CRII: CHS: Estimating the Financial, Social and Ethical Impacts of Algorithmic Crime Analysis

CRII：CHS：估计算法犯罪分析的财务、社会和道德影响

• 批准号: 1850517
• 负责人: Shion Guha
• 金额: $ 17.5万
• 依托单位: Marquette University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1850517&HistoricalAwards=false
• 关键词: CRII; CHS; Estimating; Financial; Social

This research seeks to understand the financial, social and ethical impacts of crime analysis algorithms. Misapplication of algorithms has the capacity for great harm and discrimination, notably when applied in sentencing decisions about criminal offenders. Inspecting and investigating algorithmic biases for their real-world impacts is an extremely novel but increasingly important area. The current literature has mostly focused on philosophical and sociological critiques of algorithms. What is sorely needed is a mathematical and computational investigation that builds on these socio-technical critiques in order to provide contextual and quantitative evidence for actual impact. We need to know how people's biases, assumptions, norms and values play into algorithmic perceptions and applications. More specifically, governments often seek to apply algorithms to crime analysis with the usually stated justification of financial efficiencies, reducing human error and smoothening bureaucracies. Therefore, this research seeks to understand the practical, financial, social and ethical impacts of deploying algorithms in the criminal justice system in order to develop evidence-based policies. This project has four main objectives. First, using crime mapping as a lens of inquiry, the project will investigate the perceptions of financial, social and ethical dimensions of algorithmic crime analysis, to uncover both qualitative and quantitative insights about how laypeople and experts perceive its various implications. Second, the research will deconstruct popular algorithms used in crime analysis in order to understand specific points at which bias may occur and statistically better variants that may mitigate them. Third, it will compare and contrast each biased variant with its less biased counterpart to compute metrics about financial, social and ethical impacts, providing quantitative implications for biased criminal justice policy. Fourth, as a result of the first three objectives, this proposal will help expand initial efforts to build a community of like-minded scholars, though organizing a summer workshop involving practitioners, researchers and students to educate and co-create a community of practice around ethical, fair, accountable and transparent algorithms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项研究旨在了解犯罪分析算法的财务，社会和道德影响。算法的误用会造成巨大的伤害和歧视，特别是在对刑事犯罪者的判决中。 检查和调查算法偏差对现实世界的影响是一个非常新颖但越来越重要的领域。目前的文献主要集中在算法的哲学和社会学批评。迫切需要的是建立在这些社会技术批评基础上的数学和计算调查，以便为实际影响提供背景和量化证据。我们需要知道人们的偏见、假设、规范和价值观如何影响算法的认知和应用。更具体地说，政府经常寻求将算法应用于犯罪分析，通常是出于财务效率的理由，减少人为错误并理顺官僚机构。因此，本研究旨在了解在刑事司法系统中部署算法的实际，财务，社会和道德影响，以制定基于证据的政策。该项目有四个主要目标。首先，使用犯罪地图作为调查的透镜，该项目将调查算法犯罪分析的金融，社会和道德维度的看法，以揭示关于外行和专家如何看待其各种影响的定性和定量见解。其次，该研究将解构犯罪分析中使用的流行算法，以了解可能发生偏见的特定点，以及可能减轻偏见的统计学上更好的变体。第三，它将比较和对比每一个有偏见的变量与其偏见较小的对应计算有关金融，社会和道德影响的指标，提供定量的影响有偏见的刑事司法政策。第四，作为前三个目标的结果，该提案将有助于扩大建立志同道合学者社区的初步努力，通过组织一个有从业人员、研究人员和学生参加的夏季讲习班，教育和共同创建一个围绕道德、公平、该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。

项目成果

Interrogating Human-centered Data Science: Taking Stock of Opportunities and Limitations

• DOI: 10.1145/3491101.3503740
• 发表时间: 2022-04
• 期刊: CHI Conference on Human Factors in Computing Systems Extended Abstracts
• 作者: A. Tanweer;Cecilia R. Aragon;Michael J. Muller;Shion Guha;Samir Passi;Gina Neff;M. Kogan

The Relationships between Data, Power, and Justice in CSCW Research

CSCW 研究中数据、权力和正义之间的关系

• DOI: 10.1145/3311957.3358609
• 发表时间: 2019
• 期刊: CSCW'19 Companion
• 作者: Chancellor, Stevie;Guha, Shion;Kaye, Jofish;King, Jen;Salehi, Niloufar;Schoenebeck, Sarita;Stowell, Elizabeth
• 通讯作者: Stowell, Elizabeth

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

• DOI: 10.1145/3311957.3359500
• 发表时间: 2019-11
• 期刊: Companion Publication of the 2019 Conference on Computer Supported Cooperative Work and Social Computing
• 作者: Md. Romael Haque;Katherine Weathington;Shion Guha