{"id":34435,"date":"2026-06-24T07:48:20","date_gmt":"2026-06-24T11:48:20","guid":{"rendered":"https:\/\/www.wellable.co\/blog\/?p=34435"},"modified":"2026-06-24T15:04:09","modified_gmt":"2026-06-24T19:04:09","slug":"ai-hiring-bias-algorithmic-monoculture-uber-hr-layoffs","status":"publish","type":"post","link":"https:\/\/www.wellable.co\/blog\/ai-hiring-bias-algorithmic-monoculture-uber-hr-layoffs\/","title":{"rendered":"The (Not So) Hidden Bias in AI Hiring Tools"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">In this week&#8217;s episode, Nick and Geoff&nbsp;dig&nbsp;into a&nbsp;<a href=\"https:\/\/hai.stanford.edu\/news\/ai-hiring-tools-can-yield-racial-bias-and-systemic-rejection\" target=\"_blank\" rel=\"noreferrer noopener\">landmark Stanford study<\/a>, which revealed that the AI tools many employers use to screen job applicants are introducing racial bias and locking the same candidates out of opportunities across multiple companies. They also discuss&nbsp;<a href=\"https:\/\/finance.yahoo.com\/markets\/stocks\/articles\/uber-reportedly-slashes-many-senior-154627999.html\" target=\"_blank\" rel=\"noreferrer noopener\">Uber&#8217;s announcement that it&#8217;s cutting 23% of its HR workforce<\/a>. While the company claims AI has nothing to do with it, it may be hard to take at face value.<\/p>\n\n\n\n<div class=\"video-embed\" style=\"position:relative;padding-bottom:56.25%;height:0;\">\n<iframe src=\"https:\/\/www.youtube.com\/embed\/RGaCFEg7ULU?si=KJUd9KkDZOc1k4k9\" title=\"The (Not So) Hidden Bias in AI Hiring Tools\" style=\"position:absolute;width:100%;height:100%;top:0;left:0;\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen=\"\">\n  <\/iframe>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<div class=\"row justify-content-between\">\n<div class=\"cs-btn-light text-center mb-4 col-12 col-md-6 pr-md-4\">\n  <a class=\"cs-button d-flex align-items-center justify-content-center w-100\" href=\"https:\/\/podcasts.apple.com\/us\/podcast\/the-not-so-hidden-bias-in-ai-hiring-tools\/id1869414001?i=1000774003929\" target=\"_blank\" style=\"gap: 8px\">\n\n<img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/www.wellable.co\/blog\/wp-content\/uploads\/2026\/03\/Apple-Podcasts-logo.png?w=640&#038;ssl=1\" alt=\"Apple podcast\" loading=\"lazy\" class=\"h-auto\" style=\"width: 24px\">\n\n<span style=\"font-size: 20px\">Listen on Apple Podcasts<\/span>\n<\/a>\n<\/div>\n\n<div class=\"cs-btn-light text-center mb-4 col-12 col-md-6 pl-md-4\">\n  <a class=\"cs-button d-flex align-items-center justify-content-center gap-3 w-100\" href=\"https:\/\/open.spotify.com\/episode\/5tQoyajcecdFjlfI9ZtkM9?si=tMCoz8pTR2-jw_iZZPSHPA\" target=\"_blank\" style=\"gap: 8px\">\n\n<img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/www.wellable.co\/blog\/wp-content\/uploads\/2026\/03\/Spotify_White_Logo.png?w=640&#038;ssl=1\" alt=\"Apple podcast\" loading=\"lazy\" class=\"h-auto pr\" style=\"width: 24px\">\n<span style=\"font-size: 20px\">Listen on Spotify<\/span>\n\n<\/a>\n<\/div>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n<div style=\"border: 1px solid rgb(0 0 0 \/ 0.1); padding: 25px 25px 10px; border-radius: 8px; box-shadow: 0 4px 6px -1px rgb(0 0 0 \/ 0.1), 0 2px 4px -2px rgb(0 0 0 \/ 0.1);\">\n<h3 id=\"h-pressed-for-time-here-s-a-quick-summary\" class=\"wp-block-heading nitoc\">Short on time? Here are the key takeaways:<\/h3>\n<ul>\n<li><span class=\"TextRun SCXW102080281 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW102080281 BCX0\">90% of US employers use AI screening tools to rank applicants, and most rely on the same few third-party models, creating what researchers call &#8220;algorithmic monoculture,&#8221; where a single rejection can cascade into rejection everywhere<\/span><\/span><\/li>\n<li><span class=\"TextRun SCXW87007670 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW87007670 BCX0\">A Stanford HAI study covering 3.4 million applicants and 4 million job applications found AI hiring tools are producing significant racial bias and correlated rejections across employers using the same model<\/span><\/span><\/li>\n<li><span class=\"TextRun SCXW143294360 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW143294360 BCX0\">AI hiring tools differ from human recruiters in four critical ways: pervasive adoption across employers, persistent memory across applications, high-stakes consequences, and opacity\u2014properties that create systemic risks candidates cannot see or contest<\/span><\/span><\/li>\n<li><span class=\"TextRun SCXW189241119 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW189241119 BCX0\">Application volumes have tripled as AI makes it easier than ever to apply for jobs, creating a capacity crisis that pushes HR teams toward AI screening even as the risks\u00a0<\/span><span class=\"NormalTextRun SCXW189241119 BCX0\">emerge<\/span><\/span><\/li>\n<li><span class=\"TextRun SCXW127842987 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW127842987 BCX0\">Uber is cutting 23% of its HR team while denying it is AI-driven, but the timeline\u2014aggressive AI spending, a blown Q1 AI budget, and an admission of unclear ROI\u2014makes that explanation difficult to accept<\/span><\/span><\/li>\n<\/ul>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"episode-summary\">Episode Summary<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"the-stanford-study-that-should-change-how-you-think-about-ai-hiring\">The Stanford Study That Should Change How You Think About AI Hiring<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/algorithmichiring.github.io\/\" target=\"_blank\" rel=\"noreferrer noopener\">Ninety percent of US employers<\/a>&nbsp;now use&nbsp;<a href=\"https:\/\/www.wellable.co\/blog\/top-hr-ai-tools\/#h-ai-tools-for-hiring-recruitment\" target=\"_blank\" rel=\"noreferrer noopener\">AI tools<\/a>&nbsp;to screen and rank job applicants. The more consequential detail is what follows from it:&nbsp;the majority of&nbsp;those employers are using the same small number of third-party AI models, which means the hiring decisions of thousands of companies are running through the same algorithmic infrastructure.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The&nbsp;<a href=\"https:\/\/hai.stanford.edu\/news\/ai-hiring-tools-can-yield-racial-bias-and-systemic-rejection\" target=\"_blank\" rel=\"noreferrer noopener\">Stanford HAI study<\/a>&nbsp;that Nick and Geoff&nbsp;discuss&nbsp;is the first large-scale empirical examination of what that infrastructure is&nbsp;actually doing. The numbers are significant: 3.4 million applicants, 4 million job applications, 1,700 job postings, 150 employers across 11 industries. What the study found is that&nbsp;a&nbsp;major problem&nbsp;is&nbsp;embedded in how these tools work.&nbsp;The article&nbsp;coins&nbsp;the term \u201calgorithmic monoculture\u201d which gives way to&nbsp;a significant&nbsp;<a href=\"https:\/\/www.wellable.co\/blog\/how-to-use-chatgpt-in-hr-examples\/#h-3-bias\" target=\"_blank\" rel=\"noreferrer noopener\">increase in racial bias<\/a>&nbsp;relative&nbsp;to human screening.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The term comes from agriculture. A monoculture farm&nbsp;plants&nbsp;a single crop strain. When every farm does this, one disease can wipe out the entire supply because there is no diversity&nbsp;to absorb&nbsp;the shock. Applied to hiring, the logic is identical. When every employer uses the same AI model, a rejection at company A is not an independent event from a rejection at company B. The same algorithm, seeing the same profile,&nbsp;can&nbsp;makes&nbsp;the same call&nbsp;regardless of a completely new application or role. A candidate who gets screened out for one position can find themselves locked out across&nbsp;many other&nbsp;companies&nbsp;running that model, even if their skills are a strong fit for several of those roles. The employer loses&nbsp;a good candidate&nbsp;they&nbsp;never&nbsp;considered. The candidate never gets a chance to be considered.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"why-ai-hiring-tools-are-structurally-different-from-human-recruiters\">Why AI Hiring Tools Are Structurally Different from Human Recruiters<\/h3>\n\n\n<div class=\"wp-block-image center-mobile\">\n<figure class=\"alignright size-full is-resized\"><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" width=\"566\" height=\"435\" src=\"https:\/\/i0.wp.com\/www.wellable.co\/blog\/wp-content\/uploads\/2026\/06\/AI-In-HR-2024-Transforming-Talent-With-Technology-1-edited.png?resize=566%2C435&#038;ssl=1\" alt=\"A cartoon illustration of two people interacting with a large AI robot emerging from a smartphone screen, with one person working on a laptop and another holding a tablet, representing the use of AI-powered tools in a digital hiring or recruitment process.\" class=\"wp-image-34443\" style=\"aspect-ratio:1.3011524244400956;width:401px;height:auto\" srcset=\"https:\/\/i0.wp.com\/www.wellable.co\/blog\/wp-content\/uploads\/2026\/06\/AI-In-HR-2024-Transforming-Talent-With-Technology-1-edited.png?w=566&amp;ssl=1 566w, https:\/\/i0.wp.com\/www.wellable.co\/blog\/wp-content\/uploads\/2026\/06\/AI-In-HR-2024-Transforming-Talent-With-Technology-1-edited.png?resize=300%2C231&amp;ssl=1 300w\" sizes=\"(max-width: 566px) 100vw, 566px\" \/><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Nick draws a distinction that is worth sitting with. In a world of human recruiters, a rejection at company A is genuinely independent from a rejection at company B. The recruiters are different people, evaluating the same resume with different contexts, different hiring mandates, and different moments in their own thinking. Even if they reach the same conclusion, they reach it separately.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI tools break that independence. Nick&nbsp;identifies&nbsp;four properties that make AI hiring models structurally different from the human process they are replacing. First, pervasive adoption:&nbsp;when the same model is used across hundreds of employers, it&nbsp;encounters&nbsp;the same applicant multiple times in ways no human recruiter ever would. Second, persistent memory: unlike a recruiter who reviews a resume fresh, the model carries data about&nbsp;previous&nbsp;interactions with that profile. Third, high-stakes consequences: a rejection from an AI screening tool is not just a lost opportunity at one company but potentially a correlated rejection across an entire market. Fourth, opacity: candidates have no visibility into why they were rejected or how the model&nbsp;weighted&nbsp;their profile.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">None of those properties individually would disqualify AI from a role in hiring. But in combination, they create a system that is fundamentally different from human-led recruiting in ways that are not obvious to employers or applicants, and that are actively harmful in the ways the Stanford study documents.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"ubers-hr-cuts-and-the-question-nobody-is-asking\">Uber&#8217;s HR Cuts and the Question Nobody Is Asking<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The second story is&nbsp;<a href=\"https:\/\/finance.yahoo.com\/markets\/stocks\/articles\/uber-reportedly-slashes-many-senior-154627999.html\" target=\"_blank\" rel=\"noreferrer noopener\">Uber&#8217;s announcement that it is cutting 23% of its HR workforce<\/a>. Uber&#8217;s CEO&nbsp;stated&nbsp;explicitly in an internal memo that the changes are not AI-driven, describing them instead as necessary to maximize the effectiveness of the&nbsp;people&nbsp;team. Nick and Geoff are skeptical.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The context makes the denial hard to accept. In the weeks leading up to the layoff announcement, Uber had been prominently in the news for three separate AI-related stories: a public commitment to using AI to optimize the business, a report that the&nbsp;<a href=\"https:\/\/finance.yahoo.com\/sectors\/technology\/articles\/uber-burned-entire-2026-ai-180347400.html\" target=\"_blank\" rel=\"noreferrer noopener\">company blew through its entire AI budget<\/a>&nbsp;in the first three months of the year, and a CTO statement acknowledging the company&nbsp;<a href=\"https:\/\/www.aibusinessreview.org\/2026\/05\/26\/uber-ai-spending-roi-questions\/\" target=\"_blank\" rel=\"noreferrer noopener\">does not yet have a clear or demonstrable ROI<\/a>&nbsp;from its AI spend. The sequence paints a picture of a company that made aggressive AI investments, could not show results, and is now cutting costs, with HR, a function that has a middle-management-adjacent structure and is therefore a common target in AI-era restructuring, absorbing a significant share of those cuts.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Whether Uber&#8217;s HR cuts are technically AI-driven or not, they are happening in an environment where the public narrative around AI has made cutting people-facing functions easier to justify. The more useful question is not what Uber&nbsp;calls&nbsp;the layoff but whether the core HR functions are still going to be owned and performed by someone. Nick&#8217;s analogy to wellness programs is apt: the question is not whether you have a dedicated wellness coordinator but whether someone owns the program. The same logic applies to HR. The title and reporting structure can change. The work cannot disappear.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"frequently-asked-questions\">Frequently Asked Questions<\/h2>\n\n\n\n    <section class=\"faq-section\">\n      <div class=\"faq-accordion\">\n\n        \n        <div class=\"faq-item card card-faq\">\n          <button \n            class=\"faq-question\" \n            data-target=\"faq_1\"\n            type=\"button\"\n          >\n            What did the Stanford HAI study find about AI hiring tools?            <span class=\"icon\"><\/span>\n          <\/button>\n\n          <div id=\"faq_1\" class=\"faq-answer\">\n            <p><span class=\"TextRun SCXW249649374 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW249649374 BCX0\">The Stanford Human-Centered AI study examined 3.4 million applicants, 4 million job applications, 1,700 job postings, 150 employers, and 11 industries, making it the first large-scale empirical study of AI hiring. It found two major problems: a significant increase in racial bias relative to human screening, and a pattern of correlated rejections across employers using the same model, meaning candidates screened out by one company&#8217;s AI tool were systematically more likely to be rejected by others using the same system.<\/span><\/span><\/p>\n          <\/div>\n        <\/div>\n\n        \n        <div class=\"faq-item card card-faq\">\n          <button \n            class=\"faq-question\" \n            data-target=\"faq_2\"\n            type=\"button\"\n          >\n            What is algorithmic monoculture and why does it matter for hiring?            <span class=\"icon\"><\/span>\n          <\/button>\n\n          <div id=\"faq_2\" class=\"faq-answer\">\n            <p><span class=\"NormalTextRun SCXW77179889 BCX0\">Algorithmic monoculture describes what happens when most employers use the same small number of AI models to screen applicants. Borrowed from agriculture, where planting a single crop strain creates vulnerability to a single disease, the concept applies directly to\u00a0<\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW77179889 BCX0\">hiring:<\/span><span class=\"NormalTextRun SCXW77179889 BCX0\">\u00a0when the same model sees the same resume across multiple employers, a rejection\u00a0<\/span><span class=\"NormalTextRun SCXW77179889 BCX0\">at<\/span><span class=\"NormalTextRun SCXW77179889 BCX0\">\u00a0one company is no longer independent of rejections elsewhere. A candidate who is a strong fit for several roles can be locked out of all of them because a single algorithm does not like their profile.<\/span><\/p>\n          <\/div>\n        <\/div>\n\n        \n        <div class=\"faq-item card card-faq\">\n          <button \n            class=\"faq-question\" \n            data-target=\"faq_3\"\n            type=\"button\"\n          >\n            How has AI changed the volume of job applications, and what does that mean for HR?            <span class=\"icon\"><\/span>\n          <\/button>\n\n          <div id=\"faq_3\" class=\"faq-answer\">\n            <p><span class=\"TextRun SCXW5551526 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW5551526 BCX0\">AI tools have made it dramatically easier and faster for job seekers to apply for positions, including generating customized resumes and cover letters at scale.\u00a0<\/span><\/span><a class=\"Hyperlink SCXW5551526 BCX0\" href=\"https:\/\/seramount.com\/articles\/high-application-volume-is-breaking-early-talent-hiring-why-its-happening-and-what-to-do-about-it\/\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"TextRun Underlined SCXW5551526 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW5551526 BCX0\" data-ccp-charstyle=\"Hyperlink\">Application volumes at many companies have tripled<\/span><\/span><\/a><span class=\"TextRun SCXW5551526 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW5551526 BCX0\">\u00a0<\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW5551526 BCX0\">as<\/span><span class=\"NormalTextRun SCXW5551526 BCX0\">\u00a0a result. That volume surge is one of the primary reasons employers are turning to AI screening tools, creating a cycle where AI-generated applications are being evaluated by AI screening models,\u00a0<\/span><span class=\"NormalTextRun SCXW5551526 BCX0\">largely without<\/span><span class=\"NormalTextRun SCXW5551526 BCX0\">\u00a0human review at the\u00a0<\/span><span class=\"NormalTextRun SCXW5551526 BCX0\">initial<\/span><span class=\"NormalTextRun SCXW5551526 BCX0\">\u00a0stage.<\/span><\/span><\/p>\n          <\/div>\n        <\/div>\n\n        \n        <div class=\"faq-item card card-faq\">\n          <button \n            class=\"faq-question\" \n            data-target=\"faq_4\"\n            type=\"button\"\n          >\n            Is Uber's HR layoff AI-driven or not?            <span class=\"icon\"><\/span>\n          <\/button>\n\n          <div id=\"faq_4\" class=\"faq-answer\">\n            <p><span class=\"NormalTextRun SCXW191184591 BCX0\">Uber&#8217;s CEO\u00a0<\/span><span class=\"NormalTextRun SCXW191184591 BCX0\">stated<\/span><span class=\"NormalTextRun SCXW191184591 BCX0\">\u00a0explicitly that the 23% HR workforce reduction is not AI-driven. Nick and Geoff take that claim with skepticism, noting that in the weeks before the announcement, Uber had been in the news for aggressive AI spending, a blown AI budget in Q1, and a CTO admission of unclear AI ROI. Whether or not the cuts are formally attributed to AI, they fit a pattern of companies using the favorable AI narrative to restructure functions that have a middle-management-adjacent profile.<\/span><\/p>\n          <\/div>\n        <\/div>\n\n        \n        <div class=\"faq-item card card-faq\">\n          <button \n            class=\"faq-question\" \n            data-target=\"faq_5\"\n            type=\"button\"\n          >\n            What should HR leaders do in response to AI hiring bias research?            <span class=\"icon\"><\/span>\n          <\/button>\n\n          <div id=\"faq_5\" class=\"faq-answer\">\n            <p><span class=\"NormalTextRun SCXW63911708 BCX0\">Nick and Geoff suggest that the\u00a0<\/span><span class=\"NormalTextRun SCXW63911708 BCX0\">most likely near-term<\/span><span class=\"NormalTextRun SCXW63911708 BCX0\">\u00a0response from HR teams is not to abandon AI screening tools but to work with vendors on configuration, bias auditing, and transparency. The volume problem that drives AI adoption is not going away. The more durable solution requires pressure on AI vendors to allow employer-level configuration, provide explainability for screening decisions, and\u00a0<\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW63911708 BCX0\">build in<\/span><span class=\"NormalTextRun SCXW63911708 BCX0\">\u00a0safeguards against correlated rejection patterns.<\/span><\/p>\n          <\/div>\n        <\/div>\n\n        \n        <div class=\"faq-item card card-faq\">\n          <button \n            class=\"faq-question\" \n            data-target=\"faq_6\"\n            type=\"button\"\n          >\n            Do companies still need dedicated HR functions?            <span class=\"icon\"><\/span>\n          <\/button>\n\n          <div id=\"faq_6\" class=\"faq-answer\">\n            <p><span class=\"NormalTextRun SCXW241896755 BCX0\">Yes, in the sense that the underlying work HR performs, payroll, compliance, benefits administration, employee relations, culture maintenance,\u00a0<\/span><span class=\"NormalTextRun AdvancedProofingIssueV2Themed SCXW241896755 BCX0\">has to<\/span><span class=\"NormalTextRun SCXW241896755 BCX0\">\u00a0be owned by someone regardless of how the function is structured or titled. The question is not whether to have\u00a0<\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW241896755 BCX0\">HR<\/span><span class=\"NormalTextRun SCXW241896755 BCX0\">\u00a0but how to ensure those responsibilities are clearly assigned and resourced. Nick&#8217;s analogy to wellness programs applies: a wellness program without a clear internal owner will fail, not because the software is bad but because no one is accountable for making it work.<\/span><\/p>\n          <\/div>\n        <\/div>\n\n        \n      <\/div>\n    <\/section>\n\n    \n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"full-episode-transcript\"><strong style=\"color: transparent; visibility: hidden; opacity: 0;\">Full Episode Transcript<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n    <section class=\"faq-section toc-helper-accordion\">\n      <div class=\"faq-accordion\">\n\n        \n        <div class=\"custom-accordion-item\">\n          <button \n            class=\"faq-question\" \n            data-target=\"transcript_1\"\n            type=\"button\"\n          >\n            <h2 id=\"full-episode-transcript\"><strong>Full Episode Transcript<\/strong><\/h2>\n            <span class=\"icon\"><\/span>\n          <\/button>\n\n          <div id=\"transcript_1\" class=\"faq-answer\">\n            <p><b><span data-contrast=\"auto\">Nick:<\/span><\/b><span data-contrast=\"auto\">\u00a0Welcome to the\u00a0Wellable\u00a0Weekly Podcast, where we talk about key topics and trends at the intersection of well-being, technology, and HR.\u00a0I&#8217;m\u00a0Nick, along with my good friend and colleague Geoff.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">All right, hopping into it \u2014 another AI article to kick us off.\u00a0What&#8217;s good about this is that it&#8217;s not just an article, it&#8217;s\u00a0actually a\u00a0study, which I find\u00a0really interesting.\u00a0It&#8217;s\u00a0from the Stanford University Human-Centered AI group, Stanford HAI. To\u00a0set\u00a0the foundation: a big part of our audience is involved in recruitment and retention, and an interesting stat from this study was that 90% of US employers use AI screening tools to sort and rank job seekers. That blew my mind. I know AI is\u00a0really popular, and I always feel like as a\u00a0company\u00a0we&#8217;re\u00a0fairly ahead\u00a0of the times. We\u00a0don&#8217;t\u00a0do that.\u00a0So\u00a0I saw 90% and it was a curveball. I\u00a0don&#8217;t\u00a0understand how 90% of companies do anything \u2014\u00a0that&#8217;s\u00a0a ton of alignment.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">What&#8217;s probably more interesting,\u00a0going\u00a0a layer deeper, is that\u00a0the majority of\u00a0those companies are working with the same few third-party models and vendors. So as an applicant, if I apply\u00a0to\u00a010\u00a0jobs, and\u00a0I can now generate customized resumes and cover letters using AI \u2014 so I&#8217;m applying to more jobs than ever \u2014 and 5 of those 10 companies are using the same AI model, that model has now seen my resume five different times. Because it sees it multiple times,\u00a0it&#8217;s\u00a0using some of that prior data to\u00a0render\u00a0an opinion about me for each new position. Whereas in the real world without AI,\u00a0you&#8217;d\u00a0have five HR people at five different companies reviewing my\u00a0resume each\u00a0independently.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The study covered\u00a03.4 million people\u00a0who\u00a0submitted\u00a04 million job applications, across 1,700 job postings, 150 employers, and 11 industries.\u00a0It&#8217;s\u00a0the first large-scale study of AI hiring, which makes it especially compelling. It found two\u00a0major issues: a significant increase in racial bias, and a tendency to shut the same people out of jobs everywhere they apply. If\u00a0you&#8217;re\u00a0rejected from the first position, that rejection plays a factor in future\u00a0applications to companies using the same model, and you can get effectively locked out even if\u00a0you&#8217;re\u00a0a strong fit.\u00a0That&#8217;s\u00a0bad for employers who miss good candidates and bad for employees who never get the chance to interview.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Geoff:<\/span><\/b><span data-contrast=\"auto\">\u00a0A few interesting stats worth unpacking.\u00a0The applications\u00a0across 1,700 job postings \u2014\u00a0that&#8217;s\u00a0over 2,000 applications per job posting.\u00a0That&#8217;s\u00a0actually not\u00a0that uncommon for larger companies that are appealing to new graduates looking for secure work with compelling benefits and pay.\u00a0Because of that volume, and the ease with which people can apply now,\u00a0it&#8217;s\u00a0created a perfect storm of increased applications.\u00a0In order to\u00a0handle that volume, organizations are resorting to tools powered by a select few AI models.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The term the article coins to describe this effect is algorithmic monoculture.\u00a0That term\u00a0actually comes\u00a0from farming.\u00a0The basic premise is that if every farm plants the same single crop strain, one disease could wipe everything out because\u00a0there&#8217;s\u00a0no diversity. The same logic applies here. When every employer uses the same AI vendor for screening, rejection decisions become correlated.\u00a0Normally, applying to company A and company B should produce independent decisions.\u00a0But if\u00a0they&#8217;re\u00a0running the same algorithm and that algorithm\u00a0doesn&#8217;t\u00a0like your profile, they can reject you for the exact same reason across the board. Most applicants\u00a0aren&#8217;t\u00a0even aware this could\u00a0be happening\u00a0when they\u00a0submit\u00a0their applications.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Nick:<\/span><\/b><span data-contrast=\"auto\">\u00a0Just one detail about the study worth noting: all the applications in this study were processed through the same AI hiring tool, so they could confirm every application was going through the same algorithmic model. That makes the findings cleaner.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">I always think about the concept of AGI \u2014 artificial general intelligence \u2014 which refers to AI that can be\u00a0intelligent\u00a0the way a human is. In an ideal world, an AI hiring tool would work like a recruiter who reviews each application independently. But a recruiter at company A is looking at a resume for the first time, and a recruiter at company B is also looking at it for the first time. Those evaluations are independent of each other.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">That independence disappears when it all feeds into a single model. These AI tools have four properties that fundamentally distinguish them from human recruiters: they&#8217;re pervasively adopted across many employers simultaneously, they have persistent memory across applications, their decisions are highly consequential, and they&#8217;re opaque \u2014 neither candidates nor employers can see exactly how decisions are being made. Even if AI were capable of true AGI-level reasoning, those properties create problems that\u00a0don&#8217;t\u00a0exist in the human recruiting process.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Solving it is not straightforward. You could tell HR teams: if you see a resume that appeared in a prior screening, clear your history. But I\u00a0don&#8217;t\u00a0know if that works, because I\u00a0don&#8217;t\u00a0know\u00a0if the problem is\u00a0the memory\u00a0or if the same model would just produce the same output regardless.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Geoff:<\/span><\/b><span data-contrast=\"auto\">\u00a0Right. The\u00a0natural instinct\u00a0for an HR leader reading this study is\u00a0probably not\u00a0to ditch the tool altogether but to look for configuration tweaks that preserve the efficiency gains. Given the volume of applications organizations\u00a0are receiving\u00a0every day, I\u00a0don&#8217;t\u00a0think many will abandon AI screening entirely. Some of the onus\u00a0has\u00a0to\u00a0fall on the AI vendors to allow employer-level configuration and\u00a0actually have\u00a0those configurations affect how memory and screening are conducted. But\u00a0I&#8217;m\u00a0not sure how much will change quickly.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Nick:<\/span><\/b><span data-contrast=\"auto\">\u00a0It&#8217;s\u00a0a tough place to be in HR. Even if your team stayed the same size, 3x the applications\u00a0means\u00a0you&#8217;re\u00a0not equipped to handle that volume without AI.\u00a0So\u00a0there&#8217;s\u00a0a real need for a solution. AI is\u00a0the\u00a0natural one. There are clearly\u00a0real challenges\u00a0with it, as this study shows. But just powering through triple the applications manually\u00a0doesn&#8217;t\u00a0seem like the right answer either.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Which\u00a0actually dovetails\u00a0into our next story.\u00a0Uber is planning to lay off 23% of its HR workforce. Before we get into the details \u2014 Uber has explicitly said this is not AI-driven, which is\u00a0very different\u00a0from\u00a0almost every\u00a0other layoff\u00a0you&#8217;ve\u00a0seen in the news lately.\u00a0It&#8217;s\u00a0hard to completely believe\u00a0that, because\u00a0Uber has been disproportionately in the news lately.\u00a0First\u00a0for its\u00a0very strong\u00a0stated intention to use AI to\u00a0optimize\u00a0its business. Then a\u00a0report\u00a0that they blew through their AI budget in the first three months of the year. Then the CTO came out and acknowledged they\u00a0don&#8217;t\u00a0have\u00a0a very clear\u00a0or demonstrable ROI from their AI spend.\u00a0So\u00a0before you hear about the layoff, what you know is that\u00a0they&#8217;re\u00a0spending aggressively on AI and\u00a0don&#8217;t\u00a0have much to show for it, which means\u00a0they&#8217;re\u00a0probably looking\u00a0for cost savings.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">In this new environment, it feels like everyone is cutting middle managers. HR doesn&#8217;t have middle managers in the strict sense, but it has a middle-manager-adjacent dynamic of managing people and processes that makes it a common target. The CEO, in an internal memo, said the changes are necessary to maximize the effectiveness of the\u00a0people team\u00a0and the enormous potential ahead. That&#8217;s vague enough to mean almost anything. They later explicitly said this is not AI-driven, but I understand why that&#8217;s met with some skepticism.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Geoff:<\/span><\/b><span data-contrast=\"auto\">\u00a0Actions speak louder than words, in\u00a0its\u00a0most basic form.\u00a0There&#8217;s\u00a0so much scrutiny and skepticism around C-suite intentions and how layoffs are being communicated. We talked about this with the Bolt CEO, Ryan Breslow, making a big announcement about getting rid of his entire HR team \u2014 but he still had people ops covering those functions. You simply\u00a0have to\u00a0have these functions covered. Someone needs to own core people-related\u00a0processes for the business to run smoothly. You can reframe it as HR versus people operations, that&#8217;s fine. But cutting the processes out entirely is not sustainable. It&#8217;s about how you structure it to make sure those things are still getting done.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Nick:<\/span><\/b><span data-contrast=\"auto\">\u00a0A similar analogy: I often get the question of whether a company should hire a dedicated wellness coordinator. And I come back\u00a0to:\u00a0what you really need is someone to own the wellness program. That could be a single person with wellness in their title, or it could be a wellness committee, or an HR benefits person who&#8217;s really engaged with wellbeing and wants to own the process. What you can&#8217;t do is launch wellness software without any true internal owner, because it will flop. Someone\u00a0has to\u00a0own it. The same logic applies to HR. Yes, it could be traditional HR. But the reality is all these processes need an owner \u2014 whether that&#8217;s HR, managers, or a combination. Uber is a sophisticated company. They&#8217;re not going to forget payroll or benefits. They&#8217;re just trying to distribute those responsibilities differently, probably eliminating the middle layer and pushing ownership directly to managers and the rank and file.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Geoff:<\/span><\/b><span data-contrast=\"auto\">\u00a0Yeah, makes sense. Well, that seems like a good place to wrap up. Thanks as always for tuning in. You can catch us on Spotify, Apple Podcasts, or wherever you get your podcasts. And be sure to subscribe to\u00a0Wellable\u00a0Weekly for\u00a0all of\u00a0our latest insights. Thank you.<\/span><\/p>\n          <\/div>\n        <\/div>\n\n        \n      <\/div>\n    <\/section>\n\n    \n","protected":false},"excerpt":{"rendered":"<p>Wellable Weekly breaks down a landmark Stanford study on AI hiring bias, the &#8220;algorithmic monoculture&#8221; problem reshaping recruitment, and what Uber&#8217;s decision to cut 23% of its HR team means for the future of people operations.<\/p>\n","protected":false},"author":1,"featured_media":34442,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[23],"tags":[],"class_list":["post-34435","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-podcasts"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.6 (Yoast SEO v27.7) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ 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