Data science is the foundation of our modern world. It underlies applications used by billions of people every day, providing new tools, forms of entertainment, economic growth, and potential solutions to difficult, complex problems. These opportunities come with significant societal consequences, raising fundamental questions about issues such as data quality, fairness, privacy, and causation. In this book, four leading experts convey the excitement and promise of data science and examine the major challenges in gaining its benefits and mitigating its harms. They offer frameworks for critically evaluating the ingredients and the ethical considerations needed to apply data science productively, illustrated by extensive application examples. The authors' far-ranging exploration of these complex issues will stimulate data science practitioners and students, as well as humanists, social scientists, scientists, and policy makers, to study and debate how data science can be used more effectively and more ethically to better our world.
Norvig, Peter: - Peter Norvig is a Distinguished Education Fellow at Stanford's Human-Centered Artificial Intelligence Institute and a researcher at Google; previously he directed Google's core search algorithms group and Google's research group. He has taught at the University of Southern California, Stanford University, and the University of California at Berkeley, from which he received a Ph.D. in 1986 and the distinguished alumni award in 2006. He was co-teacher of an Artificial Intelligence class that signed up 160,000 students, helping to kick off the current round of massive open online classes. His books include Artificial Intelligence: A Modern Approach (the leading textbook in the field) and Paradigms of AI Programming: Case Studies in Common Lisp. He is also the author of the Gettysburg Powerpoint Presentation and the world's longest palindromic sentence. He is a fellow of the AAAI, ACM, California Academy of Science and American Academy of Arts & Sciences.
Spector, Alfred Z.: - Alfred Z. Spector is a technologist and research leader. His career has led him from innovation in large scale, networked computing systems (at Stanford, CMU, and his company, Transarc) to broad research leadership: first leading IBM Software Research and then Google Research. Following Google, he was the CTO at Two Sigma Investments, and he is presently a Visiting Scholar at MIT. In addition to his managerial career, Dr. Spector lectured widely on the growing importance of computer science across all disciplines (CS+X) and on the Societal Implications of Data Science. He is a fellow of the ACM, IEEE, and the American Academy of Arts and Sciences, and a member of the National Academy of Engineering. Dr. Spector won the 2001 IEEE Kanai Award for Distributed Computing, was co-awarded the 2016 ACM Software Systems Award, and was a Phi Beta Kappa Visiting Scholar. He received a Ph.D. in Computer Science from Stanford and an A.B. in Applied Mathematics from Harvard.
Wiggins, Chris: - Chris Wiggins is an Associate Professor of Applied Mathematics at Columbia University and the Chief Data Scientist at The New York Times. At Columbia he is a founding member of the executive committee of the Data Science Institute, and of the Department of Applied Physics and Applied Mathematics as well as the Department of Systems Biology, and is affiliated faculty in Statistics. He is a co-founder and co-organizer of hackNY (http: //hackNY.org), a non-profit which since 2010 has organized once a semester student hackathons, and the hackNY Fellows Program, a structured summer internship at NYC startups. Prior to joining the faculty at Columbia he was a Courant Instructor at NYU (1998-2001) and earned his Ph.D. at Princeton University (1993-1998) in theoretical physics. He is a Fellow of the American Physical Society and is a recipient of Columbia's Avanessians Diversity Award.
