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The high bar of proof to demonstrate either a disparate treatment or disparate impact cause of action under Title VII of the Civil Rights Act, coupled with the “black box” nature of many automated hiring systems, renders the detection and redress of bias in such algorithmic systems difficult. This Article, with contributions at the intersection of administrative law, employment & labor law, and law & technology, makes the central claim that the automation of hiring both facilitates and obfuscates employment discrimination. That phenomenon and the deployment of intellectual property law as a shield against the scrutiny of automated systems combine to form an insurmountable obstacle for disparate impact claimants. To ensure against the identified “bias in, bias out” phenomenon associated with automated decision-making, I argue that the employer’s affirmative duty of care as posited by other legal scholars creates “an auditing imperative” for algorithmic hiring systems. This auditing imperative mandates both internal and external audits of automated hiring systems, as well as record-keeping initiatives for job applications. Such audit requirements have precedent in other areas of law, as they are not dissimilar to the Occupational Safety and Health Administration (OSHA) audits in labor law or the Sarbanes-Oxley Act audit requirements in securities law. I also propose that employers that have subjected their automated hiring platforms to external audits could receive a certification mark, “the Fair Automated Hiring Mark,” which would serve to positively distinguish them in the labor market. Labor law mechanisms such as collective bargaining could be an effective approach to combating the bias in automated hiring by establishing criteria for the data deployed in automated employment decision-making and creating standards for the protection and portability of said data. The Article concludes by noting that automated hiring, which captures a vast array of applicant data, merits greater legal oversight given the potential for “algorithmic blackballing,” a phenomenon that could continue to thwart many applicants’ future job bids. 

A received wisdom is that automated decision-making serves as an anti-bias intervention. The conceit is that removing humans from the decision-making process will also eliminate human bias. The paradox, however, is that in some instances, automated decision-making has served to replicate and amplify bias. With a case study of the algorithmic capture of hiring as heuristic device, this Article provides a taxonomy of problematic features associated with algorithmic decision-making as anti-bias intervention and argues that those features are at odds with the fundamental principle of equal opportunity in employment. To examine these problematic features within the context of algorithmic hiring and to explore potential legal approaches to rectifying them, the Article brings together two streams of legal scholarship: law and technology studies and employment & labor law. Counterintuitively, the Article contends that the framing of algorithmic bias as a technical problem is misguided. Rather, the Article’s central claim is that bias is introduced in the hiring process, in large part, due to an American legal tradition of deference to employers, especially allowing for such nebulous hiring criterion as “cultural fit.” The Article observes the lack of legal frameworks that take into account the emerging technological capabilities of hiring tools which make it difficult to detect disparate impact. The Article thus argues for a re-thinking of legal frameworks that take into account both the liability of employers and those of the makers of algorithmic hiring systems who, as brokers, owe a fiduciary duty of care. Particularly related to Title VII, the Article proposes that in legal reasoning corollary to extant tort doctrines, an employer’s failure to audit and correct its automated hiring platforms for disparate impact could serve as prima facie evidence of discriminatory intent, leading to the development of the doctrine of discrimination per se. The article also considers other approaches separate from employment law such as establishing consumer legal protections for job applicants that would mandate their access to the dossier of information consulted by automated hiring systems in making the employment decision.