Assess graduate-level ability to analyze trade-offs between patient data privacy, algorithmic fairness, and clinical utility in AI-driven healthcare decision systems. The quiz tests knowledge of privacy-preserving techniques (de-identification, differential privacy, federated learning), fairness metrics and mitigation approaches, impacts on predictive performance and clinical outcomes, regulatory/ethical constraints, and frameworks for evaluating and balancing competing objectives using technical, clinical, and policy levers. Include short scenarios requiring critique of design choices, measurement strategies, and stakeholder impacts.