Grant Fleming - Responsible Data Science

1. Chapter 2
2. Chapter 7
3. Chapter 8

1. Chapter 1
2. Chapter 2
3. Chapter 3
4. Chapter 4
5. Chapter 5
6. Chapter 6
7. Chapter 7
8. Chapter 8

Grant Fleming

Peter Bruce

Copyright 2021 by John Wiley & Sons, Inc., Indianapolis, Indiana

Published simultaneously in Canada

ISBN: 978-1-119-74175-6

ISBN: 978-1-119-74177-0 (ebk)

ISBN: 978-1-119-74164-0 (ebk)

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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 Chief 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.

ROBERT DE GRAAF is a data scientist and statistician from Melbourne, Australia. He is the author of Managing Your Data Science Projects and coauthor of SQL Cookbook, 2nd edition. He is husband to Clare and father to Maya and Leda, and enjoys playing guitar and learning new languages.

First and foremost, we acknowledge the support of Elder Research, Inc., and of John Elder (chairman) and Gerhard Pilcher (CEO) in particular. We have benefited greatly from the technical and philosophical conversations we have shared with our colleagues. Elder Research has been most generous in permitting us to pursue this project. At the same time, this book has not been reviewed or edited by the company, and we, the authors, bear sole responsibility for all opinions, errors, and omissions.

We thank, especially, our coauthors on select chapters. Will Goodrum lent his expertise to the legal issues explored in , Auditing for Neural Networks.

Robert de Graaf served as technical editor, raising important points and contributing in many places to a better book. This book certainly would have been incomplete without his input.

Our editorial team at Wiley has been most supportive throughout the process. Jim Minatel, associate publisher, embraced our vision from the beginning. Our editor, Jan Lynn, kept us on track and patiently shepherded the various pieces of the project to all come together. Saravanan Dakshinamurthy handled the production side of things, Louise Watson did the copy-editing, and Pete Gaughan managed the process behind the scenes.

We would like to thank Matthew Dwinnell and Amy Zhang for their tips on working with HTML and CSS, as well as our co-workers Brittany Pugh and Chris Lee for their advice and feedback. Grant would like to thank his professors and mentors, including Edward Munn Sanchez, Lite Nartey, Edward R. Carr, Gregory Magai Patterson, Jennifer Bess, Mark Schaffer, Patrick Jessee, and Brandie Wagner, for their endless support of his efforts and spirit. Peter's appreciation goes to Galit Shmueli, his coauthor on other book projects, with whom he has had lively conversations on ethical issues surrounding the practice of data science.

Finally, we express our appreciation to our students at Statistics.com who have made constructive and useful comments on the material presented here.

In this book, we will review some of the harmful ways artificial intelligence has been used and provide a framework to facilitate the responsible practice of data science. While we will touch upon mitigating legal risks, in this book we will focus primarily on the modeling process itself, especially on how factors overlooked by current modeling practices lead to unintended harms once the model is deployed in a real-world context.

Three core themes will be developed through this book:

• Any AI algorithm can have a harmful, dark side: once they are applied in the real world, AI algorithms can cause any number of harms. An algorithm designed to help police catch murderers can later be appropriated by totalitarian states to persecute dissidents; an algorithm that expands the availability of financial credit for the vast majority of people may nonetheless intensify bias against minorities.