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Beschreibung
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.
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.
Über den Autor
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.
Inhaltsverzeichnis
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.
Details
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Allgemeines |
| Genre: | Importe, Wirtschaft |
| Rubrik: | Recht & Wirtschaft |
| Medium: | Buch |
| Inhalt: | Gebunden |
| ISBN-13: | 9781009272209 |
| ISBN-10: | 1009272209 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: |
Spector, Alfred Z
Norvig, Peter Wiggins, Chris |
| Hersteller: | Cambridge University Press |
| Maße: | 248 x 174 x 23 mm |
| Von/Mit: | Alfred Z Spector (u. a.) |
| Erscheinungsdatum: | 20.10.2022 |
| Gewicht: | 0,698 kg |
Über den Autor
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.
Inhaltsverzeichnis
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.
Details
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Allgemeines |
| Genre: | Importe, Wirtschaft |
| Rubrik: | Recht & Wirtschaft |
| Medium: | Buch |
| Inhalt: | Gebunden |
| ISBN-13: | 9781009272209 |
| ISBN-10: | 1009272209 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: |
Spector, Alfred Z
Norvig, Peter Wiggins, Chris |
| Hersteller: | Cambridge University Press |
| Maße: | 248 x 174 x 23 mm |
| Von/Mit: | Alfred Z Spector (u. a.) |
| Erscheinungsdatum: | 20.10.2022 |
| Gewicht: | 0,698 kg |
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