Largest study of AI hiring algorithms to date finds ‘clear racial disparities’ — over 25% of Black applicants tainted by bias

The most comprehensive independent study of AI-powered hiring algorithms ever conducted has found stark racial disparities embedded in the tools used to screen millions of job applicants, with more than one in four applications submitted by Black job seekers directed to positions where the algorithm produces outcomes that trigger federal discrimination scrutiny. The paper, “Algorithmic Monocultures in Hiring,” was authored by researchers at Stanford University, Chapman University, and Northeastern University, and will be presented at the ACM Conference on Fairness, Accountability, and Transparency in Montreal next month. It analyzed more than 4 million job applications submitted by 3 million applicants across 156 employers — mostly companies with $5 billion and up in annual revenue — all screened by algorithms built by the same vendor, a talent platform called Pymetrics. “We find clear racial disparities in applicant outcomes,” the authors write. “As a single vendor comes to dominate decision-making in a space, their quirks or shortfalls can be present across that entire sector in a way that wasn’t possible before,” Northeastern professor and research co-author Kathleen Creel told the Financial Times, which previously reported on the study. Pymetrics’ owner, Harver, did not respond to a request for comment. How the algorithm works—and where it breaks down Pymetrics, which was acquired in 2022 and whose algorithms are used by major employers across finance, manufacturing, and technology, screens applicants not through resumes but through a battery of online games designed to measure cognitive traits like risk tolerance, processing speed, and altruism. The company has long marketed this approach as more objective than traditional resume screening, and, in its own prior analysis, found no disparities that rose to the level of legal scrutiny. The new research challenges that conclusion — not by disputing Pymetrics’ math, but by arguin