Understand, Manage, and Prevent Algorithmic Bias

A Guide for Business Users and Data Scientists

Authors: Baer, Tobias

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  • Teaches the many sources of algorithmic bias and shows the holistic measures you can use to manage and prevent bias
  • Provides practical, proven techniques to effectively combat and eliminate bias
  • Addresses both basic statistical concepts such as logistic regression and advanced techniques such as neural networks
  • Discusses the impact of bias on society and possible regulatory responses
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eBook $24.99
price for Brazil (gross)
  • ISBN 978-1-4842-4885-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $34.99
price for Brazil
  • ISBN 978-1-4842-4884-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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 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. 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.


What You'll Learn

  • Study the many sources of algorithmic bias, including cognitive biases in the real world, biased data, and statistical artifact
  • Understand the risks of algorithmic biases, how to detect them, and managerial techniques to prevent or manage them
  • Appreciate how machine learning both introduces new sources of algorithmic bias and can be a part of a solution
  • Be familiar with specific statistical techniques a data scientist can use to detect and overcome algorithmic bias


Who This Book is For

Business executives of companies using algorithms in daily operations; data scientists (from students to seasoned practitioners) developing algorithms; compliance officials concerned about algorithmic bias; politicians, journalists, and philosophers thinking about algorithmic bias in terms of its impact on society and possible regulatory responses; and consumers concerned about how they might be affected by algorithmic bias

About the authors

Tobias Baer is a data scientist, psychologist, and top management consultant with over 20 years of experience in risk analytics. Until June 2018, he was Master Expert and Partner at McKinsey & Co., Inc., where he built McKinsey's Risk Advanced Analytics Center of Competence in India in 2004, led the Credit Risk Advanced Analytics Service Line globally, and served clients in over 50 countries on topics such as the development of analytical decision models for credit underwriting, insurance pricing, and tax enforcement, as well as debiasing decisions. Tobias has been pursuing a research agenda around analytics and decision making both at McKinsey (e.g., on debiasing judgmental decisions and on leveraging machine learning to develop highly transparent predictive models) and at University of Cambridge, UK (e.g., the effect of mental fatigue on decision bias).

Tobias holds a PhD in finance from University of Frankfurt, an MPhil in psychology from University of Cambridge, an MA in economics from UWM, and has done  undergraduate studies in business administration and law at University of Giessen. He started publishing as a teenager, writing about programming tricks for the Commodore C64 home computer in a German software magazine, and now blogs regularly on his LinkedIn page.

Table of contents (23 chapters)

Table of contents (23 chapters)
  • Introduction

    Pages 3-7

    Baer, Tobias

  • Bias in Human Decision-Making

    Pages 9-20

    Baer, Tobias

  • How Algorithms Debias Decisions

    Pages 21-27

    Baer, Tobias

  • The Model Development Process

    Pages 29-39

    Baer, Tobias

  • Machine Learning in a Nutshell

    Pages 41-49

    Baer, Tobias

Buy this book

eBook $24.99
price for Brazil (gross)
  • ISBN 978-1-4842-4885-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $34.99
price for Brazil
  • ISBN 978-1-4842-4884-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.

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Bibliographic Information

Bibliographic Information
Book Title
Understand, Manage, and Prevent Algorithmic Bias
Book Subtitle
A Guide for Business Users and Data Scientists
Authors
Copyright
2019
Publisher
Apress
Copyright Holder
Tobias Baer
eBook ISBN
978-1-4842-4885-0
DOI
10.1007/978-1-4842-4885-0
Softcover ISBN
978-1-4842-4884-3
Edition Number
1
Number of Pages
XIII, 245
Number of Illustrations
1 b/w illustrations
Topics