Behind the Black Box: Employer Accountability for Algorithmic Hiring Bias

Authors

• Nicole Capp

Abstract

AI hiring tools are now ubiquitous in employment, promising efficiency, cost savings, and reduced human bias. Yet these systems often operate as “black boxes,” replicating or amplifying existing biases and raising significant legal concerns under Title VII of the Civil Rights Act of 1964. Even without discriminatory intent, AI trained on historical hiring data can produce disparate impacts, exposing employers to liability for outcomes they cannot fully understand or explain. Plaintiffs face steep challenges in litigating such claims, particularly in identifying specific practices, demonstrating causation, and proposing feasible alternatives. This article examines how AI perpetuates discrimination in hiring, analyzes the principles and limits of disparate impact doctrine, highlights practical and legal obstacles for plaintiffs, and proposes a uniform federal framework to ensure transparency, accountability, and fairness in algorithmic employment decisions.

First Page

134

Recommended Citation

Nicole Capp, Behind the Black Box: Employer Accountability for Algorithmic Hiring Bias, 10 Bus. Entrepreneurship & Tax L. Rev. 134 (2026).
Available at: https://scholarship.law.missouri.edu/betr/vol10/iss1/8