rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Ethical data science
    sub title: prediction in the public interest
  name:
    name part: Washington, Anne L.
    role:
      role term: creator
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: nyu
    date issued: 2023
    copyright date: 2023
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Can data science truly serve the public interest? Data-driven analysis shapes many interpersonal, consumer, and cultural experiences yet scientific solutions to social problems routinely stumble. All too often, predictions remain solely a technocratic instrument that sets financial interests against service to humanity. Amidst a growing movement to use science for positive change, Anne L. Washington offers a solution-oriented approach to the ethical challenges of data science. 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.
  abstract: Amidst a growing movement to use science for positive change, 'Ethical Data Science' offers a solution-oriented approach to the ethical challenges of data science. 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.
  table of contents: Prologue: Tracing ethics in the prediction supply chain -- Source : data are people too -- Model : dear validity, advice for wayward algorithms -- Compare : no comparison without representation -- Optimize : data science reasoning -- Learn : for good -- Show us your work or someone gets hurt -- Conclusion: Prediction in the public interest.
  note: Anne L. Washington, PHD.
  note: Includes bibliographical references and index.
  subject:
    topic: Data mining
    topic: Moral and ethical aspects
  subject:
    topic: Predictive analytics
    topic: Moral and ethical aspects
  subject:
    topic: Machine learning
    topic: Moral and ethical aspects
  subject:
    topic: Public interest
  subject:
    topic: Exploration de données (Informatique)
    topic: Aspect moral
  subject:
    topic: Intérêt public
  subject:
    topic: Apprentissage automatique
    topic: Aspect moral
  subject:
    topic: public interest
  subject:
    topic: Communications
  subject:
    topic: LAW
  subject:
    topic: Ethics & Professional Responsibility
  subject:
    topic: Science & Technology
  subject:
    topic: Computer & Internet
  subject:
    topic: Public interest
  subject:
    topic: Graphical & digital media applications
  subject:
    topic: Computers and IT
  classification: QA76.9.D343 W375 2023
  classification: 006.312
  related item:
    title info:
      title: Ethical data science
    name:
      name part: Washington, Anne L.
    origin info:
      publisher: New York, NY : Oxford University Press, [2023]
    identifier: (DLC) 2023032900
    identifier: (OCoLC)1386702376
  related item:
    identifier: (DLC) 2023032900
    identifier: (OCoLC)1386702376
  related item:
    title info:
      title: Oxford technology law and policy
  identifier: 9780197693063
  identifier: 0197693067
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  identifier: 9780197693025
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  identifier: 9780197693032
  identifier: 0197693032
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: STBDS
    record creation date: 230929
    record change date: 20260318170133.4
    record identifier: in00005698972
    language of cataloging:
      language term: eng