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Cited by 320Ethics and Social Impacts of AIPrivacy, Security, and Data ProtectionDigitalization, Law, and Regulation

Big Data’s End Run around Anonymity and Consent

Barocas, Solon, Nissenbaum, Helen·2014·Privacy, Big Data, and the Public Good
DOI 10.1017/cbo9781107590205.004
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Abstract

Introduction Big data promises to deliver analytic insights that will add to the stock of scientific and social scientific knowledge, significantly improve decision making in both the public and private sector, and greatly enhance individual self-knowledge and understanding. They have already led to entirely new classes of goods and services, many of which have been embraced enthusiastically by institutions and individuals alike. And yet, where these data commit to record details about human behavior, they have been perceived as a threat to fundamental values, including everything from autonomy, to fairness, justice, due process, property, solidarity, and, perhaps most of all, privacy. Given this apparent conflict, some have taken to calling for outright prohibitions on various big data practices, while others have found good reason to finally throw caution (and privacy) to the wind in the belief that big data will more than compensate for its potential costs. Still others, of course, are searching for a principled stance on privacy that offers the flexibility necessary for these promises to be realized while respecting the important values that privacy promotes. This is a familiar situation because it rehearses many of the long-standing tensions that have characterized each successive wave of technological innovation over the past half-century and their inevitable disruption of constraints on information flows through which privacy had been assured. It should come as no surprise that attempts to deal with new threats draw from the toolbox assembled to address earlier upheavals. Ready-to-hand, anonymity and informed consent remain the most popular tools for relieving these tensions – tensions that we accept, from the outset, as genuine and, in many cases, acute. Taking as a given that big data implicates important ethical and political values, we direct our focus instead on attempts to avoid or mitigate the conflicts that may arise. We do so because the familiar pair of anonymity and informed consent continues to strike many as the best and perhaps only way to escape the need to actually resolve these conflicts one way or the other.

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  • Consent can serve as a loophole to circumvent critical questions about what and whose values a given information flow protects or threatens.

    “Consent can serve, in this way, as a loophole to circumvent critical questions about what and whose values a given information flow protects or threatens (Barocas and Nissenbaum, 2014).”

    From Algorithmic harms and digital ageism in the use of surveillance technologies in nursing homes (2022) · cites this paper as Barocas and Nissenbaum, 2014

  • Corporate personal data practices threaten a diverse range of intersecting values and rights including autonomy, fairness, equality, democratic sovereignty, due process, and property.

    “corporate personal data practices also threaten a diverse range of intersecting values and rights including autonomy, fairness, equality, democratic sovereignty, due process, and property”

    From Social Injustice in Surveillance Capitalism (2017) · cites this paper as Barocas and Nissenbaum 2014

  • Big data algorithms and data mining are fundamentally about discrimination; their purpose is to separate society into groups through identifying patterns of difference and sameness (norms and deviations) in vast datasets through data reduction and categorization.

    “Big data algorithms and data mining are fundamentally about discrimination (see Barocas 2014); their purpose is to separate society into groups through identifying patterns of difference and sameness-or norms and deviations from them (Amoore 2009)-in vast datasets through processes of data reduction and categorization.”

    From Social Injustice in Surveillance Capitalism (2017) · cites this paper as Barocas 2014

  • Volunteered information from a few people on social media can unlock the same undisclosed information about many through social network analysis and pattern recognition, creating a 'tyranny of the minority'.

    “Likewise, the volunteered information of a few people on social media can unlock the same undisclosed information about the many through social network analysis and pattern recognition, creating what Barocas & Nissenbaum [51, p. 61] term the 'the tyranny of the minority'.”

    From The ethics of smart cities and urban science (2016) · cites this paper as [51]

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Authors
Barocas, S., & Nissenbaum, H.
Affiliations
New York University (US)
ORCID
Venue
Privacy, Big Data, and the Public Good
Publisher
Cambridge University Press
Published
2014-06-09
Pages
44-75
DOI
10.1017/cbo9781107590205.004
OpenAlex
W817684483
Cited by
320
References
77
Citations by year
2026: 15 · 2025: 18 · 2024: 27 · 2023: 30 · 2022: 35 · 2021: 26 · 2020: 50 · 2019: 33 · 2018: 30 · 2017: 24 · 2016: 24 · 2015: 6 · 2014: 2
FWCI
90.16
Type
book chapter
Language
en
Open access
closed
Licence
www.cambridge.org/core/terms (unspecified, 26 d)
Locations
Cambridge University Press eBooks (publishedVersion)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
  • Social Sciences › Law › Digitalization, Law, and Regulation
Keywords
big data, privacy, anonymity, informed consent, data ethics, personal data, autonomy, information flow, privacy preservation, technological innovation

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