Understand, Manage, and Prevent Algorithmic Bias, 1st ed.
A Guide for Business Users and Data Scientists


Language: Anglais

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260 p. · 15.5x23.5 cm · Paperback

Algorithmic bias can affect us everywhere, from minor trivia such as our social media feed to critical decisions where, say, racial bias can wreak havoc with a person's life dream or a company's survival. Read this interdisciplinary book about algorithmic bias to 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.

Understand, Manage, and Prevent Algorithmic Bias provides a comprehensive background on the psychology of biases?cognitive biases of human decision makers that are mirrored by algorithms as well as the role biases of data scientists play during model development?and demonstrates that overcoming algorithmic bias requires the combination of psychological, statistical, and managerial interventions. Recognizing that most readers may be expert in one dimension but typically not in all three domains, this book is written for the lay reader without losing the depth necessary for recommendations to be actionable. While most writings on algorithmic bias focus on the dangers, the focus of this positive, fun book points toward a path forward where bias is kept at bay and even eliminated.

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

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.-

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.

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