The DOI matches its Crossref record; 6 of the 7 references it cites were found in a registry or an index.
Personalized Risk Assessments in the Criminal Justice System
Abstract
In an effort to bring greater efficiency, equity, and transparency to the criminal justice system, statistical risk assessment tools are increasingly used to inform bail, sentencing, and parole decisions. We examine New York City's stop-and-frisk program, and propose two new use cases for personalized risk assessments. First, we show that risk assessment tools can help police officers make considerably better real-time stop decisions. Second, we show that such tools can help audit past actions; in particular, we argue that a sizable fraction of police stops were conducted on the basis of little evidence, in possible violation of constitutional protections.
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Simple Heuristics That Make Us Smart
Gigerenzer, G., & Todd, P. M. (1999) · Cited by 3,563
Extraneous factors in judicial decisions
Danziger, S., Levav, J., & Avnaim-Pesso, L. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018033108 · Cited by 1,431
A Methodology for Direct and Indirect Discrimination Prevention in Data Mining
Hajian, S., & Domingo-Ferrer, J. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.72 · Cited by 312
Precinct or prejudice? Understanding racial disparities in New York City’s stop-and-frisk policy
Goel, S., Rao, J. M., & Shroff, R. (2016) · The Annals of Applied Statistics · doi:10.1214/15-aoas897 · Cited by 285
Self-citationBerk, R., Sherman, L., Barnes, G., Kurtz, E., & Ahlman, L. (2009) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2008.00556.x · Cited by 178
Developing a Practical Forecasting Screener for Domestic Violence Incidents
Berk, R. A., He, Y., & Sorenson, S. B. (2005) · Evaluation Review · doi:10.1177/0193841x05275333 · Cited by 43
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- Ferguson Andrew Guthrie, 2012, Emory Law Journal, 62, 259
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Cohen, I. (2022) · The International Journal of Evidence & Proof · doi:10.1177/13657127221120957
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Metadata
- Authors
- Goel, S., Rao, J. M., & Shroff, R.
- Affiliations
- Stanford University (US)
- Microsoft (United States) (US)
- New York University (US)
- Venue
- American Economic Review
- Publisher
- American Economic Association
- Published
- 2016-05-01
- Volume
- 106
- Issue
- 5
- Pages
- 119-123
- ISSN
- 0002-8282 (ISSN-L)
- OpenAlex
- W2356267092
- Cited by
- 27
- References
- 7
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 2 · 2022: 4 · 2021: 4 · 2020: 4 · 2019: 1 · 2018: 5 · 2017: 1
- FWCI
- 4.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Economic Review (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Social Sciences › Political Science and International Relations › Policing Practices and Perceptions
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- statistical risk assessment, stop and frisk, police stops, bail decisions, sentencing, parole decisions, algorithmic decision-making, racial bias in policing, Fourth Amendment, New York City
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