Mitigating bias in algorithmic employment screening: Evaluating claims and practices

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<p>There has been rapidly growing interest in the use of algorithms for employment assessment,especially as a means to address or mitigate bias in hiring. Yet, to date, little is known abouthow these methods are being used in practice. How are algorithmic assessments built, vali-dated, and examined for bias? In this work, we document and assess the claims and practicesof companies offering algorithms for employment assessment, using a methodology that can beapplied to evaluate similar applications and issues of bias in other domains. In particular, weidentify vendors of algorithmic pre-employment assessments (i.e., algorithms to screen candi-dates), document what they have disclosed about their development and validation procedures,and evaluate their techniques for detecting and mitigating bias. We find that companies’ for-mulation of “bias” varies, as do their approaches to dealing with it. We also discuss the variouschoices vendors make regarding data collection and prediction targets, in light of the risks andtrade-offs that these choices pose. We consider the implications of these choices and we raise anumber of technical and legal considerations.</p>