Largest study of AI hiring algorithms to date finds ‘clear racial disparities’ — over 25% of Black applicants tainted by bias
A comprehensive study of AI hiring algorithms has revealed significant racial disparities in job applicant outcomes. Over 25% of applications from Black job seekers were found to be directed to positions with discriminatory outcomes. The research highlights systemic rejection patterns, where being rejected by one employer predicts rejection by others using the same algorithm.
- ▪The study analyzed over 4 million job applications from 3 million applicants across 156 employers.
- ▪More than 30% of Black applicants applied to at least one position with adverse impacts on their chances.
- ▪The algorithms used by Pymetrics have been shown to create an 'algorithmic blackball' effect, where rejection from one company predicts rejection from others.
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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.Recommended Video 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.
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