@article {2019,
	title = {Mitigating bias in algorithmic employment screening: Evaluating claims and practices},
	year = {2019},
	abstract = {<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{\textquoteright} for-mulation of {\textquotedblleft}bias{\textquotedblright} 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>},
	keywords = {social power of algorithms},
	url = {https://www.researchgate.net/publication/333971698_Mitigating_Bias_in_Algorithmic_Employment_Screening_Evaluating_Claims_and_Practices},
	author = {Manish Raghavan and Solon Barocas and Jon KleinbergKaren Levy}
}
