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

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


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.

Original languageEnglish
Title of host publicationMachine Learning and the City
Subtitle of host publicationApplications in Architecture and Urban Design
Number of pages12
ISBN (Electronic)9781119815075
StatePublished - May 27 2022

Bibliographical note

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


  • Auditability
  • Big data research methods
  • Ethical research
  • Human participants
  • Research community
  • Steadfast recognition

ASJC Scopus subject areas

  • General Engineering
  • General Arts and Humanities


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