Assess students' ability to evaluate competing machine-learning fairness interventions in clinical diagnostic systems by testing knowledge of performance and fairness metrics, bias-mitigation strategies, interpretability methods, study and evaluation design (including subgroup and external validity analyses), and how these trade-offs affect patient outcomes, ethical considerations, and regulatory compliance; require justification of context-appropriate recommendations.