Assess graduate-level understanding of how algorithmic hiring systems influence applicant selection bias, short- and long-term workforce diversity, and professional career trajectories; evaluate sources of algorithmic bias (data, labels, features, feedback loops), measurement approaches (fairness metrics, disparate impact, causal inference), empirical and longitudinal study designs, legal/ethical frameworks, and evidence-based mitigation strategies and policies.