As we incorporate the next wave of AI-enabled products in high-stakes decisions, we need some level of assurance of the safety that we have come to expect from everyday products. Continuing the progress of using AI in high-stakes decisions requires trusting AI-enabled solutions to deliver their promised benefits while protecting the public from harm. Questions about the security, safety, privacy, and fairness of AI-enabled decisions need to be answered as a condition for deploying AI solutions at scale. This book is a guide that will introduce you to key concepts, use cases, tools, and techniques of the emerging field of Responsible AI. We will cover hands-on coding techniques to identify and measure bias. Measuring bias is not enough: we also need to explain and fix our models. This book outlines how to do this throughout the machine learning pipeline. By the end of this book, you will have mastered Python coding techniques of explaining machine learning models’ logic, measuring their fairness at the individual and group levels and monitor them in production environments to detect degradation in their accuracy or fairness.

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Spis treści

Building Responsible AI with Python. Learn to identify and mitigate bias with hands-on code examples eBook -- spis treści

• 1. What is Responsible AI and Why Do We Need it?
• 2. Responsible AI Concepts
• 3. Bias and Fairness Concepts
• 4. Introducing our Datasets
• 5. Individual Fairness Assessment
• 6. Choosing the Right Metrics: A Summary
• 7. Choosing the Right Metrics: A Summary
• 8. Feature Importance Explanations
• 9. Visual Explanations
• 10. Decision Trees & Decision Rules Explanations
• 11. Contrastive and Counterfactual Explanations
• 12. Choosing the Right Explanation: A Summary
• 13. Pre-Processing Methods
• 14. In-Processing Methods
• 15. Choosing the Right Mitigation Approaches: A Summary
• 16. Choosing the Right Mitigation Approaches: A Summary
• 17. What is Model Drift and Why do We Care?
• 18. Managing the Adverse Impacts of Model Drift