Exploring the Impact of (Not) Changing Default Settings in Algorithmic Crime Mapping - A Case Study of Milwaukee, Wisconsin
Open accessAbstract
Policing decisions, allocations and outcomes are determined by mapping historical crime data geo-spatially using popular algorithms. In this extended abstract, we present early results from a mixed-methods study of the practices, policies, and perceptions of algorithmic crime mapping in the city of Milwaukee, Wisconsin. We investigate this differential by visualizing potential demographic biases from publicly available crime data over 12 years (2005-2016) and conducting semi-structured interviews of 19 city stakeholders and provide future research directions from this study.
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A core contribution is showing that the theoretically derived ADMAPS framework, empirically validated in this child-welfare case study, can be used in other public-sector domains to assess high-stakes algorithms.
“Thus, a core contribution of this paper is that we demonstrate how the theoretically derived ADMAPS framework (empirically validated through this case study in child-welfare) can be used within other public sector domains (e.g., criminal justice [58,59]) to assess the strengths and weaknesses of algorithms making high-stakes decisions in the lives of people.”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [59]
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“, criminal justice [58, 59]) to assess the strengths and weaknesses of algorithms making high-stakes decisions in the lives of people.”
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A Critical Study of Geospatial Algorithm Use in Crime Analysis and Predictive Policing
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Self-citationWhen Metrics Mislead: Parents’ Lived Realities in the Public Safety Net
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- Authors
- Haque, M. R., Weathington, K., & Guha, S.
- Affiliations
- Marquette University (US)
- Venue
- Companion Publication of the 2019 Conference on Computer Supported Cooperative Work and Social Computing
- Publisher
- ACM
- Published
- 2019-11-09
- Pages
- 206-210
- OpenAlex
- W2984033940
- Cited by
- 7
- References
- 18
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2022: 1 · 2021: 1 · 2020: 2
- FWCI
- 1.05
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- ACM Conference on Computer Supported Cooperative Work (CSCW) (publishedVersion)
- e-Publications@Marquette (Marquette University) (submittedVersion)
- Funders
- National Science Foundation (grant number: 1850517)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › Policing Practices and Perceptions
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Keywords
- default options, algorithmic bias, demographic bias, predictive policing, stakeholder perspectives, Milwaukee
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