In this series of blogs, we have been exploring different conceptual and theoretical approaches to information privacy. In the last post, we explored an influential, historical argument by Warren and Brandeis in their paper on the ‘Right to Privacy’, written in a time when anxieties about photographic and print technologies were prevalent. In this post, we examine some of the anxieties and concerns that contemporary data science methods and technologies like machine learning pose to privacy, and theoretical responses to these anxieties in Mireille Hildebrandt’s 2019 paper, ‘Privacy as Protection of the Incomputable Self: From Agnostic to Agonistic Machine Learning’. (Theoretical Inquiries in Law, 20, 83 – 121)
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