Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
This work addresses the problem of bias in algorithmic hiring for job seekers and employers, but it is incremental as it documents existing practices without proposing new solutions.
The study investigated how companies develop and validate algorithmic hiring tools to mitigate bias, finding that practices vary widely and often lack transparency, with limited evidence of effective bias detection or mitigation.
There has been rapidly growing interest in the use of algorithms in hiring, especially as a means to address or mitigate bias. Yet, to date, little is known about how these methods are used in practice. How are algorithmic assessments built, validated, and examined for bias? In this work, we document and analyze the claims and practices of companies offering algorithms for employment assessment. In particular, we identify vendors of algorithmic pre-employment assessments (i.e., algorithms to screen candidates), document what they have disclosed about their development and validation procedures, and evaluate their practices, focusing particularly on efforts to detect and mitigate bias. Our analysis considers both technical and legal perspectives. Technically, we consider the various choices vendors make regarding data collection and prediction targets, and explore the risks and trade-offs that these choices pose. We also discuss how algorithmic de-biasing techniques interface with, and create challenges for, antidiscrimination law.