Exploring the Impact of (Not) Changing Default Settings in Algorithmic Crime Mapping - A Case Study of Milwaukee, Wisconsin

Authors

MD Romael Haque, Marquette University
Katy Weathington, Marquette University
Shion Guha, Marquette UniversityFollow

Document Type

Conference Proceeding

Publication Date

2019

Publisher

Association for Computing Machinery

Source Publication

CSCW '19 Companion: Conference Companion Publication of the 2019 ACM Conference on Computer Supported Cooperative Work and Social Computing

Source ISSN

978145036692

Abstract

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.

Comments

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.

Recommended Citation

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.
https://epublications.marquette.edu/comp_fac/24