Data Science and Designing for Privacy

Techné: Research in Philosophy and Technology 20 (1):51-68 (2016)
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Abstract

Unprecedented advances in the ability to store, analyze, and retrieve data is the hallmark of the information age. Along with enhanced capability to identify meaningful patterns in large data sets, contemporary data science renders many classical models of privacy protection ineffective. Addressing these issues through privacy-sensitive design is insufficient because advanced data science is mutually exclusive with preserving privacy. The special privacy problem posed by data analysis has so far escaped even leading accounts of informational privacy. Here, I argue that accounts of privacy must include norms about information processing in addition to norms about information flow. Ultimately, users need the resources to control how and when personal information is processed and the knowledge to make information decisions about that control. While privacy is an insufficient design constraint, value-sensitive design around control and transparency can support privacy in the information age.

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Michael Falgoust
Tulane University (PhD)

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