Algorithmic Monocultures in Hiring

Algorithmic Monocultures in Hiring

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Key Findings

1. Large-scale adverse impact for Asians and Blacks. We are the first to demonstrate adverse impact in deployed algorithmic hiring as one of the largest demonstrations of unfair outcomes in real high-stakes AI decisions. 25.87% of applications submitted by Black applicants and 14.74% of applications submitted by Asian applicants are directed to positions that adversely impact them based on the standards of the relevant U.S. employment law (Title VII).
2. Adverse impact only revealed by disaggregated position-by-position analysis. While empirical studies of algorithmic hiring are very constrained due to data access limitations, prior studies showed minimal adverse impact due studying all of the vendor's data as a whole. By studying each position separately, in accordance with the standards of Title VII, we identify positions that demonstrate adverse impact that gets washed out in aggregate.
3. Algorithmic monocultures in hiring yield systemic rejections. We are the first to demonstrate systemic rejections in deployed algorithmic hiring as posited in prior theoretical research about algorithmic monoculture. The observed systemic rejection rate significantly exceeds that of the baseline of statistically independent decisions, even though the baseline accurately predicted observed systemic rejection rates in other hiring data in the absence of centralized algorithmic monocultures.
4. Data access inhibits independent research into hiring algorithms. We are the first and only group to independently conduct empirical research deployed hiring algorithms at scale, even though hiring algorithms mediate high-stakes decisions and are pervasively adopted. Given the data barriers, policy intervention may be necessary to enable scientific inquiry and increase accountability into this high-impact application of AI.

Algorithmic Hiring Pipeline

Many employers procure hiring algorithms from the same third-party vendors. Over 60% of the Fortune 100 use HireVue's algorithms. When hiring algorithms from a single vendor mediate hiring decisions at multiple employers, they constitute an algorithmic monoculture.

Revealing Adverse Impact

Title VII of the US Civil Rights Act governs discrimination in hiring. Prior studies found very limited adverse impact in algorithmic hiring data as a whole. We surface previously-overlooked adverse impact by studying positions separately. Black applicants are the most likely to be adversely impacted: 30% of Black applicants apply to at least one position that demonstrates adverse impact against Black applicants. In terms of total effect, Asian applicants experience the largest shortfall: if Asians were selected at the same rate as the most selected racial group for each position, then 29000 additional Asian applications would be recommended.

Identifying Systemic Rejection

When hiring algorithms from a single vendor mediate hiring decisions at multiple employers, they constitute an algorithmic monoculture. This can lead to systemic rejections, where certain groups are disproportionately rejected based on their characteristics. We demonstrate that algorithmic monocultures in hiring yield systemic rejections, which can have significant consequences for individuals and society as a whole.