Assess graduate-level understanding of conceptual frameworks for evaluating algorithmic fairness and interpretability in healthcare AI, including fairness metrics and trade-offs, sources of dataset and measurement bias, subgroup performance and calibration, interpretability approaches (inherently interpretable vs post-hoc), stakeholder-centered evaluation, and ethical/regulatory considerations for practical assessment and critique.