research-article

Guiding Prosecutorial Decisions with an Interpretable Statistical Model

Authors:

Alex Chohlas-Wood,

Sharad GoelAuthors Info & Claims

AIES '19: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society

Pages 469 - 476

https://doi.org/10.1145/3306618.3314235

Published: 27 January 2019 Publication History

Abstract

After a felony arrest, many American jurisdictions hold individuals for several days while police officers investigate the incident and prosecutors decide whether to press criminal charges. This pre-arraignment detention can both preserve public safety and reduce the need for officers to seek out and re-arrest individuals who are ultimately charged with a crime. Such detention, however, also comes at a high social and financial cost to those who are never charged but still incarcerated. In one of the first large-scale empirical analyses of pre-arraignment detention, we examine police reports and charging decisions for approximately 30,000 felony arrests in a major American city between 2012 and 2017. We find that 45% of arrested individuals are never charged for any crime but still typically spend one or more nights in jail before being released. In an effort to reduce such incarceration, we develop a statistical model to help prosecutors identify cases soon after arrest that are likely to be ultimately dismissed. By carrying out an early review of five such candidate cases per day, we estimate that prosecutors could potentially reduce pre-arraignment incarceration for ultimately dismissed cases by 35%. To facilitate implementation and transparency, our model to prioritize cases for early review is designed as a simple, weighted checklist. We show that this heuristic strategy achieves comparable performance to traditional, black-box machine learning models.

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Cited By

Lin ZSheng HGoel SRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Probability paths and the structure of predictions over timeProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3541418(15098-15110)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3541418
Chohlas-Wood ANudell JYao KLin ZNyarko JGoel SFourcade MKuipers BLazar SMulligan D(2021)Blind Justice: Algorithmically Masking Race in Charging DecisionsProceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3461702.3462524(35-45)Online publication date: 21-Jul-2021
https://dl.acm.org/doi/10.1145/3461702.3462524

Index Terms

Guiding Prosecutorial Decisions with an Interpretable Statistical Model
1. Applied computing
  1. Law, social and behavioral sciences
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning linear models

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cover image ACM Conferences

AIES '19: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society

January 2019

577 pages

ISBN:9781450363242

DOI:10.1145/3306618

General Chairs:
Vincent Conitzer
Duke University, USA
,
Gillian Hadfield
University of Toronto + Vector Institute, Canada; OpenAI, USA
,
Shannon Vallor
Santa Clara University, USA

Copyright © 2019 ACM.

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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SIGAI: ACM Special Interest Group on Artificial Intelligence
AAAI: American Association for Artificial Intelligence

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 January 2019

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AIES '19: AAAI/ACM Conference on AI, Ethics, and Society

January 27 - 28, 2019

HI, Honolulu, USA

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Overall Acceptance Rate 61 of 162 submissions, 38%

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Cited By

Lin ZSheng HGoel SRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Probability paths and the structure of predictions over timeProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3541418(15098-15110)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3541418
Chohlas-Wood ANudell JYao KLin ZNyarko JGoel SFourcade MKuipers BLazar SMulligan D(2021)Blind Justice: Algorithmically Masking Race in Charging DecisionsProceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3461702.3462524(35-45)Online publication date: 21-Jul-2021
https://dl.acm.org/doi/10.1145/3461702.3462524

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