Ethical Data Science: Prediction in the Public Interest (Oxford Technology Law and Policy)
Language: English
Published by Oxford University Press, 2023
- Hardcover
- Used
Condition: Used - Very good
£ 19.64
Quantity: 1 available
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Complete and clean. All pages present and readable, binding intact. Highlighting or writing is absent or limited to a handful of pages. No water damage. Cover and spine may show light shelf wear, edge bumping, or minor creasing. May have a remainder mark, stickers or sticker residue, or a previous owner's name. Access codes, CDs, DVDs, and other bundled supplements may not be included and should be assumed used or missing unless the listing explicitly states otherwise.
Seller Inventory # C2257B00894U
- Title
- Ethical Data Science: Prediction in the Public Interest (Oxford Technology Law and Policy)
- Author
- Washington, Anne L.
- Publisher
- Oxford University Press
- Publication year
- 2023
- Condition
- Very Good
- Binding
- hardcover
- Language
- English
- ISBN 10
- 0197693024
- ISBN 13
- 9780197693025
- Item weight
- 14 ounces
- Dimensions
- 6x1x8
Ethical Data Science empowers those striving to create predictive data technologies that benefit more people. As one of the first books on public interest technology, it provides a starting point for anyone who wants human values to counterbalance the institutional incentives that drive computational prediction. It argues that data science prediction embeds administrative preferences that often ignore the disenfranchised. The book introduces the prediction supply chain to highlight moral questions alongside the interlocking legal and commercial interests influencing data science. Structured around a typical data science workflow, the book systematically outlines the potential for more nuanced approaches to transforming data into meaningful patterns. Drawing on arts and humanities methods, it encourages readers to think critically about the full human potential of data science step-by-step. Situating data science within multiple layers of effort exposes dependencies while also pinpointing opportunities for research ethics and policy interventions.
This approachable process lays the foundation for broader conversations with a wide range of audiences. Practitioners, academics, students, policy makers, and legislators can all learn how to identify social dynamics in data trends, reflect on ethical questions, and deliberate over solutions. The book proves the limits of predictive technology controlled by the few and calls for more inclusive data science.
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