Understand, Manage, and Prevent Algorithmic Bias

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 biasesΓÇöcognitive biases of human decision makers that are mirrored by algorithms as well as the role biases of data scientists play during model developmentΓÇöand 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 solutionBe 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