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: Data science ethics
    sub title: concepts, techniques, and cautionary tales
  name:
    name part: Martens, David
    name part: (Professor of data science)
    role:
      role term: creator
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2022
    copyright date: 2022
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xii, 255 pages) : illustrations
  abstract: "Data science ethics is all about what is right and wrong when conducting data science. Data science has so far mainly been used for positive outcomes for businesses and society. However, just as with any technology, data science has also come with some negative consequences: an increase of privacy invasion, data-driven discrimination against sensitive groups, and decision making by complex models without explanations. This book looks at the different concepts and techniques related to data science ethics. Data scientists and business managers are not inherently unethical, but at the same time not trained to think this through either. This book aims to address this important gap. The techniques discussed range from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, measurements to assess, and techniques to remove discrimination against sensitive groups, and various explainable AI techniques. The real-life cautionary tales further illustrate the importance and potential impact of data science ethics, including tales of racist bots, search censoring, government backdoors, discrimination in recruitment, predicting pregnancy, redlining, re-identification of persons based on movie viewing and location data, cheating academics, and face recognition. Additionally, 10 discussions are provided with hypothetical scenarios, for example:‘you are the founder of a startup …', which then present an ethical dilemma related to data science. These can serve as structured exercises to be completed with your fellow colleagues, and will teach you how to balance the ethical concerns and the utility of your data"--Publisher's description.
  table of contents: Introduction to data science ethics -- Ethical data gathering -- Ethical data preprocessing -- Ethical modelling -- Ethical evaluation -- Ethical deployment.
  note: David Martens.
  note: Includes bibliographical references and index.
  subject:
    topic: Big data
    topic: Moral and ethical aspects
  subject:
    topic: Data mining
    topic: Moral and ethical aspects
  subject:
    topic: Données volumineuses
    topic: Aspect moral
  subject:
    topic: Exploration de données (Informatique)
    topic: Aspect moral
  subject:
    topic: Intelligence (AI) & Semantics
  subject:
    topic: COMPUTERS
  subject:
    topic: Ethics & Moral Philosophy
  subject:
    topic: PHILOSOPHY
  subject:
    topic: Big data
  subject:
    topic: Data mining
  classification: QA76.9.B45 M37 2022
  classification: 005.7
  related item:
    title info:
      title: Data science ethics
    name:
      name part: Martens, David.
    origin info:
      publisher: Oxford, United Kingdom : Oxford University Press, [2022]
    identifier: (OCoLC)1273469063
  identifier: 9780192663023
  identifier: 019266302X
  identifier: 9780191939655
  identifier: 019193965X
  identifier: 9780192847263
  identifier: 0192847260
  identifier: 0192847279
  identifier: 9780192847270
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: N$T
    record creation date: 220425
    record change date: 20260318164131.3
    record identifier: in00005696954
    language of cataloging:
      language term: eng