Understand, manage, and prevent algorithmic bias: a guide for business users and data scientists

Table of Contents

Part I: An Introduction to Biases and Algorithms
Chapter 1: Introduction
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
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.

Are algorithms friend or foe?
The human mind is evolutionarily designed to take shortcuts in order to survive. We jump to conclusions because our brains want to keep us safe. A majority of our biases work in our favour, such as when we feel a car speeding in our direction is dangerous and we instantly move, or when we decide not take a bite of food that appears to have gone bad. However, inherent bias negatively affects work environments and the decision-making surrounding our communities. While the creation of algorithms and machine learning attempts to eliminate bias, they are, after all, created by human beings, and thus are susceptible to what we call algorithmic bias.
In Understand, Manage, and Prevent Algorithmic Bias, author Tobias Baer helps you understand where algorithmic bias comes from, how to manage it as a business user or regulator, and how data science can prevent bias from entering statistical algorithms. Baer expertly addresses some of the 100+ varieties of natural bias such as confirmation bias, stability bias, pattern-recognition bias, and many others. Algorithmic bias mirrors—and originates in—these human tendencies. Baer dives into topics as diverse as anomaly detection, hybrid model structures, and self-improving machine learning.
While most writings on algorithmic bias focus on the dangers, the core of this positive, fun book points toward a path where bias is kept at bay and even eliminated. You’ll come away with managerial techniques to develop unbiased algorithms, the ability to detect bias more quickly, and knowledge to create unbiased data. Understand, Manage, and Prevent Algorithmic Bias is an innovative, timely, and important book that belongs on your shelf. Whether you are a seasoned business executive, a data scientist, or simply an enthusiast, now is a crucial time to be educated about the impact of algorithmic bias on society and take an active role in fighting bias.

https://www.apress.com/gp/book/9781484248843