Part I: An Introduction to Biases and Algorithms
Chapter 2: Bias in Human Decision-Making
Chapter 3: How Algorithms Debias Decisions
Chapter 4: The Model Development Process
Chapter 5: Machine Learning in a Nutshell
Part II: Where Does Algorithmic Bias Come From?
Chapter 6: How Real World Biases Will Be Mirrored by Algorithms
Chapter 7: Data Scientists' Biases
Chapter 8: How Data Can Introduce Biases
Chapter 9: The Stability Bias of Algorithms
Chapter 10: Biases Introduced by the Algorithm Itself
Chapter 11: Algorithmic Biases and Social Media
Part III: What to Do About Algorithmic Bias from a User Perspective
Chapter 12: Options for Decision-Making
Chapter 13: Assessing the Risk of Algorithmic Bias
Chapter 14: How to Use Algorithms Safely
Chapter 15: How to Detect Algorithmic Biases
Chapter 16: Managerial Strategies for Correcting Algorithmic Bias
Chapter 17: How to Generate Unbiased Data
Part IV: What to Do About Algorithmic Bias from a Data Scientist's Perspective
Chapter 18: The Data Scientist's Role in Overcoming Algorithmic Bias
Chapter 19: An X-Ray Exam of Your Data
Chapter 20: When to Use Machine Learning with Traditional Methods
Chapter 21: How to Marry Machine Learning with Traditional Methods
Chapter 22: How to Prevent Bias in Self-Improving Models
Chapter 23: How to Institutionalize Debiasing.
