Hiring by Algorithm
Posted: 10 Mar 2016 Last revised: 21 Mar 2018
Date Written: March 10, 2016
In the past decade, advances in computing processes such as data mining and machine learning have prompted corporations to rely on algorithmic decision-making with the presumption that such decisions are efficient and fair. The use of such technologies in the hiring process represents a particularly sensitive legal arena. In this Article, I note the increasing use of automated hiring platforms by large corporations and how such technologies might facilitate unlawful employment discrimination, whether due to (inadvertent) disparate impact on protected classes or the technological capability to substitute facially neutral proxies for protected demographic details. I also parse some of the proposed technological solutions to discrimination in hiring and examine them for the potential for unintended outcomes. I argue that technologically-based solutions should be employed only in support of legislative and litigation-driven redress mechanisms that encourage employers to adopt fair hiring practices. I make the policy argument that audits of automated hiring platforms should be a mandated business practice that serves the ends of equal opportunity in employment. Employers that subject their automated hiring platform to external audits could receive a certification that serves to distinguish them in the labor market. Borrowing from Tort law, I argue that an employer’s failure to audit its automated hiring platforms for disparate impact should serve as prima facie evidence of discriminatory intent under Title VII. Finally, I conclude that ex ante solutions such as the adoption of fairness by design principles for algorithmic hiring systems represents another viable option to prevent covert employment discrimination.
Keywords: Big Data, Algorithms, Disparate Impact, Employment Discrimination, Hiring, Business, Corporations, Organizations, Sociology of Work, Diversity
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