Lilly Tech Systems
Bias & Fairness Testing
Detect and measure AI bias with fairness metrics, demographic parity, equalized odds, IBM AI Fairness 360, and Google What-If Tool.
Course Lessons
Work through these lessons sequentially or jump to the topic most relevant to you.
1. Understanding Bias in AI
Sources and types of AI bias
2. Fairness Metrics
Quantifying fairness in ML models
3. Demographic Parity Testing
Testing for demographic parity
4. Equalized Odds Analysis
Analyzing equalized odds
5. IBM AI Fairness 360
Using IBM AIF360 toolkit
6. Google What-If Tool
Exploring models with What-If Tool
7. Building Fairness Test Suites
Creating comprehensive fairness tests
What You'll Learn
By the end of this course, you will be able to:
Core Concepts
Understand the fundamental principles and techniques of bias & fairness testing for production AI systems.
Practical Skills
Build hands-on skills with real code examples, frameworks, and tools used by industry professionals.
Best Practices
Apply industry best practices and avoid common pitfalls when implementing testing in your ML projects.
Production Ready
Ship reliable, well-tested AI systems with confidence using automated testing pipelines.