Law Enforcement Use of Predictive Policing Approaches: Proceedings of a Workshop (2025)
Chapter: Appendix C: References
Appendix C
References
Bhalerao, R., McDonald, N., Barakat, H., Hamilton, V., McCoy, D., & Redmiles, E. (2022). Ethics and efficacy of unsolicited anti-trafficking SMS outreach. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), 1–39. https://doi.org/10.1145/3555083
Braga, A. A., & Bond, B. J. (2008). Policing crime and disorder hot spots: A randomized controlled trial. Criminology, 46(3), 577–607. https://doi.org/10.1111/j.1745-9125.2008.00124.x
Braga, A. A., Sousa, W. H., Coldren, J. R., Jr., & Rodriguez, D. (2018). The effects of body-worn cameras on police activity and police-citizen encounters: A randomized controlled trial. Journal of Criminal Law and Criminology, 108(3), 511–538.
Brayne, S. (2021). Predict and surveil: Data, discretion, and the future of policing. Oxford University Press.
Bureau of Justice Assistance. (2010). Smart policing initiative. U.S. Department of Justice. https://www.ojp.gov/ncjrs/virtual-library/abstracts/smart-policing-initiative
Bureau of Justice Statistics. (2022). Local police departments personnel, 2020. U.S. Department of Justice, Office of Justice Programs. https://bjs.ojp.gov/library/publications/local-police-departments-personnel-2020
Carter, J. G., Mohler, G., Raje, R., Chowdhury, N., & Pandey, S. (2021). The Indianapolis harmspot policing experiment. Journal of Criminal Justice, 74(c), 101814. https://doi.org/10.1016/j.jcrimjus.2021.101814
Doyle, M. C., & Gerell, M. (2024). Assessing crime history as a predictor: Exploring hotspots of violent and property crime in Malmö, Sweden. International Criminal Justice Review. https://doi.org/10.1177/10575677241230915
Executive Order No. 14074, 87 FR 32945 (2022). Advancing effective, accountable policing and criminal justice practices to enhance public trust and public safety. https://www.federalregister.gov/documents/2022/05/31/2022-11810/advancing-effective-accountable-policing-and-criminal-justice-practices-to-enhance-public-trust-and
Executive Order No. 14110, 88 FR 75191 (2023). Safe, secure, and trustworthy development and use of artificial intelligence. https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence
Florence, C., Shepherd, J. P., Brennan, I., & Simon, T. (2011). Effectiveness of anonymised information sharing and use in health service, police, and local government partnership for preventing violence related injury: Experimental study and time series analysis. British Medical Journal, 342, d3313. https://doi.org/10.1136/bmj.d3313
Gill, C., Weisburd, D., Nazaire, D., Prince, H., & Shader, C. G. (2024). Building “a beautiful safe place for youth” through problem-oriented community organizing: A quasi-experimental evaluation. Criminology & Public Policy, 23(2), 287–325. https://doi.org/10.1111/1745-9133.12657
Gill, C., Weisburd, D., Telep, C. W., Vitter, Z., & Bennett, T. (2014). Community-oriented policing to reduce crime, disorder and fear and increase satisfaction and legitimacy among citizens: A systematic review. Journal of Experimental Criminology, 10, 399–428. https://doi.org/10.1007/s11292-014-9210-y
Green, B. (2019). The just city: Machine learning’s social and political foundations. In The smart enough city (1st ed.). MIT Press. https://smartenoughcity.mitpress.mit.edu/pub/vmjl8djz/release/1
Hunt, P., Saunders, J., & Hollywood, J. S. (2014). Evaluation of the Shreveport predictive policing experiment. Rand Corporation.
Kunichoff, Y., & Sier, P. (2017). The contradictions of Chicago police’s secretive list. Chicago Magazine, the Chicago Tribune. https://www.chicagomag.com/city-life/August-2017/Chicago-Police-Strategic-Subject-List/
Lee, Y., Bradford, B., & Posch, K. (2024). The effectiveness of big data-driven predictive policing: Systematic review. Justice Evaluation Journal, 7(2), 127–160. https://doi.org/10.1080/24751979.2024.2371781
Lin, Z. J., Jung, J., Goel, S., & Skeem, J. (2020). The limits of human predictions of recidivism. Science Advances, 6(7), Article eaaz0652. https://doi.org/10.1126/sciadv.aaz0652
Mehrotra, D., Mattu, S., Gilbertson, A., & Sankin, A. (2021, December 2). How we determined crime prediction software disproportionately targeted low-income, Black, and Latino neighborhoods. The Markup. https://themarkup.org/show-your-work/2021/12/02/how-we-determined-crime-prediction-software-disproportionately-targeted-low-income-black-and-latino-neighborhoods
Musa, J. (2024). Re: Data-driven policing technologies at issue in Executive Order 14110 Section 7.1(b). National Association of Criminal Defense Lawyers. https://www.nacdl.org/getattachment/097760a8-5a96-40c2-8932-be9a3708e754/nacdl-comments-to-doj-on-issues-with-the-use-of-data-driven-policing-technologies-in-eo14110-05282024.pdf
Office of Inspector General. (2019). Review of selected Los Angeles Police Department data-driven policing strategies. L.A. Police Commission. https://www.lapdpolicecom.lacity.org/031219/BPC_19-0072.pdf
Office of Inspector General. (2020). Advisory concerning the Chicago Police Department’s predictive risk models. City of Chicago. https://igchicago.org/wp-content/uploads/2020/01/OIG-Advisory-Concerning-CPDs-Predictive-Risk-Models-.pdf
Ratcliffe, J. H. (2019). Advocate: Predictive policing. In D. Weisburd & A. A. Braga (Eds.), Police innovation: Contrasting perspectives (pp. 347–365). Cambridge University Press. https://doi.org/10.1017/9781108278423.016
Ratcliffe, J. H., & Kikuchi, G. (2019). Harm-focused offender triage and prioritization: A Philadelphia case study. Policing: An International Journal, 42(1), 59–73. https://doi.org/10.1108/PIJPSM-08-2018-0118
Ratcliffe, J. H., & Rengert, G. F. (2008). Near-repeat patterns in Philadelphia shootings. Justice Quarterly, 25(2), 348–370.
Ratcliffe, J. H., Taylor, R. B., Askey, A. P., Thomas, K., Grasso, J., Bethel, K., Fisher, R., & Koehnlein, J. (2021). The Philadelphia predictive policing experiment: Final report. Journal of Experimental Criminology, 17(1), 15–41.
Sankin, A., Mehrotra, D., Mutta, S., & Gilbertson, A. (2021, December 2). Crime prediction software promised to be free of biases. New data shows it perpetuates them. The Markup. https://themarkup.org/prediction-bias/2021/12/02/crime-prediction-software-promised-to-be-free-of-biases-new-data-shows-it-perpetuates-them
Schiff, K. J., Schiff, D. S., Adams, I. T., McCrain, J., & Mourtgos, S. M. (2023). Institutional factors driving citizen perceptions of AI in government: Evidence from a survey experiment on policing. Public Administration Review. https://doi.org/10.1111/puar.13754
Stop LAPD Spying Coalition. (n.d.). The architecture of data-driven policing. In Automating banishment: The surveillance and policing of looted land (pp. 6–23). https://automating-banishment.org/section/2-architecture-of-data-driven-policing/
Wyatt, J., & Alexander, M. (2010). Integrating crime and traffic crash data in Nashville. Geography & Public Safety, 2(3), 9–11.
This page intentionally left blank.
