[Assess graduate-level understanding of conceptual frameworks and practical approaches for identifying, measuring, and mitigating algorithmic bias and assessing fairness in clinical decision-support systems (CDSS). Test knowledge of fairness definitions and metrics, socio-technical and causal frameworks, subgroup and intersectional analyses, dataset shift and validation strategies, interpretability and auditing tools (e.g., model cards, datasheets), mitigation techniques, and relevant ethical/regulatory considerations; require designing an evaluation plan for a CDSS scenario.]