📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI Ethics and Explainable AI (XAI) in Healthcare, from foundations to a certified capstone project.
Ethical Frame
• Autonomy, beneficence, non-maleficence and justice as operational constraints
• Informed consent when a model contributes to a clinical decision
• Accountability when responsibility is distributed across developer, vendor and clinician
Bias
• Sources of bias: sampling, label, measurement and deployment
• Documented cases where clinical algorithms disadvantaged patient groups
• Fairness metrics, their incompatibility, and choosing among them defensibly
Explainability
• SHAP, LIME and attention maps: what they do and do not establish
• Saliency methods that look convincing while being unreliable
• Inherently interpretable models as an alternative to post-hoc explanation
Clinician Interaction
• Calibrated trust: appropriate reliance rather than maximum acceptance
• Automation bias and deskilling risk in routine use
• Designing explanations that support rather than replace clinical reasoning
Governance
• Transparency and explainability requirements in emerging regulation
• Ethics committee review and institutional deployment governance
• Post-deployment monitoring for equity as well as accuracy
Earn government-registered certification in AI Ethics and Explainable AI (XAI) in Healthcare
e-Certificate and e-Marksheet issued on successful completion.
