Applying Old Rules to New Tools: Employment Discrimination Law in the Age of Algorithms
74 Pages Posted: 30 Oct 2019 Last revised: 21 Aug 2020
Date Written: October 21, 2019
Companies, policymakers, and scholars alike are paying increasing attention to algorithmic recruitment and hiring tools that leverage artificial intelligence, machine learning, and Big Data. To its advocates, algorithmic employee selection processes can be more effective in choosing the strongest candidates, increasing diversity, and reducing the influence of human prejudices. Many observers, however, express concern about other forms of bias that can infect algorithmic selection procedures, leading to fears regarding the potential for algorithms to create unintended discriminatory effects or mask more deliberate forms of discrimination. This article represents the most comprehensive analysis to date of the legal, ethical, and practical challenges associated with using these tools.
The article begins with background on both the nature of algorithmic selection tools and the legal backdrop of antidiscrimination laws. It then breaks down the key reasons why employers, courts and policymakers will struggle to fit these tools within the existing legal framework. These challenges include algorithmic tools’ reliance on correlation; the opacity of models generated by many algorithmic selection tools; and the difficulty in fitting algorithmic tools into a legal framework developed for the employee selection tools of the mid-20th century.
The article concludes with a comprehensive proposed legal framework that weaves together the usually separate analyses of disparate treatment and disparate impact. It takes the fundamental principles of antidiscrimination laws, and the landmark Supreme Court cases interpreting them, and articulates a set of standards that address the unique challenges posed by algorithmic tools. The proposed framework (1) uses tests of reasonableness in disparate impact analysis in place of tests of statistical significance, which will become less and less meaningful in the age of Big Data; (2) requires employers to satisfy a modified form of the business necessity defense when an algorithmic tool has a disparate impact on a protected group; and (3) allows employers to use novel machine-learning techniques to prevent disparate impacts from arising without exposing themselves to disparate treatment liability.
Keywords: disparate impact, disparate treatment, test validity, validity, algorithmic bias, Title VII, discrimination, employment discrimination, machine learning, Big Data
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