Exploring the Impact of (Not) Changing Default Settings in Algorithmic Crime Mapping - A Case Study of Milwaukee, Wisconsin
Association for Computing Machinery
CSCW '19 Companion: Conference Companion Publication of the 2019 ACM Conference on Computer Supported Cooperative Work and Social Computing
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.
Haque, MD Romael; Weathington, Katy; and Guha, Shion, "Exploring the Impact of (Not) Changing Default Settings in Algorithmic Crime Mapping - A Case Study of Milwaukee, Wisconsin" (2019). Computer Science Faculty Research and Publications. 24.
ADA Accessible Version
Published version. Published as part of the proceedings of the conference Computer Supported Cooperative Work and Social Computing, 2019: 206-210. DOI. © 2019 the owner/author(s). Used with permission.