(Honors Thesis under the direction of Dr. Sekou Bermiss)

Abstract: Artificial intelligence (AI) increasingly shapes early-stage recruitment, often determining which candidates advance before human review. Although often framed as efficient and objective, these systems can reproduce or amplify labor-market inequalities by generating disparate impacts–outcome-based differences in selection rates across protected groups, regardless of intent (Barocas & Selbst, 2016). Using a comparative approach, it analyzes New York City’s Local Law 144, which mandates bias audits for Automated Employment Decision Tools, and North Carolina’s voluntary Responsible AI Framework. Drawing on policy analysis, regulatory guidance, and audit documentation, the study assesses the influence of mandatory versus voluntary governance models on transparency, accountability, and equity. Findings reveal significant gaps, showing that limited oversight can obscure algorithmic decision-making and weaken bias mitigation. By focusing on entry-level recruitment, this research highlights how governance frameworks shape access to economic opportunity and offers insights for more equitable AI hiring practices.