The increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of “Black box” algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair.

Responsible Data Science delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to:

Improve model transparency, even for black box models
Diagnose bias and unfairness within models using multiple metrics
Audit projects to ensure fairness and minimize the possibility of unintended harm

Perfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians.

About the Author

GRANT FLEMING is a Data Scientist at Elder Research Inc. His professional focus is on machine learning for social science applications, model interpretability, civic technology, and building software tools for reproducible data science.

PETER BRUCE is the Senior Learning Officer at Elder Research, Inc., author of several best-selling texts on data science, and Founder of the Institute for Statistics Education at Statistics.com, an Elder Research Company.

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