Regulating Artificial Intelligence: A State-by-State Analysis of Legislative Approaches to Potential AI Harms

Abstract

This thesis explores how three U.S. states—Colorado, California, and New York—have pioneered legislative approaches to address the societal impacts of artificial intelligence. Focusing on algorithmic discrimination, data privacy, and job displacement, it analyzes why each state prioritized different policy dimensions. Colorado’s Senate Bill 24-205 tackles bias in AI decision-making, establishing transparency standards and requiring bias audits. California’s Consumer Privacy Act (CCPA) underscores comprehensive data protection, aiming to grant consumers control over their personal information. New York’s legislative efforts target job displacement, proposing task forces, disclosure requirements, and taxes on automated systems that replace human labor. Through comparative case studies and legislative analysis, the thesis identifies key drivers behind these varied approaches, including public opinion, interest group influence, and partisan dynamics. Although the U.S. lacks a unified AI regulatory framework, state-level laws offer critical insight into emerging governance models.