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Partially Generative Neural Networks for Gang Crime Classification with Partial Information

Authors:

P. Jeffrey Brantingham,

Yan LiuAuthors Info & Claims

AIES '18: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society

Pages 257 - 263

https://doi.org/10.1145/3278721.3278758

Published: 27 December 2018 Publication History

Abstract

More than 1 million homicides, robberies, and aggravated assaults occur in the United States each year. These crimes are often further classified into different types based on the circumstances surrounding the crime (e.g., domestic violence, gang-related). Despite recent technological advances in AI and machine learning, these additional classification tasks are still done manually by specially trained police officers. In this paper, we provide the first attempt to develop a more automatic system for classifying crimes. In particular, we study the question of classifying whether a given violent crime is gang-related. We introduce a novel Partially Generative Neural Networks (PGNN) that is able to accurately classify gang-related crimes both when full information is available and when there is only partial information. Our PGNN is the first generative-classification model that enables to work when some features of the test examples are missing. Using a crime event dataset from Los Angeles covering 2014-2016, we experimentally show that our PGNN outperforms all other typically used classifiers for the problem of classifying gang-related violent crimes.

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Cited By

Ramyadharshni SBhuvaneswari A(2023)CrimedetGANCyber-Physical System Solutions for Smart Cities10.4018/978-1-6684-7756-4.ch002(16-35)Online publication date: 5-May-2023
https://doi.org/10.4018/978-1-6684-7756-4.ch002
P AM SVignesh CS K(2023)Machine Learning Solution for Police Functions2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)10.1109/IDCIoT56793.2023.10053461(463-469)Online publication date: 5-Jan-2023
https://doi.org/10.1109/IDCIoT56793.2023.10053461
Cole MCant CUstek Spilda FGraham M(2022)Politics by Automatic Means? A Critique of Artificial Intelligence Ethics at WorkFrontiers in Artificial Intelligence10.3389/frai.2022.8691145Online publication date: 15-Jul-2022
https://doi.org/10.3389/frai.2022.869114
Show More Cited By

Index Terms

Partially Generative Neural Networks for Gang Crime Classification with Partial Information
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Learning in probabilistic graphical models

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Published In

cover image ACM Conferences

AIES '18: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society

December 2018

406 pages

ISBN:9781450360128

DOI:10.1145/3278721

Program Chairs:
Jason Furman
Harvard University, USA
,
Gary Marchant
Arizona State University, USA
,
Huw Price
Cambridge University, UK
,
Francesca Rossi
IBM Research, USA & University of Padova, Italy

Copyright © 2018 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 the author(s) 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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New York, NY, United States

Publication History

Published: 27 December 2018

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National Science Foundation
MINERVA

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AIES '18

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SIGAI

AIES '18: AAAI/ACM Conference on AI, Ethics, and Society

February 2 - 3, 2018

LA, New Orleans, USA

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AIES '18 Paper Acceptance Rate 61 of 162 submissions, 38%;

Overall Acceptance Rate 61 of 162 submissions, 38%

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Cited By

Ramyadharshni SBhuvaneswari A(2023)CrimedetGANCyber-Physical System Solutions for Smart Cities10.4018/978-1-6684-7756-4.ch002(16-35)Online publication date: 5-May-2023
https://doi.org/10.4018/978-1-6684-7756-4.ch002
P AM SVignesh CS K(2023)Machine Learning Solution for Police Functions2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)10.1109/IDCIoT56793.2023.10053461(463-469)Online publication date: 5-Jan-2023
https://doi.org/10.1109/IDCIoT56793.2023.10053461
Cole MCant CUstek Spilda FGraham M(2022)Politics by Automatic Means? A Critique of Artificial Intelligence Ethics at WorkFrontiers in Artificial Intelligence10.3389/frai.2022.8691145Online publication date: 15-Jul-2022
https://doi.org/10.3389/frai.2022.869114
Dakalbab FAbu Talib MAbu Waraga OBou Nassif AAbbas SNasir Q(2022)Artificial intelligence & crime prediction: A systematic literature reviewSocial Sciences & Humanities Open10.1016/j.ssaho.2022.1003426:1(100342)Online publication date: 2022
https://doi.org/10.1016/j.ssaho.2022.100342
Walczak S(2021)Predicting Crime and Other Uses of Neural Networks in Police Decision MakingFrontiers in Psychology10.3389/fpsyg.2021.58794312Online publication date: 7-Oct-2021
https://doi.org/10.3389/fpsyg.2021.587943
Green B(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
https://doi.org/10.23919/JSC.2021.0029
Sambasivan NArnesen EHutchinson BDoshi TPrabhakaran V(2021)Re-imagining Algorithmic Fairness in India and BeyondProceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency10.1145/3442188.3445896(315-328)Online publication date: 3-Mar-2021
https://dl.acm.org/doi/10.1145/3442188.3445896
Cotton HKwembe T(2021)Using Data Analytics to Forecast Violent Crime2021 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI54926.2021.00122(301-304)Online publication date: Dec-2021
https://doi.org/10.1109/CSCI54926.2021.00122
Safat WAsghar SGillani S(2021)Empirical Analysis for Crime Prediction and Forecasting Using Machine Learning and Deep Learning TechniquesIEEE Access10.1109/ACCESS.2021.30781179(70080-70094)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3078117
Xu JWang AWu JWang CWang RZhou F(2020)SPCSS: Social Network Based Privacy-Preserving Criminal Suspects SensingIEEE Transactions on Computational Social Systems10.1109/TCSS.2019.29608577:1(261-274)Online publication date: Feb-2020
https://doi.org/10.1109/TCSS.2019.2960857
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