Work

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

Year: 2019

Type: conference-paper

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.

Source: ACM Conference on Computer Supported Cooperative Work (CSCW)

DOI: 10.1145/3311957.3359500

Authors: MD Romael Haque, Katherine Weathington, Shion Guha

Institution: Marquette University

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Peace, justice, and strong institutions, Reduced inequalities, Sustainable cities and communities

Open Access status: bronze

Funder: National Science Foundation