Privacy is an essentially contested concept: a multi-dimensional analytic for mapping privacy

By Deirdre K. Mulligan, Colin Koopman, Nick Doty. 2016

151 citations recorded by the source corpus.

The meaning of privacy has been much disputed throughout its history in response to wave after wave of new technological capabilities and social configurations. The current round of disputes over privacy fuelled by data science has been a cause of despair for many commentators and a death knell for privacy itself for others. We argue that privacy’s disputes are neither an accidental feature of the concept nor a lamentable condition of its applicability. Privacy is essentially contested. Because it is, privacy is transformable according to changing technological and social conditions. To make productive use of privacy’s essential contestability, we argue for a new approach to privacy research and practical design, focused on the development of conceptual analytics that facilitate dissecting privacy’s multiple uses across multiple contexts. This article is part of the themed issue ‘The ethical impact of data science’.

Subfields: Artificial Intelligence, Safety Research, Sociology and Political Science, Privacy & Security, Social Sciences & Humanities

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Citations in the corpus

Measurement and Fairness (2019, background): “agreement. This disagreement marks fairness as as an essentially contested construct, i.e., a construct for which the space of theoretical understandings are inherently con icting and context-specic [25, 48]. Without theoretical agreement, there ought to be no way to assess how well any operationali”
This Thing Called Fairness (2019, methodology): “Together the definitional overview and analytic also help clarify different contexts of usage, so that these ongoing moments of debate can lead to “meaningful contestation” [107] rather than talking past one another.”
Explaining Explanations to Society (2019, background): “Similar recommendations in using explanations in law have been examined in promoting ethics for design [23], for privacy [24], and liability for machines [33].”