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Reducing Incarceration through Prioritized Interventions

Published: 20 June 2018 Publication History

Abstract

The most vulnerable individuals in society often struggle with long-lasting, multi-faceted challenges like mental illness, substance abuse, chronic health conditions, and homelessness. Individuals experiencing these difficulties tend to interact with public services and departments frequently, but many communities are struggling to identify those individuals, let alone meet their needs in meaningful and cost-effective ways. In this paper, we describe our work with Johnson County, Kansas, that uses machine learning to prioritize outreach to individuals most at risk of being booked into jail within the next year. For the first time, we brought together Johnson County's jail, emergency medical, and mental health data, identified individuals who touch multiple systems, and built a model to predict individual jail bookings. Our system significantly outperformed both a random baseline and several simple heuristics that domain experts are likely to use and implement. By focusing on 200 individuals (which is the intervention capacity of Johnson County) who had interacted with both mental health services and the criminal justice system, we predicted jail bookings in the following year with 51% precision, which outperforms a baseline heuristic model by 1.5 times, and is 4.6 times better than a random baseline. This work provides a framework and prototype system for Johnson County as well as many other jurisdictions that are part of the Data Driven Justice Initiative as they develop intervention models to proactively connect social and mental health workers with individuals in need of care to avoid incarceration.

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cover image ACM Conferences
COMPASS '18: Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies
June 2018
472 pages
ISBN:9781450358163
DOI:10.1145/3209811
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 June 2018

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Author Tags

  1. criminal justice system
  2. frequent utilizers
  3. machine learning
  4. predictive intervention system

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COMPASS '18
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COMPASS '18: ACM SIGCAS Conference on Computing and Sustainable Societies
June 20 - 22, 2018
CA, Menlo Park and San Jose, USA

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Overall Acceptance Rate 25 of 50 submissions, 50%

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Cited By

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  • (2024)Role of Machine Learning in Policy Making and EvaluationInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT24OCT687(456-463)Online publication date: 19-Oct-2024
  • (2024)From Prisons to Programming: Fostering Self-Efficacy via Virtual Web Design Curricula in Prisons and JailsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642717(1-13)Online publication date: 11-May-2024
  • (2024)Judicial leadership matters (yet again): the association between judge and public trust for artificial intelligence in courtsDiscover Artificial Intelligence10.1007/s44163-024-00142-34:1Online publication date: 28-Jun-2024
  • (2021)Data Science as Political Action: Grounding Data Science in a Politics of JusticeJournal of Social Computing10.23919/JSC.2021.00292:3(249-265)Online publication date: Sep-2021
  • (2021)An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy SettingsACM SIGKDD Explorations Newsletter10.1145/3468507.346851823:1(69-85)Online publication date: 29-May-2021
  • (2021)Empirical observation of negligible fairness–accuracy trade-offs in machine learning for public policyNature Machine Intelligence10.1038/s42256-021-00396-x3:10(896-904)Online publication date: 14-Oct-2021
  • (2020)Machine Learning in Mental HealthACM Transactions on Computer-Human Interaction10.1145/339806927:5(1-53)Online publication date: 17-Aug-2020
  • (2020)Algorithmic realismProceedings of the 2020 Conference on Fairness, Accountability, and Transparency10.1145/3351095.3372840(19-31)Online publication date: 27-Jan-2020
  • (2019)Towards an Effective Digital Literacy Intervention to Assist Returning Citizens with Job SearchProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300315(1-12)Online publication date: 2-May-2019
  • (2018)Returning Citizens' Job Search and Technology UseCompanion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing10.1145/3272973.3274098(365-368)Online publication date: 30-Oct-2018

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