This chapter provides a general introduction to forecasting criminal behavior in criminal justice settings. A common application is to predict at a parole hearing whether the inmate being considered for release is a significant risk to public safety. It may surprise some that criminal justice forecasts of risk have been used by decision-makers in the United States since at least the 1920s. Over time, statistical methods have replaced clinical methods, leading to improvements in forecasting accuracy. The gains were at best gradual until recently, when the increasing availability of very large datasets, powerful computers, and new statistical procedures began to produce dramatic improvements. But, criminal justice forecasts of risk are inextricably linked to criminal justice decision-making and to both the legitimate and illegitimate interests of various stakeholders. Sometimes, criticisms of risk assessment become convenient vehicles to raise broader issues around social inequality. There are, in short, always political considerations, ethical complexities, and judgement calls for which there can be no technical fix. The recent controversy about “racial bias” in risk instruments is a salient example.
- Alexander, M. (2018) The newest Jm Crow: recent criminal justice reforms contain the seeds of a frightening system of “e-encarceration”. New York Times November 8th, https://www.nytimes.com/2018/11/08/opinion/sunday/criminal-justice-reforms-race-technology.htm
- Amhed S. & Walker, C., (2018) There have been on the average 1 school shooting every week this year. CNN posted May 25th, 2018. https://www.cnn.com/2018/03/02/us/school-shootings-2018-list-trnd/index.html
- Berk, R. A. (2008) Forecasting methods in crime and justice. In J. Hagan, K. L. Schepple, and T. R. Tyler (eds.) Annual Review of Law and Social Science 4 (173–192). Palo Alto: Annual Reviews.Google Scholar
- Berk, R. A., Heirdari, H., Jabbari, S., Kearns, M., & Roth, A. (2018a) Fairness in criminal justice risk assessments: The State of the Art. Sociological Methods and Research, in press.Google Scholar
- Burgess, E. M. (1928) Factors determining success or failure on parole. In A. A. Bruce, A. J. Harno, E. .W Burgess, and E. W., Landesco (eds.) The Working of the Indeterminate Sentence Law and the Parole System in Illinois (pp. 205–249). Springfield, Illinois, State Board of Parole.Google Scholar
- Casey, P. M., Warren, R. K., & Elek, J. K. (2011) Using offender risk and needs assessment information at sentencing: guidance from a national working group. National Center for State Courts, www.ncsconline.org/.
- Elzayn, H., Jabbari, S., Jung, C., Kearns, M., Neel, S., Aaron Roth, A., & Schutzman, Z. (2018) Fair algorithms for learning in allocation problems. In Proceedings of ACM Conference on Fairness, Accountability, and Transparency (ACM FAT*?18).Google Scholar
- Farrington, D. P. & Tarling, R. (1985) Prediction in Criminology. Albany: SUNY Press.Google Scholar
- Gigi, A. (1990) Nonlinear Multivariate Analysis New York: Wiley.Google Scholar
- Hastie, R., & Dawes, R. M. (2001) Rational Choice in an Uncertain World. Thousand Oaks: Sage Publications.Google Scholar
- Hyatt, J.M., Chanenson, L. & Bergstrom, M.H. (2011) Reform in motion: the promise and profiles of incorporating risk assessments and cost-benefit analysis into Pennsylvania Sentencing. Duquesne Law Review 49(4): 707–749.Google Scholar
- Monahan, J. (1981) Predicting Violent Behavior: An Assessment of Clinical Techniques. Newbury Park: Sage Publications.Google Scholar
- Pew Center of the States, Public Safety Performance Project (2011) Risk/needs assessment 101: science reveals new tools to manage offenders. The Pew Center of the States. www.pewcenteronthestates.org/publicsafety.
- Reiss, A. J. (1951) The accuracy, efficiency, and validity of a prediction instrument. American Journal of Sociology 17: 268–274.Google Scholar
- Robinson, D. & Scognigs, C., (2018) The detection of criminal groups in real-world fused data: using the graph-mining algorithm “GraphExtract.” Security Informatics, published online, https://doi.org/10.1186/s13388-018-0031-9.
- Roehl, J., O’Sullivan, C., Webster, D., & Campbell, J. (2005) Intimate partner violence risk assessment validation study, final report. National Institute of Justice, U.S. Department of Justice.Google Scholar
- Sánchez, A., & Carme Ruíz de Villa, M. (2018) A tutorial review of microarray data analysis. Working paper, Department of Statistics, University of Barcelona. http://www.ub.edu/stat/docencia/bioinformatica/microarrays/ADM/slides/A_Tutorial_Review_of_Microarray_data_Analysis_17-06-08.pdf.
- Ubiñas, H. (2018) In Philly, we’re burying our children, not our weapons. Philadelphia Daily News at Philly.com, posted August 24th, 2018 http://www2.philly.com/philly/columnists/helen_ubinas/helen-ubinas-philadelphia-violence-erase-the-rate-philadelphia-police-20180824.html.
- Wager, S. & Athey, S. (2017) Estimation and inference of heterogeneous treatment effects using random forests. arXiv:1510.04342v4 [stat.ME].Google Scholar
- Wilson, C. (2018) This chart shows the number of school shooting victims since Sandy Hook. Time Magazine, posted February 22nd, 2018. http://time.com/5168272/how-many-school-shootings/.