Algorithms and Economic Justice: A Taxonomy of Harms and a Path Forward for the Federal Trade Commission

Rebecca Kelly Slaughter
Janice Kopec & Mohamad Batal
23 Yale J.L. & Tech. Special Issue 1

As an FTC Commissioner, I aim to promote economic and social justice through consumer protection and competition law and policy. In recent years, algorithmic decision-making has produced biased, discriminatory, and otherwise problematic outcomes in some of the most important areas of the American economy. This article describes harms caused by algorithmic decision-making in the high-stakes spheres of employment, credit, health care, and housing, which profoundly shape the lives of individuals. These harms are often felt most acutely by historically disadvantaged populations, especially Black Americans and other communities of color. And while many of the harms I describe are not entirely novel, AI and algorithms are especially dangerous because they can simultaneously obscure problems and amplify them—all while giving the false impression that these problems do not or could not possibly exist.

This article offers three primary contributions to the existing literature. First, it provides a baseline taxonomy of algorithmic harms that portend injustice, describing both the harms themselves and the technical mechanisms that drive those harms. Second, it describes my view of how the FTC’s existing tools—including section 5 of the FTC Act, the Equal Credit Opportunity Act, the Fair Credit Reporting Act, the Children’s Online Privacy Protection Act, and market studies under section 6(b) of the FTC Act—can and should be aggressively applied to thwart injustice. And finally, it explores how new legislation or an FTC rulemaking under section 18 of the FTC Act could help structurally address the harms generated by algorithmic decision-making.