Is Big Data challenging criminology?

The advent of ‘Big Data’ and machine learning algorithms is predicted to transform how we work and think. Specifically, it is said that the capacity of Big Data analytics to move from sampling to census, its ability to deal with messy data and the demonstrated utility of moving from causality to cor...

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Vydáno v:Theoretical criminology Ročník 20; číslo 1; s. 21 - 39
Hlavní autoři: Chan, Janet, Bennett Moses, Lyria
Médium: Journal Article
Jazyk:angličtina
Vydáno: London, England SAGE Publications 01.02.2016
Sage Publications Ltd
Témata:
Algorithms
Artificial intelligence
Big Data
Causality
Censuses
Correlation analysis
Criminology
Literature reviews
Machine learning
Predictions
Sampling
Social sciences
Big Data analytics
causality and correlation
machine learning
predictive policing
criminological theory and research
ISSN:1362-4806, 1461-7439
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Shrnutí:The advent of ‘Big Data’ and machine learning algorithms is predicted to transform how we work and think. Specifically, it is said that the capacity of Big Data analytics to move from sampling to census, its ability to deal with messy data and the demonstrated utility of moving from causality to correlation have fundamentally changed the practice of social sciences. Some have even predicted the end of theory—where the question why is replaced by what—and an enduring challenge to disciplinary expertise. This article critically reviews the available literature against such claims and draws on the example of predictive policing to discuss the likely impact of Big Data analytics on criminological research and policy.
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ISSN:1362-4806
1461-7439
DOI:10.1177/1362480615586614