Predictive Policing and Artificial Intelligence in India's Criminal Justice System: Constitutional Limits and a Framework for Accountable Use

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Artificial intelligence is entering Indian criminal justice through expanding data infrastructure, analytics and machine-learning tools. Predictive policing promises better deployment of scarce police resources, earlier identification of crime patterns and faster investigation. Its constitutional difficulty, however, is not confined to technical error. Police data are produced by earlier enforcement choices; a model trained on such data may convert unequal visibility into a forecast of unequal risk. Opacity can then prevent an affected person from discovering or contesting the basis of surveillance, intervention, bail or sentencing. This paper uses a doctrinal and comparative method to examine predictive policing under Articles 14 and 21 of the Constitution of India, the emerging Digital Personal Data Protection framework, the United States decision in State v. Loomis, and the European Union's risk-based regulation of artificial intelligence. It argues for a constitutional permission line: place-based analytics may assist non-coercive resource allocation when supported by law, necessity, audited data and public oversight, but a person-based score must never create suspicion, justify coercive police action, or determine a judicial outcome. The paper proposes statutory authorization, algorithmic impact assessment, data-quality and equality audits, meaningful disclosure, contestability, human responsibility, procurement controls, independent supervision and sunset review. These safeguards treat efficiency as a legitimate public objective while preserving the rule that coercive state power must rest on lawful, individualized and reviewable reasons.