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

Deirdre K Mulligan1, Colin Koopman2, Nick Doty3

  • 1University of California, Berkeley, School of Information, and Berkeley Center for Law & Technology, Berkeley, CA 94720, USA dmulligan@berkeley.edu.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|March 25, 2017
PubMed
Summary
This summary is machine-generated.

Privacy is inherently contested, evolving with technology and society. This essential contestability allows for new conceptual analytics to understand and design for privacy across diverse contexts, particularly in data science.

Keywords:
data sciencedesignprivacyprivacy analyticprivacy by designvalues in design

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Area of Science:

  • Social Sciences
  • Information Science
  • Ethics

Background:

  • The concept of privacy has historically evolved in response to technological advancements and societal changes.
  • Current debates surrounding privacy are significantly influenced by the rise of data science, leading to widespread concern about its future.

Purpose of the Study:

  • To reframe privacy not as a static concept but as essentially contested.
  • To propose a new approach for privacy research and design that leverages its inherent contestability.

Main Methods:

  • Conceptual analysis of privacy's historical disputes and transformations.
  • Argument for developing conceptual analytics to dissect privacy's multifaceted applications.

Main Results:

  • Privacy is fundamentally an "essentially contested concept," meaning its meaning and application are perpetually debated and redefined.
  • This contestability is not a flaw but a feature that enables privacy's adaptation to new technological and social landscapes.

Conclusions:

  • Privacy's essential contestability should be embraced as a driver for innovation in privacy research and design.
  • Developing conceptual analytics is crucial for understanding and managing the diverse uses of privacy in various contexts, especially within data science applications.