The first large-scale study of hiring algorithms in the wild finds concerning patterns to how systems reject candidates.The first large-scale study of hiring algorithms in the wild finds concerning patterns to how systems reject candidates.

It’s graduation season and the Class of 2026 is entering one of the toughest labor markets in years. Entry-level hiring has slowed. At the same time, AI tools have made it easier than ever for job seekers to fire off applications. Together, fewer jobs and more applications mean companies are now seeing nearly three times as many applications for entry-level positions as in 2022. AI is changing not just if firms hire, but how they hire. Ninety percent of U.S. employers use AI screening tools to sort and rank job seekers, with most relying on the same few third-party vendors. When one algorithm influences many employers, what is the impact on job seekers?

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.

Surfacing racial bias at scaleSurfacing racial bias at scale

Algorithmic monocultures can give rise to systemic rejectionAlgorithmic monocultures can give rise to systemic rejection

...read more at hai.stanford.edu