A Stanford study that tracked 4 million job applications across 1,700 positions found that AI hiring tools can pass standard bias audits at the aggregate level while still systematically screening out Black and Asian candidates for specific roles. The finding exposes a gap in how most employers validate their screening software - and it arrives as a federal judge weighs whether Workday can be held liable under California law for how its algorithms filter candidates.
The 2026 research, led by Sarah Bana of Chapman University, followed 3.4 million people submitting applications screened by a single third-party vendor. The researchers call this setup "algorithmic monoculture": a small number of vendors now supply hiring algorithms to a large share of U.S. employers, meaning bias in one tool can ripple across every company using it.
What aggregate audits miss
Applying the Equal Employment Opportunity Commission's four-fifths rule - the standard threshold for identifying adverse impact - the study found that 26% of Black applicants and 15% of Asian applicants applied to positions where the AI system discriminated against their racial group. If those candidates had been advanced 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 when numbers were averaged across all roles. "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," Bana 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."
U.S. employment law evaluates adverse impact one position at a time, because that's how employers make hiring decisions. A clean aggregate audit doesn't reflect that reality. Employers are already using these tools to make high-stakes filtering decisions at enormous volume, and the Workday case shows what that exposure looks like.
Legal exposure sits with the employer
The lawsuit was originally filed in 2023 by Derek Mobley, a Black job seeker who claims he was passed over for more than 100 positions at companies using Workday's software, citing discrimination based on race, age, and disability. The case has since expanded to include additional plaintiffs and broader claims under California's Fair Employment and Housing Act.
The presiding judge has already ruled that Workday can be treated as an employer under federal anti-discrimination law because it performs screening functions its clients would otherwise carry out themselves. That finding is significant, but it doesn't shift liability away from employers. "It probably depends on the vendor, but I think it's important to recognize that the legal exposure sits with the hiring firm, not the vendor," Bana said. "Under New York City's Local Law 144, and with the EEOC designating AI-based screening tools as an enforcement priority in its current Strategic Enforcement Plan, the employer is the regulated party. So your legal office should do their due diligence."
Three steps organizations should take now
Bana offered concrete recommendations for any organization using AI to screen candidates.
First: Periodically advance a small random sample of candidates the algorithm would have rejected, then track how they perform downstream. "Without observing how the applicants the algorithm rejects would have performed, you cannot validate whether the screening tool is filtering out the right people," she said. "If the filtered-out applicants perform comparably, the screening tool is generating artificial scarcity. If it performs worse, the screening tool is producing genuine signal."
Second: Require position-level disparity reporting as a contractual condition. Aggregate reports can hide significant problems in specific roles. "Require selection rates broken out by position and demographic group as part of your contract," Bana said. "This is information that will let you know if you're getting what you paid for: an unbiased screening tool."
Third: Close the gap between screening data and performance data. Most organizations keep these in separate systems that don't communicate. "Performance data lives in one table and screening recommendations live in another, and the two don't typically talk to each other," Bana said. "The screening tool isn't just a hiring tool. It shapes who ends up working at your firm."
For HR leaders building internal governance around these tools, the AI Learning Path for CHROs addresses the compliance and validation frameworks that position-level auditing demands. Broader coverage of how screening algorithms intersect with employment law continues to evolve across AI for Human Resources resources.
Why this matters for HR professionals
The regulatory environment is still catching up to the technology. "Everything feels quite voluntary right now," Bana said. That's unlikely to remain true. The algorithmic monoculture the Stanford researchers identified means that when one vendor's tool gets it wrong, the consequences can extend well beyond one employer. The question HR teams need to ask isn't simply whether their hiring tool has been audited. It's what that audit actually examined, at what level of granularity, and whether those answers map to how the tool is being used across specific roles. A vendor's aggregate audit report is not a substitute for position-by-position validation - and the legal exposure for getting that wrong sits squarely with the employer.
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