Ethical Data Science

Amidst a growing movement to use science for positive change, Ethical Data Science offers a solution-oriented approach to the ethical challenges of data science. As one of the first books on public interest technology, it provides a starting point for anyone who wants human values to counterbalance the institutional incentives that drive computational prediction.

Anne L. Washington PhD leverages her expertise in government data to improve technology policy. She is a computer science graduate of Brown University, with a master's degree in library and information science (MLIS) from Rutgers University, as well as a doctorate from The George Washington University School of Business in Information Systems and Technology Management. Before her academic career, she worked at Barclays Global Investors, the Library of Congress, and Apple Computers. The US National Science Foundation has recognized her research multiple times, and she is the recipient of a 2018 NSF CAREER award.

Introduction: Ethical data science
Prologue: Tracking ethics in a prediction supply chain
1: SOURCE - Data are people too
2: MODEL - Dear validity: Advice for wayward algorithms
3: COMPARE - Category hacking
4: OPTIMIZE - Data science reasoning
5: LEARN - For good
6: Show us your work or someone gets hurt
7: Prediction in the public interest
References
Index