What aggregate audits miss
The 2026 Stanford study is one of the largest analyses of algorithmic hiring ever conducted. It followed 3.4 million people submitting 4 million job applications across 1,700 positions. Every application was screened by a single third-party vendor, a setup the researchers describe as “algorithmic monoculture,” reflecting the reality that a small number of vendors now supply hiring algorithms to a large share of U.S. employers. When one vendor’s tool carries bias, that bias can affect candidates across every employer using it.
Applying the Equal Employment Opportunity Commission’s (EEOC) “four-fifths rule” — the standard threshold used under U.S. employment discrimination law to identify adverse impact in hiring — researchers found that 26% of Black applicants and 15% of Asian applicants submitted applications to positions where the AI system discriminated against their racial group. If the tool had advanced those candidates at the same rate as the most-favored group, roughly 40,000 more applications would have moved forward.
The critical finding wasn’t just that bias existed. It was that the bias was invisible at the aggregate level. Sarah Bana, co-author of the study and assistant professor at Chapman University in Orange, California, explained the mechanism.
“Earlier research reported aggregate numbers, averaged across all the positions a vendor screens for. We disaggregated and looked at each position separately. That’s the major difference,” she said.
“Imagine a model that over-selects one group for warehouse jobs and under-selects them for finance jobs. The averages would look balanced; the position-by-position picture would show real bias. That’s roughly the pattern we found.”