The Legal Implications of Predictive Policing Algorithms: Bias, Oversight, and Public Accountability

Abstract

Predictive policing algorithms have become an increasingly prominent feature of modern law-enforcement systems, reshaping operational decision-making through data-driven forecasting and automated risk assessment. As these technologies expand, they introduce complex legal, ethical, and societal challenges that demand critical evaluation. This narrative review synthesizes current knowledge on the functioning of predictive policing systems, highlighting how algorithmic processes rooted in historical crime data, surveillance infrastructures, and machine-learning models influence patterns of policing. The analysis demonstrates that algorithmic bias can reinforce racial profiling, socioeconomic disparities, and spatialized over-policing, raising concerns about compliance with equality principles, due-process protections, and human-rights standards. It also examines the structural mechanisms—such as feedback loops, model opacity, and proprietary constraints—that complicate efforts to contest discriminatory outcomes or ensure evidentiary fairness in judicial proceedings. Furthermore, the review explores the governance challenges shaping the regulatory landscape, including limitations of existing data-protection laws, weaknesses in administrative oversight, and the growing influence of private vendors over public-sector policing practices. These gaps, combined with limited transparency, insufficient technical literacy, and uneven democratic oversight, create significant obstacles to achieving accountability. By analyzing the intersection of technology, law, and institutional practice, this article offers a comprehensive framework for understanding how predictive policing affects civil liberties, public trust, and the legitimacy of law enforcement. The review concludes by emphasizing the need for robust regulatory reforms grounded in transparency, human-rights protections, and meaningful public oversight to ensure that algorithmic policing evolves in ways that support fairness, democratic governance, and societal well-being.