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Journal article

The law and Big Data

Abstract:

In this article we critically examine the use of Big Data in the legal system. Big Data is driving a trend towards behavioral optimization and “personalized law,” in which legal decisions and rules are optimized for best outcomes and where law is tailored to individual consumers based on analysis of past data. Big Data, however, has serious limitations and dangers when applied in the legal context. Advocates of Big Data make theoretically problematic assumptions about the objectivity of data and scientific observation. Law is always theory-laden. Although Big Data strives to be objective, law and data have multiple possible meanings and uses and thus require theory and interpretation in order to be applied. Further, the meanings and uses of law and data are indefinite and continually evolving in ways that cannot be captured or predicted by Big Data.


Due to these limitations, the use of Big Data will likely generate unintended consequences in the legal system. Large-scale use of Big Data will create distortions that adversely influence legal decision-making, causing irrational herding behaviors in the law. The centralized nature of the collection and application of Big Data also poses serious threats to legal evolution and democratic accountability. Furthermore, its focus on behavioral optimization necessarily restricts and even eliminates the local variation and heterogeneity that makes the legal system adaptive. In all, though Big Data has legitimate uses, this article cautions against using Big Data to replace independent legal judgment.

Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
SSD
Department:
Said Business School
Role:
Author


Publisher:
Cornell Law School
Journal:
Cornell Journal of Law and Public Policy More from this journal
Volume:
27
Issue:
2
Pages:
3
Publication date:
2018-03-01
Acceptance date:
2017-04-15
ISSN:
1069-0565


Pubs id:
pubs:697664
UUID:
uuid:d79587de-f8fc-4759-94ed-83db1db7618f
Local pid:
pubs:697664
Source identifiers:
697664
Deposit date:
2017-05-30
ARK identifier:

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