Algorithmic Accountability and the Law

Abstract

This article examines the legal challenges posed by the increasing use of algorithms in consequential decisions affecting employment, credit, insurance, and criminal justice. The authors argue that algorithmic decision-making systems often operate as 'black boxes' that are opaque to the individuals affected by their outputs and to regulators tasked with ensuring fairness. The article proposes a framework for algorithmic accountability that includes transparency requirements, auditing mandates, and meaningful opportunities for individuals to challenge algorithmic decisions. The authors argue that existing anti-discrimination law is inadequate to address the novel forms of bias that can emerge from algorithmic systems.

Key Findings

• Algorithmic decision-making can perpetuate and amplify existing patterns of discrimination
• The opacity of algorithmic systems undermines due process and accountability
• Existing anti-discrimination frameworks are insufficient for algorithmic contexts
• Meaningful accountability requires transparency, auditing, and rights of contestation

Related Statutes

• Civil Rights Act of 1964, Title VII
• Equal Credit Opportunity Act
• Fair Housing Act

Related Cases

• Griggs v. Duke Power Co. (1971)
• Texas Dept. of Housing v. Inclusive Communities (2015)