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Understand, Manage, and Prevent Algorithmic Bias

A Guide for Business Users and Data Scientists

  • Tobias Baer
Book

Table of contents

  1. Front Matter
    Pages i-xiii
  2. An Introduction to Biases and Algorithms

    1. Front Matter
      Pages 1-1
    2. Tobias Baer
      Pages 3-7
    3. Tobias Baer
      Pages 9-20
    4. Tobias Baer
      Pages 21-27
    5. Tobias Baer
      Pages 29-39
    6. Tobias Baer
      Pages 41-49
  3. Where Does Algorithmic Bias Come From?

    1. Front Matter
      Pages 51-51
    2. Tobias Baer
      Pages 59-68
    3. Tobias Baer
      Pages 69-77
    4. Tobias Baer
      Pages 79-86
    5. Tobias Baer
      Pages 95-106
  4. What to Do About Algorithmic Bias from a User Perspective

    1. Front Matter
      Pages 107-107
    2. Tobias Baer
      Pages 109-115
    3. Tobias Baer
      Pages 117-122
    4. Tobias Baer
      Pages 123-127
    5. Tobias Baer
      Pages 129-160
    6. Tobias Baer
      Pages 167-171
  5. What to Do About Algorithmic Bias from a Data Scientist’s Perspective

    1. Front Matter
      Pages 173-173
    2. Tobias Baer
      Pages 193-208
    3. Tobias Baer
      Pages 209-213
    4. Tobias Baer
      Pages 233-240
  6. Back Matter
    Pages 241-245

About this book

Introduction

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 favor, 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.

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 larger sociological impact of bias in the digital era.

Keywords

algorithmic bias decision bias machine bias machine learning artificial intelligence debiasing predictive modeling biased data statistical modeling sociological

Authors and affiliations

  • Tobias Baer
    • 1
  1. 1.KaufbeurenGermany

Bibliographic information

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