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

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

DOI：
10.1145/3311957.3359500
复制
发表时间：
2019-11
期刊：
Companion Publication of the 2019 Conference on Computer Supported Cooperative Work and Social Computing
影响因子：
--
通讯作者：
Md. Romael Haque;Katherine Weathington;Shion Guha
Md. Romael Haque;Katherine Weathington;Shion Guha
中科院分区：
其他
文献类型：
--
作者：
Md. Romael Haque;Katherine Weathington;Shion Guha

文献摘要

被引文献

相似文献

警务决策、分配和结果是通过使用流行算法对历史犯罪数据进行地理空间映射来确定的。在这篇扩展摘要中，我们介绍了威斯康星州密尔沃基市对算法犯罪地图的实践、政策和看法的混合方法研究的早期结果。我们通过对12年（2005-2016年）公开犯罪数据中潜在的人口统计学偏差进行可视化分析，并对19个城市利益相关者进行半结构化访谈，来研究这种差异，并从本研究中提供未来的研究方向。
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.