Collaborative Research: RUI: Standard Grant: Artificial Intelligence and Predictive Policing: An Ethical Analysis

合作研究：RUI：标准拨款：人工智能和预测性警务：伦理分析

• 批准号: 1917707
• 负责人: Ryan Jenkins
• 金额: $ 24.03万
• 依托单位: California Polytechnic State University Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917707&HistoricalAwards=false
• 关键词: Collaborative; Research; RUI; Standard; Grant

This project is a study of the use of artificial intelligence to assist police on patrol by using massive troves of historical crime data to train machine learning algorithms to anticipate the timing and location of criminal activity. Predictive policing has come under intense criticisms from civil rights groups, academics, and communities that have been subject to predictive policing. These criticisms include its recapitulation of racially biased patterns of policing, its further burdening of marginalized communities, and its infringement on the liberties of targeted communities. The researchers propose to examine these claims and to develop best practices for the development and deployment of algorithmic policing programs. The results of this project promise to benefit police departments, communities patrolled based on algorithmic crime predictions, and public understanding of the societal and ethical implications of predictive policing. The aims of this grant are to examine and develop viable ethical frameworks for the assessment of predictive policing practices. In doing so, the researchers will propose and evaluate novel considerations that might bear on the ethics of predictive policing. They will also develop empirically grounded recommendations for the ethically sensitive and effective development and use of predictive policing. The results of this project promise to advance understanding and illuminate fertile new areas of research in moral philosophy, technology ethics, sociology, and criminology. They also will have clear relevance for other algorithmic technologies with similar implications for justice including algorithms that predict criminal sentences, make healthcare diagnoses, and serve as gatekeepers to government benefits (all of which are currently in use).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.

该项目是一项关于使用人工智能协助警察巡逻的研究，通过使用大量历史犯罪数据来训练机器学习算法，以预测犯罪活动的时间和位置。预测性警务受到了来自民权团体、学者和社区的强烈批评，这些团体一直受到预测性警务的影响。这些批评包括它重述了带有种族偏见的警务模式，进一步加重了边缘化社区的负担，以及侵犯了目标社区的自由。研究人员建议检查这些说法，并为算法警务程序的开发和部署制定最佳实践。该项目的结果有望使警察部门、基于算法犯罪预测进行巡逻的社区以及公众对预测性警务的社会和道德影响的理解受益。这笔赠款的目的是审查和制定可行的道德框架，以评估预测性警务做法。在此过程中，研究人员将提出并评估可能影响预测性警务道德的新考虑因素。他们还将为具有道德敏感性和有效的发展和使用预测性警务提出基于经验的建议。该项目的结果有望促进理解，并照亮道德哲学，技术伦理学，社会学和犯罪学研究的肥沃的新领域。它们也将对其他算法技术具有明显的相关性，这些技术对司法具有类似的影响，包括预测刑事判决、进行医疗诊断以及充当政府福利守门人的算法（所有这些都是目前正在使用的）。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Winning the Battle, Losing the War

赢得战争，输掉战争

• DOI: 10.5840/tpm20208941
• 发表时间: 2020
• 期刊: The Philosophers' Magazine
• 作者: Jenkins, Ryan

Separating facts and evaluation: motivation, account, and learnings from a novel approach to evaluating the human impacts of machine learning

分离事实和评估：动机、解释和从评估机器学习对人类影响的新方法中学到的知识

• DOI: 10.1007/s00146-022-01417-y
• 发表时间: 2022
• 期刊: AI & SOCIETY
• 影响因子: 3
• 作者: Jenkins, Ryan;Hammond, Kristian;Spurlock, Sarah;Gilpin, Leilani
• 通讯作者: Gilpin, Leilani

Big Brother Goes to School: Best Practices for Campus Surveillance Technologies During the COVID-19 Pandemic

老大哥上学：COVID-19 大流行期间校园监控技术的最佳实践

• DOI: 10.5840/techne2021226135
• 发表时间: 2021
• 期刊: Techné: Research in Philosophy and Technology
• 作者: Jenkins, Ryan;Rentz, Zachary I.;Abney, Keith
• 通讯作者: Abney, Keith