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Ten simple rules for responsible big data research

  • Matthew Zook
  • , Solon Barocas
  • , Danah Boyd
  • , Kate Crawford
  • , Emily Keller
  • , Seeta Peña Gangadharan
  • , Alyssa Goodman
  • , Rachelle Hollander
  • , Barbara A. Koenig
  • , Jacob Metcalf
  • , Arvind Narayanan
  • , Alondra Nelson
  • , Frank Pasquale

Producción científica: Chapter › revisión exhaustiva

Resumen

The use of big data research methods has grown tremendously in both academia and industry. One of the most fundamental rules of responsible big data research is the steadfast recognition that most data represent or impact people. For some projects, sharing data is an expectation of the human participants involved and thus a key part of ethical research. Research involving human participants at federally funded institutions is governed by IRBs charged with preventing harm through well-established procedures and are familiar to many researchers. Although codes of conduct will vary depending on the topic and research community, a particularly important element is designing data and systems for auditability. Responsible internal auditing processes flow easily into audit systems and also keep track of factors that might contribute to problematic outcomes.

Idioma originalEnglish
Título de la publicación alojadaMachine Learning and the City
Subtítulo de la publicación alojadaApplications in Architecture and Urban Design
Páginas523-534
Número de páginas12
ISBN (versión digital)9781119815075
EstadoPublished - may 27 2022

Nota bibliográfica

Publisher Copyright:
© 2022 John Wiley & Sons, Inc. All rights reserved.

ASJC Scopus subject areas

  • General Engineering
  • General Arts and Humanities

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