Partially Generative Neural Networks for Gang Crime Classification with Partial Information
Open accessAbstract
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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Distributive justice notions are not universal: children from a partially hunter-gatherer egalitarian African culture distributed spoils more equally than children from two other cultures, with merit playing only a limited role.
“Similarly, a study of children in three different cultures found that notions of distributive justice are not universal: “children from a partially hunter-gatherer, egalitarian African culture distributed the spoils more equally than did the other two cultures, with merit playing only a limited role” [185].”
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- Authors
- Seo, S., Chan, H., Brantingham, P. J., Leap, J., Vayanos, P., Tambe, M., & Liu, Y.
- Affiliations
- University of Southern California (US)
- University of Nebraska–Lincoln (US)
- University of California, Los Angeles (US)
- Venue
- Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society
- Publisher
- ACM
- Published
- 2018-12-27
- Pages
- 257-263
- ISSN
- 3065-8365 (ISSN-L)
- OpenAlex
- W2910680211
- Cited by
- 17
- References
- 26
- Citations by year
- 2025: 1 · 2023: 3 · 2022: 2 · 2021: 6 · 2020: 5
- FWCI
- 2.16
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the AAAI/ACM Conference on AI Ethics and Society (publishedVersion)
- Funders
- National Science Foundation (IIS-1254206)
- MINERVA (N00014-17-1-2281)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Social Sciences › Sociology and Political Science › Crime Patterns and Interventions
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- violent crime, missing data, domestic violence
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