Ethics, Bias, and Fairness in Medical AI: Designing Trustworthy Healthcare Systems
artificial-intelligence-ai.
Course Modules:
Module 1: Foundations of Medical AI Ethics
Principles of biomedical ethics: autonomy, justice, beneficence, and non-maleficence
Unique ethical challenges in AI for healthcare
Overview of AI regulatory bodies and ethical guidelines
Module 2: Understanding Bias in Medical Datasets
Historical and demographic bias in EHRs and imaging datasets
Sampling bias, label bias, and measurement bias
Examples of AI harms in healthcare systems
Module 3: Fairness in Algorithms and Predictions
Defining fairness: demographic parity, equal opportunity, equalized odds
Measuring disparities in medical predictions
Ensuring fairness across race, gender, age, and comorbidities
Module 4: Mitigation Strategies and Best Practices
Fairness-aware machine learning
Reweighting, data balancing, adversarial debiasing
Participatory design and patient-centered AI development
Module 5: Transparency, Explainability, and Accountability
The role of interpretable models in clinical trust
Tools like SHAP, LIME, and Grad-CAM for explanation
Assigning responsibility in high-stakes AI deployment
Module 6: Capstone Project – Audit and Improve a Medical AI System
Choose a medical AI use case (e.g., triage, disease prediction)
Identify ethical risks and fairness gaps
Propose improvements and submit an ethical impact report
Tools & Technologies Used:
Python
Fairlearn, AIF360 for bias detection
SHAP, LIME for interpretability
Simulated or real datasets (e.g., MIMIC-III, eICU)
Target Audience:
AI developers working on medical systems
Healthcare professionals using or evaluating AI tools
Clinical researchers and ethicists
Public health policymakers and data regulators
Global Learning Benefits:
Ensure AI in healthcare serves all patients equitably
Identify and prevent harm from biased models and datasets
Build transparent, ethical systems that gain trust from users and patients
Prepare for regulatory audits and compliance with global health standards
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