The DOI matches its Crossref record; 50 of the 51 references it cites were found in a registry or an index.
Forecasting Murder Within a Population of Probationers and Parolees: A High Stakes Application of Statistical Learning
Open accessAbstract
Summary: Forecasts of future dangerousness are often used to inform the sentencing decisions of convicted offenders. For individuals who are sentenced to probation or paroled to community supervision, such forecasts affect the conditions under which they are to be supervised. The statistical criterion for these forecasts is commonly called recidivism, which is defined as a charge or conviction for any new offence, no matter how minor. Only rarely do such forecasts make distinctions on the basis of the seriousness of offences. Yet seriousness may be central to public concerns, and judges are increasingly required by law and sentencing guidelines to make assessments of seriousness. At the very least, information about seriousness is essential for allocating scarce resources for community supervision of convicted offenders. The paper focuses only on murderous conduct by individuals on probation or parole. Using data on a population of over 60000 cases from Philadelphia’s Adult Probation and Parole Department, we forecast whether each offender will be charged with a homicide or attempted homicide within 2 years of beginning community supervision. We use a statistical learning approach that makes no assumptions about how predictors are related to the outcome. We also build in the costs of false negative and false positive charges and use half of the data to build the forecasting model, and the other half of the data to evaluate the quality of the forecasts. Forecasts that are based on this approach offer the possibility of concentrating rehabilitation, treatment and surveillance resources on a small subset of convicted offenders who may be in greatest need, and who pose the greatest risk to society.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“For instance, Berk et al. evaluated their model only on the set of defendants who have been granted parole.”
From The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables (2017) · cites this paper as [7]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This assumption does not typically hold in cases where human decisions are providing data labels [7, 21].”
From The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables (2017) · cites this paper as [7]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“While prior research has explored applications of machine learning to domains where human judgments are part of the data labeling process (crime [7, 21, 43], medical diagnosis [6, 22]), the selective labels problem is often overlooked, with evaluation of the models typically carried out by computing traditional metrics such as AUC or accuracy only on the data points for which ground truth labels”
From The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables (2017) · cites this paper as [7]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy
Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997) · Science · doi:10.1126/science.277.5328.918 · Cited by 11,999
Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067
Bias in random forest variable importance measures: Illustrations, sources and a solution
Strobl, C., Boulesteix, A., Zeileis, A., & Hothorn, T. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-25 · Cited by 3,970
Research works printed but not linked: 8
- Allen, 2007, Justice reinvestment: a new approach to crime and justice
- Anderson, 1999, Code of the Street
- Ayres, 2007, Supercrunchers: how Anything can be Predicted
- Burgess, 1928, The Working of the Indeterminant Sentence Law and the Parole System in Illinois, 205
- Gottfredson, 1987, Prediction and Classification
- Gottfredson, 1987, Prediction and Classification
- Maltz, 1984, Recidvism
- Malvestuto, 2007, Testimony to Committee on Public Safety, Council of the City of Philadelphia
Cited by
Sorted by citations
Algorithmic Decision Making and the Cost of Fairness
Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., & Huq, A. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098095 · Cited by 1,149
The Rise of Evidence-Based Policing: Targeting, Testing, and Tracking
Sherman, L. W. (2013) · Crime and Justice · doi:10.1086/670819 · Cited by 619
Self-citationDurlauf, S. N., & Nagin, D. S. (2011) · Criminology & Public Policy · doi:10.1111/j.1745-9133.2010.00680.x · Cited by 415
Neurocriminology: implications for the punishment, prediction and prevention of criminal behaviour
Glenn, A. L., & Raine, A. (2013) · Nature reviews. Neuroscience · doi:10.1038/nrn3640 · Cited by 339
“Machine-leaning techniques such as random forest have already been documented to improve the prediction of future charges of homicide or attempted homicide using traditionally available demographic and social variables 104 .”
MethodInterpretation of nonlinear relationships between process variables by use of random forests
Auret, L., & Aldrich, C. (2012) · Minerals Engineering · doi:10.1016/j.mineng.2012.05.008 · Cited by 281
Metadata
- Authors
- Berk, R., Sherman, L., Barnes, G., Kurtz, E., & Ahlman, L.
- Affiliations
- University of Pennsylvania (US)
- University of Cambridge (GB)
- First Judicial District of Pennsylvania , Philadelphia , USA
- Corresponding author
- Berk, Richard
- Venue
- Journal of the Royal Statistical Society Series A: Statistics in Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2008-07-24
- Volume
- 172
- Issue
- 1
- Pages
- 191-211
- ISSN
- 0964-1998 (ISSN-L)
- 1467-985X
- OpenAlex
- W2038763866
- Cited by
- 178
- References
- 51
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 6 · 2023: 10 · 2022: 13 · 2021: 6 · 2020: 12 · 2019: 14 · 2018: 13 · 2017: 12 · 2016: 16 · 2015: 9 · 2014: 7 · 2013: 15 · 2012: 16
- FWCI
- 24.22
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the Royal Statistical Society Series A (Statistics in Society) (publishedVersion)
- Funders
- National Science Foundation (SES-0437169)
- Topics
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Social Sciences › Psychology › Clinical Psychology › Psychopathy, Forensic Psychiatry, Sexual Offending
- Social Sciences › Law › Criminal Justice and Penology
- Keywords
- probationers, parolees, recidivism, statistical learning, community supervision, sentencing guidelines, resource allocation, Philadelphia
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
