AI Hiring Tools Can Yield Racial Bias and Systemic Rejection

> We follow 3.4 million people who submit 4 million job applications to 1,700 job postings across 150 employers and 11 industry sectors. Each job application was assessed by an AI hiring tool built by a single third-party vendor. Our new paper offers a rare look inside the “black box” of algorithmic hiring, showing that these tools increase racial bias and shut the same people out of jobs everywhere they apply. > We find substantial evidence of racial disparities in AI-based candidate screening. To measure adverse impact, we apply the EEOC’s “four-fifths rule,” which flags a position when one group is recommended at less than 80% of the rate of the most-recommended group — the relevant U.S. employment law (Title VII). We discovered that 26% of Black applicants and 15% of Asian applicants applied to positions where the AI system discriminated against their racial group. To put this in perspective: If the AI had recommended Black and Asian candidates at the same rate as it recommended the most-favored group (typically white applicants), 40,000 more of their applications would have advanced to the next stage of hiring. > We find that people who submit multiple applications to positions screened by the same algorithmic hiring vendor are more likely to be rejected from every position to which they apply than would be true if the companies made decisions statistically independently from one another. Ten percent of applicants who submit four applications are rejected from all the places to which they apply.