse reported 63% of job seekers had been interviewed by an AI as of last year.
Travis Laird, a Phoenix-area jobs expert with Robert Half, says that tracks with what he sees locally. He compares avatar interviews to the pre-screening questionnaires most applicants already fill out.
“It gives you a chance to get some face time, albeit with an avatar, to show your personality, give some context, give some story,” Laird said.
Some critics worry an AI might judge applicants on speech patterns or facial expressions unrelated to the job. Laird’s response: human recruiters carry biases too, and companies have used screening software for decades. The difference now, he said, is that employers can ask the system to explain itself.
“If you’re challenging AI as to why it’s not moving forward with certain candidates, I think you’ll find some really good positive answers,” he said.
Volume is driving adoption. Laird said employers commonly post a job Friday and find 300-plus applicants waiting by Monday. But AI cuts both ways. Robert Half research found 65% of managers say AI-generated résumés are making hiring harder, 84% say AI has increased recruiters’ workload and 67% say it has slowed hiring down.
A separate Resume Genius survey found 87% of hiring managers use AI in at least one stage of recruitment, most commonly résumé screening.
His advice to employers: treat the tool like a new team member. “You’ve got to give it good coaching as to what you’re looking for. Give it feedback, review its work,” he said. But no matter how much gets automated, a person still has to sit across from the candidate. “That’s going to be the true litmus test.”
For job seekers, he recommends using AI to critique your résumé and surface missing keywords while keeping every claim accurate.
“I can tailor my resume for a certain position, but the experience listed still needs to be accurate,” Laird said. “Because if not, it’s going to get found out throughout the process.”
He also cautions against automating mass applications. Coinbase alone said it receives some 1.7 million applications a year.
“Be selective with the companies you’re applying to,” Laird said. “If you’re going to use some type of automation for applying, you’ve got to use guardrails.”
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