The DOI matches its Crossref record; 50 of the 52 references it cites were found in a registry or an index.
An impact assessment of machine learning risk forecasts on parole board decisions and recidivism
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“Assessment tools used to predict recidivism are now widespread in most jurisdictions: bail and sentencing judges (Stevenson and Doleac, 2019; Albright, 2019), parole board members (Berk, 2017), and probation officers (Kopf, 2014) use the assessment scores to determine the level of risk.”
From Prison Rehabilitation Programs: Efficiency and Targeting (2021) · cites this paper as Berk, 2017
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Flagged first, then by citations
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Research works printed but not linked: 3
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Cited by
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Human Decisions and Machine Predictions
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Self-citation
Metadata
- Authors
- Berk, R.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Berk, Richard
- ORCID
- Berk, Richard
- Venue
- Journal of Experimental Criminology
- Published
- 2017-04-08
- Volume
- 13
- Issue
- 2
- Pages
- 193-216
- ISSN
- 1572-8315 (ISSN-L)
- 1573-3750
- OpenAlex
- W2604660495
- Cited by
- 169
- References
- 52
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 20 · 2023: 62 · 2022: 17 · 2021: 17 · 2020: 14 · 2019: 9 · 2018: 4 · 2017: 2
- FWCI
- 20.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Experimental Criminology (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Artificial Intelligence in Law
- Keywords
- parole decisions, recidivism, risk assessment, randomized experiments
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