Overview
Learn to assess and mitigate fairness issues in AI systems, focusing on healthcare disparities. Hands-on practice with Fairlearn library to evaluate and improve ML model performance across racial groups.
Syllabus
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- Introduction to Fairness in AI
-- Definition and importance of fairness in AI
-- Overview of fairness issues in AI systems
-- Case studies of bias and unfairness in AI
- Understanding Bias in Healthcare AI
-- Introduction to healthcare disparities
-- Common sources of bias in healthcare AI systems
-- The impact of unfair AI on racial groups in healthcare
- Fairness Metrics and Evaluation
-- Overview of fairness metrics
-- Selecting the right fairness metrics
-- Hands-on tutorial with Fairlearn: Calculating fairness metrics
- Introduction to the Fairlearn Library
-- Introduction and installation
-- Core functionalities of Fairlearn
-- Using Fairlearn in Python for model assessment
- Assessing Fairness in AI Models
-- Practical session: Evaluating a sample healthcare model
-- Using Fairlearn's dashboard for visualization
-- Interpreting fairness metrics results
- Techniques for Mitigating Unfairness
-- Pre-processing techniques
-- In-processing techniques
-- Post-processing techniques
-- Hands-on practice: Implementing mitigation strategies with Fairlearn
- Case Study: Improving Fairness in Healthcare Models
-- Analyzing a real-world healthcare model
-- Identifying bias and unfairness
-- Applying Fairlearn for bias mitigation
- Best Practices and Deployment
-- Strategies for maintaining fairness in deployed models
-- Continuous monitoring and feedback loops
-- Ethical considerations and regulatory compliance
- Capstone Project
-- Define a project using real or simulated healthcare data
-- Assess bias in the AI system
-- Apply mitigation strategies to improve model fairness
-- Present findings and solutions
- Conclusion and Future Directions
-- Recap of key learnings
-- Emerging trends in AI fairness
-- Resources for further learning and research
Taught by
Tags
On-Demand
