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Cited by 53Policing Practices and PerceptionsCriminal Law and EvidenceEthics and Social Impacts of AI

Combatting Police Discrimination in the Age of Big Data

Open access
Goel, Sharad, Perelman, Maya, Shroff, Ravi, Sklansky, David Alan·2017·New Criminal Law Review
DOI 10.1525/nclr.2017.20.2.181
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Abstract

The exponential growth of available information about routine police activities offers new opportunities to improve the fairness and effectiveness of police practices. We illustrate the point by showing how a particular kind of calculation made possible by modern, large-scale datasets—determining the likelihood that stopping and frisking a particular pedestrian will result in the discovery of contraband or other evidence of criminal activity—could be used to reduce the racially disparate impact of pedestrian searches and to increase their effectiveness. For tools of this kind to achieve their full potential in improving policing, though, the legal system will need to adapt. One important change would be to understand police tactics such as investigatory stops of pedestrians or motorists as programs, not as isolated occurrences. Beyond that, the judiciary will need to grow more comfortable with statistical proof of discriminatory policing, and the police will need to be more receptive to the assistance that algorithms can provide in reducing bias.

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Crossref · 10.1525/nclr.2017.20.2.181

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  • These stops and searches are less likely to lead to a finding of contraband or arrest, indicating poorer accuracy (lower hit rates) when young minority men are targeted.

    “These stops and searches are less likely to lead to a finding of contraband or arrest, indicating poorer accuracy (lower hit rates) when young minority men are targeted (Gelman, Fagan, and Kiss 2007;Goel et al. 2017).”

    From Body Cameras, Big Data, and Police Accountability (2018) · cites this paper as Goel et al. 2017

  • Data from New York indicated that blacks and Hispanics are more likely to be stopped and frisked than whites, after controlling for precinct and rates of crime commission.

    “Data from New York, one of the jurisdictions that collected stop and frisk data because of a court order, indicated that blacks and Hispanics are more likely to be stopped and frisked than whites, after controlling for precinct and rates of crime commission (Gelman, Fagan, and Kiss 2007;Goel et al. 2017).”

    From Body Cameras, Big Data, and Police Accountability (2018) · cites this paper as Goel et al. 2017

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Authors
Goel, S., Perelman, M., Shroff, R., & Sklansky, D. A.
Affiliations
  • Stanford University (US)
  • New York University (US)
ORCID
Venue
New Criminal Law Review
Publisher
University of California Press
Published
2017
Volume
20
Issue
2
Pages
181-232
ISSN
  • 1933-4192 (ISSN-L)
  • 1933-4206
DOI
10.1525/nclr.2017.20.2.181
OpenAlex
W2467815408
Cited by
53
References
0
Citations by year
2026: 1 · 2025: 5 · 2024: 2 · 2023: 3 · 2022: 6 · 2021: 4 · 2020: 21 · 2019: 3 · 2018: 3 · 2017: 3 · 2016: 2
FWCI
39.89
Type
journal article
Language
en
Open access
Locations
  • New Criminal Law Review (publishedVersion)
  • SSRN Electronic Journal (submittedVersion)
Topics
  • Social Sciences › Political Science and International Relations › Policing Practices and Perceptions
  • Social Sciences › Law › Criminal Law and Evidence
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
police discrimination, racial profiling, stop and frisk, big data, predictive policing, algorithmic bias, statistical evidence, Fourth Amendment, police searches, contraband detection

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