{"id":105618,"date":"2026-07-20T19:32:34","date_gmt":"2026-07-20T19:32:34","guid":{"rendered":"https:\/\/youzum.net\/ai-is-more-likely-than-humans-to-form-biases-when-hiring\/"},"modified":"2026-07-20T19:32:34","modified_gmt":"2026-07-20T19:32:34","slug":"ai-is-more-likely-than-humans-to-form-biases-when-hiring","status":"publish","type":"post","link":"https:\/\/youzum.net\/fr\/ai-is-more-likely-than-humans-to-form-biases-when-hiring\/","title":{"rendered":"AI is more likely than humans to form biases when hiring"},"content":{"rendered":"<div data-chronoton-summary=\"&lt;ul&gt;&lt;br&gt;&lt;li&gt;&lt;strong&gt;AI stereotypes faster than humans do:&lt;\/strong&gt; In a simulated hiring experiment, LLMs including ChatGPT, Claude, and Gemini segregated fictional job candidates by ethnic group far more aggressively than human participants\u2014scoring roughly 65% higher on a segregation scale.&lt;\/li&gt;&lt;br&gt;&lt;li&gt;&lt;strong&gt;Smarter models, stronger bias:&lt;\/strong&gt; Higher-reasoning models like OpenAI's o3 and DeepSeek's R1 showed the worst stereotyping. The same abilities that help them crack logic puzzles also makes them quick to overgeneralize from limited social data.&lt;\/li&gt;&lt;br&gt;&lt;li&gt;&lt;strong&gt;Memory makes it worse:&lt;\/strong&gt; As chatbots gain improved memory features, they risk locking in early biases\u2014but simply making them forget more isn't a solution either, since users expect personalization.&lt;\/li&gt;&lt;br&gt;&lt;li&gt;&lt;strong&gt;Goals matter more than values:&lt;\/strong&gt; Telling models to &quot;be fair&quot; barely moved the needle, but offering a bonus for diverse hiring did reduce bias\u2014suggesting the real fix lies in how AI objectives are designed, not just what they're told to care about.&lt;\/li&gt;&lt;br&gt;&lt;\/ul&gt;\" data-chronoton-post-id=\"1140655\" data-chronoton-expand-collapse=\"1\" data-chronoton-analytics-enabled=\"1\"><\/div>\n<p>The next time you apply for a job, AI may screen your r\u00e9sum\u00e9 before any human sees it. But there\u2019s good reason to question whether AI will judge you fairly. Researchers already know that LLMs pick up human biases from their training data. New research suggests that LLMs can also develop their own biases from experience\u2014and stereotype job applicants more than humans do. As AI companies race to build <a href=\"https:\/\/www.technologyreview.com\/2025\/06\/12\/1118189\/ai-agents-manus-control-autonomy-operator-openai\/\">agentic models<\/a> that <a href=\"https:\/\/www.technologyreview.com\/2026\/01\/28\/1131835\/what-ai-remembers-about-you-is-privacys-next-frontier\/\">remember<\/a> the tiniest details about users, they may be handing them ammunition for forming those biases.\u00a0<\/p>\n<p>Researchers at Princeton University and the University of Chicago ran LLMs, including ChatGPT, Claude, and Gemini, through a simulated hiring game, adapted from a <a href=\"https:\/\/bfi.uchicago.edu\/wp-content\/uploads\/2024\/12\/Bai.Costly-Exploration-Produces-Stereotypes-with-Dimensions-of-Warmth-and-Competence.pdf\">psychology study<\/a> that explored how humans can form stereotypes. Each model was told it had been hired as a consultant by the mayor of a fictional city and was then asked to help hire people for 20 jobs, including doctors, lawyers, child-care aides, and janitors. Candidates came from four fictional ethnic groups: Tufa, Aima, Reku, and Weki.\u00a0<\/p>\n<p>In each round, there was a new job opening and four candidates, one from each group. After the model hired a candidate, it learned whether they succeeded at their job and moved onto the next round. The model was told to make as many successful hires as possible over 40 rounds. Unbeknownst to the models, all candidates were equally likely to succeed at every job.<\/p>\n<p>The models quickly started segregating candidates from different groups into different jobs on the basis of early observations of hiring outcomes. For example, when a model was told an Aima had failed as a doctor, a job considered to require high levels of warmth and competence, it veered away from hiring all Aimas as doctors. Instead, it started hiring Aimas as janitors, which the model classified as being less warm and competent than doctors.\u00a0<\/p>\n<p>The models were even more likely to stereotype people by demographic group than the human participants in the original study. On the study\u2019s segregation scale, where 2 means every group has been completely confined to its own job niche, human participants scored 0.84. The models scored roughly 65% higher, with OpenAI\u2019s reasoning model o3 scoring 1.83, close to the maximum possible.<\/p>\n<p>That\u2019s because LLMs \u201creally are eager to create generalizations from limited data,\u201d says Ryan Liu, a PhD student at Princeton University and a coauthor of the study, which was published in a <a href=\"https:\/\/openreview.net\/forum?id=pc7fqaOcAH\">paper<\/a> at ICML in Seoul in July. \u201cThat\u2019s literally a lot of what they\u2019re optimized for.\u201d <\/p>\n<p>Every decision-maker, human or machine, faces a trade-off between sticking with what worked before and trying something new that might work better\u2014a phenomenon psychologists call the \u201cexploration-exploitation dilemma.\u201d It\u2019s like choosing between a new restaurant and your reliable favorite.\u00a0<\/p>\n<p>Because LLMs are trained on math, coding, and science problems\u2014tasks that reward generalizing from just a few examples\u2014they can settle on a hunch too early. And the same instinct that helps LLMs crack logic puzzles also makes them quick to stereotype. <\/p>\n<p>In the experiment, newer models with higher reasoning capabilities, such as OpenAI\u2019s o3 and DeepSeek\u2019s R1, showed even stronger biases. When LLMs rush to generalize in social settings, \u201cthat\u2019s when things tend to go wrong,\u201d says Liu. OpenAI and Anthropic did not respond to requests for comment.<\/p>\n<p>The finding is especially relevant now that chatbots are gaining improved memory and personalization features, says Angelina Wang, a computer scientist at Cornell University who did not work on the study. When a chatbot draws on its previous conversation history, it can \u201cover-index on the same kinds of behaviors it\u2019s experienced before\u201d and form biases, she says. <\/p>\n<p>Simply having chatbots remember less isn\u2019t a fix, though, because users want chatbots to remember what they say. \u201cWe still are trying to figure out just the right amount that isn\u2019t too much or too little,\u201d says Wang.<\/p>\n<p>Telling the model to be fair didn\u2019t change its behavior much. \u201cEither it can\u2019t put these values into action or that process is being submerged under the tendency to try to optimize for the goal of getting the most correct hires,\u201d says Liu. But promising the models an additional bonus for diverse hiring made them far less biased. The trick, then, is to design goals that \u201cincorporate desirable social values in order to make the large language model act in socially desirable ways,\u201d says Liu.<\/p>\n<p>The models also became less biased when they were told more personal information about individuals. In another experiment in the same study, the researchers asked the models to resettle members of different ethnic groups in cities across Canada. When the models were told personal information relevant to the ability to adapt to a new city, such as age and education, they were less likely to segregate people by their ethnicity. But when they were given irrelevant information, such as hair color and tattoo shape, the models largely fell back to sorting people by their ethnicity again.\u00a0<\/p>\n<p>To what extent AI systems will stereotype job applicants in the real world is still an open question. While the models in the experiment immediately learned whether they\u2019d made successful hires, a model screening r\u00e9sum\u00e9s in the real world doesn\u2019t get an instant report card. Companies can take a long time to find out whether a new hire is any good.\u00a0<\/p>\n\n<div>But when feedback does trickle in, a model could still read too much into those results when making future hires. As companies increasingly deploy LLMs to <a href=\"https:\/\/www.forbes.com\/sites\/courtneyconnley-hampton\/2026\/05\/08\/in-ai-age-recruiters-spend-11-seconds-a-resume-heres-what-they-notice\/\">screen r\u00e9sum\u00e9s<\/a> and even <a href=\"https:\/\/www.businessinsider.com\/ai-bot-job-interview-white-collar-work-2026-7\">conduct interviews<\/a>, the finding that models can form biases from their hiring experience \u201cis a really serious implication that they should grapple with,\u201d says Wang.\u00a0<\/div>\n<p>As LLMs learn from experience to make decisions about who gets hired, who gets a loan, or who gets parole, the biases we should worry about may include ones no human ever taught them. \u201cThese novel biases\u2014they\u2019re sort of ever present,\u201d says Liu.<\/p>","protected":false},"excerpt":{"rendered":"<p>The next time you apply for a job, AI may screen your r\u00e9sum\u00e9 before any human sees it. But there\u2019s good reason to question whether AI will judge you fairly. Researchers already know that LLMs pick up human biases from their training data. New research suggests that LLMs can also develop their own biases from experience\u2014and stereotype job applicants more than humans do. As AI companies race to build agentic models that remember the tiniest details about users, they may be handing them ammunition for forming those biases.\u00a0 Researchers at Princeton University and the University of Chicago ran LLMs, including ChatGPT, Claude, and Gemini, through a simulated hiring game, adapted from a psychology study that explored how humans can form stereotypes. Each model was told it had been hired as a consultant by the mayor of a fictional city and was then asked to help hire people for 20 jobs, including doctors, lawyers, child-care aides, and janitors. Candidates came from four fictional ethnic groups: Tufa, Aima, Reku, and Weki.\u00a0 In each round, there was a new job opening and four candidates, one from each group. After the model hired a candidate, it learned whether they succeeded at their job and moved onto the next round. The model was told to make as many successful hires as possible over 40 rounds. Unbeknownst to the models, all candidates were equally likely to succeed at every job. The models quickly started segregating candidates from different groups into different jobs on the basis of early observations of hiring outcomes. For example, when a model was told an Aima had failed as a doctor, a job considered to require high levels of warmth and competence, it veered away from hiring all Aimas as doctors. Instead, it started hiring Aimas as janitors, which the model classified as being less warm and competent than doctors.\u00a0 The models were even more likely to stereotype people by demographic group than the human participants in the original study. On the study\u2019s segregation scale, where 2 means every group has been completely confined to its own job niche, human participants scored 0.84. The models scored roughly 65% higher, with OpenAI\u2019s reasoning model o3 scoring 1.83, close to the maximum possible. That\u2019s because LLMs \u201creally are eager to create generalizations from limited data,\u201d says Ryan Liu, a PhD student at Princeton University and a coauthor of the study, which was published in a paper at ICML in Seoul in July. \u201cThat\u2019s literally a lot of what they\u2019re optimized for.\u201d Every decision-maker, human or machine, faces a trade-off between sticking with what worked before and trying something new that might work better\u2014a phenomenon psychologists call the \u201cexploration-exploitation dilemma.\u201d It\u2019s like choosing between a new restaurant and your reliable favorite.\u00a0 Because LLMs are trained on math, coding, and science problems\u2014tasks that reward generalizing from just a few examples\u2014they can settle on a hunch too early. And the same instinct that helps LLMs crack logic puzzles also makes them quick to stereotype. In the experiment, newer models with higher reasoning capabilities, such as OpenAI\u2019s o3 and DeepSeek\u2019s R1, showed even stronger biases. When LLMs rush to generalize in social settings, \u201cthat\u2019s when things tend to go wrong,\u201d says Liu. OpenAI and Anthropic did not respond to requests for comment. The finding is especially relevant now that chatbots are gaining improved memory and personalization features, says Angelina Wang, a computer scientist at Cornell University who did not work on the study. When a chatbot draws on its previous conversation history, it can \u201cover-index on the same kinds of behaviors it\u2019s experienced before\u201d and form biases, she says. Simply having chatbots remember less isn\u2019t a fix, though, because users want chatbots to remember what they say. \u201cWe still are trying to figure out just the right amount that isn\u2019t too much or too little,\u201d says Wang. Telling the model to be fair didn\u2019t change its behavior much. \u201cEither it can\u2019t put these values into action or that process is being submerged under the tendency to try to optimize for the goal of getting the most correct hires,\u201d says Liu. But promising the models an additional bonus for diverse hiring made them far less biased. The trick, then, is to design goals that \u201cincorporate desirable social values in order to make the large language model act in socially desirable ways,\u201d says Liu. The models also became less biased when they were told more personal information about individuals. In another experiment in the same study, the researchers asked the models to resettle members of different ethnic groups in cities across Canada. When the models were told personal information relevant to the ability to adapt to a new city, such as age and education, they were less likely to segregate people by their ethnicity. But when they were given irrelevant information, such as hair color and tattoo shape, the models largely fell back to sorting people by their ethnicity again.\u00a0 To what extent AI systems will stereotype job applicants in the real world is still an open question. While the models in the experiment immediately learned whether they\u2019d made successful hires, a model screening r\u00e9sum\u00e9s in the real world doesn\u2019t get an instant report card. Companies can take a long time to find out whether a new hire is any good.\u00a0 But when feedback does trickle in, a model could still read too much into those results when making future hires. As companies increasingly deploy LLMs to screen r\u00e9sum\u00e9s and even conduct interviews, the finding that models can form biases from their hiring experience \u201cis a really serious implication that they should grapple with,\u201d says Wang.\u00a0 As LLMs learn from experience to make decisions about who gets hired, who gets a loan, or who gets parole, the biases we should worry about may include ones no human ever taught them. \u201cThese novel biases\u2014they\u2019re sort of ever present,\u201d says Liu.<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_pvb_checkbox_block_on_post":false,"footnotes":""},"categories":[52,5,7,1],"tags":[],"class_list":["post-105618","post","type-post","status-publish","format-standard","hentry","category-ai-club","category-committee","category-news","category-uncategorized","pmpro-has-access"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.3 - 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But there\u2019s good reason to question whether AI will judge you fairly. Researchers already know that LLMs pick up human biases from their training data. New research suggests that LLMs can also develop their own biases from\u2026","_links":{"self":[{"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/posts\/105618","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/comments?post=105618"}],"version-history":[{"count":0,"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/posts\/105618\/revisions"}],"wp:attachment":[{"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/media?parent=105618"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/categories?post=105618"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/youzum.net\/fr\/wp-json\/wp\/v2\/tags?post=105618"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}