Lilly Tech Systems
AI Bias & Fairness
Learn to identify, measure, and mitigate bias in AI systems. Explore data bias, algorithmic bias, fairness metrics like disparate impact and equalized odds, and tools like Fairlearn and AI Fairness 360.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is AI bias? Why fairness matters, real-world consequences, and the ethical imperative for fair AI.
2. Types of Bias
Data bias, selection bias, measurement bias, algorithmic bias, representation bias, and historical bias.
3. Detection
Fairness metrics, disparate impact analysis, equalized odds, demographic parity, and statistical testing methods.
4. Mitigation
Pre-processing, in-processing, and post-processing techniques for reducing bias while maintaining model performance.
5. Tools
Fairlearn, AI Fairness 360, What-If Tool, Aequitas, and other frameworks for measuring and mitigating bias.
6. Best Practices
Fairness-aware ML pipelines, documentation standards, audit processes, and organizational practices for equitable AI.
What You'll Learn
By the end of this course, you'll be able to:
Identify Bias Sources
Recognize where bias enters the ML pipeline from data collection through model deployment and monitoring.
Measure Fairness
Apply fairness metrics like disparate impact, equalized odds, and demographic parity to evaluate model behavior.
Apply Mitigation
Implement bias mitigation techniques at each stage of the ML lifecycle using industry-standard tools.
Build Fair Systems
Design end-to-end ML pipelines with fairness constraints, auditing, and continuous monitoring built in.