Bias, Fairness, and Responsible AI
Ensuring that AI systems are equitable, transparent, and trustworthy. This section addresses the scientific, ethical, and increasingly regulatory dimensions of responsible AI development.
Sources of Bias in Medical AIsoon
Where bias enters the pipeline: historical bias in clinical data, selection bias in datasets, measurement bias across devices, labeling bias from annotators, and algorithmic amplification.
Demographic Fairness: Performance Across Populationssoon
Why models must be evaluated across demographic subgroups (age, sex, race, ethnicity), how to measure disparities, and what to do when performance varies across populations.
Generalization Across Devices, Sites, and Geographiessoon
The technical and clinical dimensions of generalization failure. Domain shift, dataset bias toward specific scanners or institutions, and strategies for building robust models.
Explainability and Interpretability in Clinical AIsoon
What it means for a model to be "interpretable." Saliency maps, attention visualization, SHAP values, concept-based explanations, and the tradeoff between performance and transparency.
Transparency and Reproducibilitysoon
The reproducibility crisis in AI research and what you can do about it. Model cards, datasheets for datasets, open-source practices, and documentation standards.
Ethical Frameworks for AI in Healthcaresoon
Beneficence, non-maleficence, autonomy, and justice as applied to clinical AI. Informed consent for AI-assisted care, the role of human oversight, and emerging governance frameworks.