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by Trisha Mahoney, Kush R. Varshney, Michael Hind
AI Fairness
Introduction
Are Human Decisions Less Biased Than Automated Ones?
AI Fairness Is Becoming Increasingly Critical
Defining Fairness
Where Does Bias Come From?
Bias and Machine Learning
Can’t I Just Remove Protected Attributes?
Conclusion
1. Understanding and Measuring Bias with AIF 360
Tools and Terminology
Terminology
Which Metrics Should You Use?
Individual Versus Group Fairness Metrics
Worldviews and Metrics
Dataset Class
Transparency in Bias Metrics
Explainer Class
AI FactSheets
2. Algorithms for Bias Mitigation
Most Bias Starts with Your Data
Pre-Processing Algorithms
In-Processing Algorithms
Post-Processing Algorithms
Continuous Pipeline Measurement
3. Python Tutorial
Step 1: Import Statements
Step 2: Load Dataset, Specify Protected Attribute, and Split Dataset into Train and Test
Step 3: Compute Fairness Metric on Original Training Dataset
Step 4: Mitigate Bias by Transforming the Original Dataset
Step 5: Compute Fairness Metric on Transformed Dataset
4. Conclusion
The Future of Fairness in AI
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