Legal and Ethical Implications of Predictive Policing Technologies

Abstract

Predictive policing, a rapidly growing area of AI in law enforcement, uses algorithms to predict potential criminal actions, aiming to improve resource allocation, faster reaction times, and crime prevention. However, concerns about its efficacy, potential biases, and ethical implications persist. This study examines the data-driven approaches and dependence on previous crime data used by predictive police algorithms. The primary ethical concerns raised by predictive policing include data selection, machine bias, forecast visualization and interpretation, openness and accountability, efficiency and timeliness, and stigmatization of people, places, and things. These concerns have implications for the law, particularly privacy issues. The existing legal system, primarily focused on protecting individual rights, does not address these concerns for organizations and their potential impact. Building trust is a major social problem surrounding the use of predictive policing. This review, created in collaboration with European law enforcement agencies and civil society representatives, argues that the effectiveness of predictive policing in reducing crime rates remains uncertain.