Duncan Purves (U Florida) and Ryan Jenkins (Cal Polytechnic) have posted “A Machine Learning Evaluation Framework for Place-based Algorithmic Patrol Management” on SSRN. Here is the abstract:
American law enforcement agencies are increasingly adopting data-driven technologies to combat crime, with the market for such technologies projected to grow significantly in the coming years. One prevalent approach, place-based algorithmic patrol management (PAPM), analyzes data on past crimes to optimize police patrols. These systems promise several benefits, including efficient resource allocation, reduced bias, and increased transparency. However, the adoption of these technologies has raised ethical and social concerns, particularly around privacy, bias, and community impact. This report aims to provide a comprehensive framework, including many concrete recommendations, for the ethical and responsible development and deployment of PAPM systems. Targeting developers, law enforcement agencies, policymakers, and community advocates, the recommendations emphasize collaboration among these stakeholders to address the complex challenges presented by PAPM. We suggest that failure to meet the proposed ethical guidelines might make the use of such technologies unacceptable. This report has been supported by National Science Foundation awards #1917707 and #1917712 and the Center for Advancing Safety of Machine Intelligence (CASMI).