Responsible AI: Implementing Ethical and Unbiased Algorithms

✍ Scribed by Sray Agarwal, Shashin Mishra

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 189
• Edition: 1
• Category: Library

✦ Synopsis

This book is written for software product teams that use AI to add intelligent models to their products or are planning to use it. As AI adoption grows, it is becoming important that all AI driven products can demonstrate they are not introducing any bias to the AI-based decisions they are making, as well as reducing any pre-existing bias or discrimination.

 The responsibility to ensure that the AI models are ethical and make responsible decisions does not lie with the data scientists alone. The product owners and the business analysts are as important in ensuring bias-free AI as the data scientists on the team. This book addresses the part that these roles play in building a fair, explainable and accountable model, along with ensuring model and data privacy. Each chapter covers the fundamentals for the topic and then goes deep into the subject matter – providing the details that enable the business analysts and the data scientists to implement these fundamentals. 

AI research is one of the most active and growing areas of computer science and statistics. This book includes an overview of the many techniques that draw from the research or are created by combining different research outputs. Some of the techniques from relevant and  popular libraries are covered, but deliberately not drawn very heavily from as they are already well documented, and new research is likely to replace some of it.

✦ Table of Contents

Foreword
Preface
Who is this Book for?
How to Read this Book
How to Access the Code
Acknowledgements
Contents
Chapter 1: Introduction
What Is Responsible AI
Facets of Responsible AI
Fair AI
Explainable AI
Accountable AI
Data and Model Privacy
Bibliography
Chapter 2: Fairness and Proxy Features
Introduction
Key Parameters
Confusion Matrix
Common Accuracy Metrics
Fairness and Fairness Metrics
Fairness Metrics
Equal Opportunity
Predictive Equality
Equalized Odds
Predictive Parity
Demographic Parity
Average Odds Difference
Python Implementation
Proxy Features
Methods to Detect Proxy Features
Linear Regression
Variance Inflation Factor (VIF)
Linear Association Method Using Variance
Cosine Similarity/Distance Method
Mutual Information
Conclusion
Bibliography
Chapter 3: Bias in Data
Introduction
Statistical Parity Difference
Disparate Impact
When the Y Is Continuous and S Is Binary
When the Y Is Binary and S Is Continuous
Conclusion
Key Takeaways for the Product Owner
Key Takeaways for the Business Analysts/SMEs
Key Takeaways for the Data Scientists
Bibliography
Chapter 4: Explainability
Introduction
Feature Explanation
Information Value Plots
Partial Dependency Plots
Accumulated Local Effects
Sensitivity Analysis
Model Explanation
Split and Compare Quantiles
Global Explanation
Local Explanation
Morris Sensitivity
Explainable Models
Generalized Additive Models (GAM)
Counterfactual Explanation
Conclusion
Bibliography
Chapter 5: Remove Bias from ML Model
Introduction
Reweighting the Data
Calculating Weights
Implementing Weights in ML Model
Protected Feature: Married
Protected Feature: Single
Protected Feature: Divorced
Protected Feature: Number of Dependants Less than Three
Protected Feature: Work Experience Less than 10 Years
Calibrating Decision Boundary
Composite Feature
Additive Counterfactual Fairness
High Level Steps for Implementing ACF Model
ACF for Classification Problems
ACF for Continuous Output
Linear Regression Model
ACF Model
Calculating Unfairness
Conclusion
Bibliography
Chapter 6: Remove Bias from ML Output
Introduction
Reject Option Classifier
Optimizing the ROC
Handling Multiple Features in ROC
Conclusion
Bibliography
Chapter 7: Accountability in AI
Introduction
Data Drift
Covariate Drift
Jensen-Shannon Distance
Wasserstein Distance
Stability Index
Concept Drift
Kolmogorov–Smirnov Test
Brier Score
Page-Hinkley Test (PHT)
Early Drift Detection Method
Hierarchical Linear Four Rate (HLFR)
Conclusion
Bibliography
Chapter 8: Data and Model Privacy
Introduction
Basic Techniques
Hashing
K-Anonymity, L-Diversity and T-Closeness
Differential Privacy
Privacy Using Exponential Mechanism
Differentially Private ML Algorithms
Federated Learning
Conclusion
Bibliography
Chapter 9: Conclusion
Responsible AI Lifecycle
Responsible AI Canvas
AI and Sustainability
Need for an AI Regulator
Bibliography