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Data Science in Context: Foundations, Challenges, Opportunities

Data Science in Context: Foundations, Challenges, Opportunities

Data Science in Context: Foundations, Challenges, Opportunities

Data Science in Context: Foundations, Challenges, Opportunities


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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.

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Product Details

ISBN-13: 9781009272209
Publisher: Cambridge University Press
Publication date: 10/20/2022
Pages: 335
Product dimensions: 6.85(w) x 9.84(h) x 0.87(d)

About the Author

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.

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.

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 (, 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.

Jeannette M. Wing is the Executive Vice President for Research and Professor of Computer Science at Columbia University. She joined Columbia in 2017 as the inaugural Avanessians Director of the Data Science Institute. From 2013 to 2017, she was a Corporate Vice President of Microsoft Research. She twice served as the Head of the Computer Science Department at Carnegie Mellon University, where she had been on the faculty since 1985. From 2007–2010 she was the Assistant Director of the Computer and Information Science and Engineering Directorate at the National Science Foundation. Professor Wing's current research focus is on trustworthy AI. She is known for her research contributions in security and privacy, programming languages, and concurrent and distributed systems. Her 2006 seminal essay, titled 'Computational Thinking,' is credited with helping to establish the centrality of computer science to problem-solving in fields where previously it had not been embraced. She received the Computing Research Association Distinguished Service Award in 2011 and the ACM Distinguished Service Award in 2014. She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, ACM, and IEEE. She received her S.B., S.M., and Ph.D. degrees in Computer Science from MIT.

Table of Contents

Introduction; Part I. Data Science: 1. Foundations of data science; 2. Data science is transdisciplinary; 3. A framework for ethical considerations; Recap of Part I – Data Science; Part II. Applying Data Science: 4. Data science applications: six examples; 5. The analysis rubric; 6. Applying the analysis rubric; 7. A principlist approach to ethical considerations; Recap of Part II – Transitioning from Examples and Learnings to Challenges; Part III. Challenges in Applying Data Science: 8. Tractable data; 9. Building and deploying models; 10. Dependability; 11. Understandability; 12. Setting the right objectives; 13. Toleration of failures; 14. Ethical, legal, and societal challenges; Recap of Part III – Challenges in Applying Data Science; Part IV. Addressing Concerns: 15. Societal concerns; 16. Education and intelligent discourse; 17. Regulation; 18. Research and development; 19. Quality and ethical governance; Recap of Part IV – Addressing Concerns: 20. Concluding thoughts; Appendix. Summary of recommendations from Part IV; About the authors; References; Index.

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