Prediction, Preemption, Presumption: How Big Data Threatens Big Picture Privacy

8 Pages Posted: 12 Jun 2019

See all articles by Ian R. Kerr

Ian R. Kerr

University of Ottawa - Common Law Section

Jessica Earle


Date Written: September 3, 2013


Big Data has become a familiar concept in legal and social scientific literature and debate. This paper explores the nature of Big Data by examining its intersection with consequential, preferential, and preemptive predictions. The authors address how fundamental jurisprudential principles, including the presumption of innocence and the associated privacy and due process values are threatened by an overreliance on Big Data and the way it is put to use in making preemptive predictions. While the authors acknowledge the benefits of big data, they question whether the trade-off is worth it in light of the resultant undesirable social consequences.

Ultimately, the employment of Big Data by corporations, governmental entities, and individuals can replace proof with mere prediction. In order to mitigate potential negative outcomes, the authors maintain that subjects of preemptive predictions must be able to scrutinize and contest projections and assumptions about themselves.

Keywords: big data, privacy, privacy law, preemptive predictions

Suggested Citation

Kerr, Ian R. and Earle, Jessica, Prediction, Preemption, Presumption: How Big Data Threatens Big Picture Privacy (September 3, 2013). Stanford Law Review, Vol. 66, No. 65, 2013, Available at SSRN:

Ian R. Kerr (Contact Author)

University of Ottawa - Common Law Section ( email )

57 Louis Pasteur Street
Ottawa, K1N 6N5
613-562-5800 (Phone)

Jessica Earle

CTPL ( email )


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