Evaluate students' ability to analyze and balance trade-offs among data privacy, algorithmic fairness, and predictive accuracy in ML systems for healthcare policy. Assess understanding of technical metrics (e.g., DP parameters, fairness definitions, calibration), empirical evaluation methods (simulation, holdout studies, cost-benefit), mitigation strategies, and ethical/regulatory and stakeholder implications; require justified policy recommendations and critical appraisal of trade-offs in interdisciplinary contexts.