Hidden in Code: Unmasking Al Bias in Hiring

Authors

• Katie Rubin, Benjamin N. Cardozo School of Law

Abstract

The note argues that while AI-driven hiring tools offer efficiency, they often automate and amplify systemic biases by relying on flawed historical data, creating a "hidden" form of discrimination. The analysis asserts that existing legal frameworks are inadequate for addressing these algorithmic harms, necessitating urgent federal reforms to ensure transparency and accountability.

Disciplines

Law | Legal Profession | Science and Technology Law

Recommended Citation

Katie Rubin, Hidden in Code: Unmasking Al Bias in Hiring, 32 Cardozo J. Equal Rts. & Soc. Just. 444 (2026).
Available at: https://larc.cardozo.yu.edu/cardozoersj/vol32/iss2/6