Responsible Data Science
Bruce, Peter C., Fleming, Grant
Explore the most serious prevalent ethical issues in data science with this insightful new resource
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.
Peter Bruce founded the Institute for Statistics Education at Statistics.com in 2002. The Institute specializes in introductory and graduate level online education in statistics, optimization, risk modeling, predictive modeling, data mining, and other subjects in quantitative analytics.
Grant Fleming is a Data Scientist at Elder Research Inc. (ERI). During his time at ERI, he has worked with clients in both government and the private sector on statistical testing, data asset creation, predictive analytics, and latent variable modeling. He has given multiple talks on machine learning interpretability and fairness within ERI as well as to outside groups. Internally to ERI, Grant is working on developing software packages for creating reproducible and interpretable black box models.