人工智能和机器学习的进步引起了世界各国政府的关注,一些国家希望利用这些工具使预测性警察用于遏制犯罪。最近,芝加哥大学的科学家开发了一种新的算法,可以提前一周预测犯罪,准确率约为90%。

Advances in artificial intelligence and machine learning have sparked interest from governments that would like to use these tools for predictive policing to deter crime. However, early efforts at crime prediction have been controversial, because they do not account for systemic biases in police enforcement and its complex relationship with crime and society.
人工智能和机器学习的进步引起了世界各国政府的关注,他们希望利用这些工具使预测性的警察使用来遏制犯罪。然而,犯罪预测的早期工作一直存在争议,没有考虑警察执法中的系统性偏差及其与犯罪和社会的复杂关系。

University of Chicago data and social scientists have developed a new algorithm that forecasts crime by learning patterns in time and geographic locations from public data on violent and property crimes. It has demonstrated success at predicting future crimes one week in advance with approximately 90% accuracy.
芝加哥大学数据和社会科学家开发了一种新的算法,通过从暴力和财产犯罪的公共数据中学习时间和地理位置模式来预测犯罪。该算法提前一周成功预测未来犯罪,准确率约为90%。

The new study was published on June 30, 2022, in the journal Nature Human Behavior.
该研究论文于2022年6月30日发表在《自然-人类行为》杂志上。

The new tool was tested and validated using historical data from the City of Chicago around two broad categories of reported events: violent crimes (homicides, assaults and batteries) and property crimes (burglaries, thefts, and motor vehicle thefts).
利用芝加哥的历史数据,围绕两类报案案例对新模型进行了测试和验证:暴力犯罪(谋杀、殴打和人身攻击)和财产犯罪(入室盗窃、盗窃和机动车盗窃)。

The new model isolates crime by looking at the time and spatial coordinates of discrete events and detecting patterns to predict future events. It divides the city into spatial tiles roughly 1,000 feet across and predicts crime within these areas instead of relying on traditional neighborhood or political boundaries, which are also subject to bias. The model performed just as well with data from seven other US cities: Atlanta, Austin, Detroit, Los Angeles, Philadelphia, Portland, and San Francisco.
模型通过观察离散事件的时空坐标,检测模式来预测未来事件,从而预防犯罪。它将城市划分为每个宽约300米的区,并预测这些区内的犯罪,而不是依靠传统的街区或行政边界,因为这些边界也会有偏差。该模型在七个美国城市的数据中表现同样出色:亚特兰大、奥斯汀、底特律、洛杉矶、费城、波特兰和旧金山。

Ishanu Chattopadhyay, Assistant Professor of Medicine at UChicago and senior author of the study, is careful to note that the tool’s accuracy does not mean that it should be used to direct law enforcement, with police departments using it to swarm neighborhoods proactively to prevent crime. Instead, it should be added to a toolbox of urban policies and policing strategies to address crime.
研究论文的第一作者、芝加哥大学医学助理教授伊珊·查托·帕迪亚(Yi Shan Chato Padia)谨慎地表示,这一工具的准确性并不意味着它应该被用来指导执法,让警察主动进入社区预防犯罪。相反,应将其应用于城市政策和公共安全战略,以应对犯罪。


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