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Analysis Study Research Paper | Law | Volume 15 Issue 4, April 2026 | Pages: 44 - 53 | India
Predictive Policing Algorithms: The Promise and Peril of AI-Driven Crime Control
Abstract: Predictive policing refers to the use of artificial intelligence (AI) and algorithmic models to forecast crime patterns and identify potential offenders or victims. It has emerged as a cutting-edge tool for proactive law enforcement worldwide. This approach shifts policing from a reactive response to crimes towards a proactive strategy aimed at preventing crime. By analysing historical crime patterns, predictive systems identify high-risk areas and individuals, helping police allocate resources more efficiently. In India, initiatives such as MARVEL in Maharashtra and CMAPS in Telangana exemplify this move towards data-driven policing. While these technologies hold promise for improving crime prevention and resource management, they also raise serious constitutional and ethical concerns. The key challenges stem from the reliance on historical crime data, which often contains biases that disproportionately affect marginalised communities. Such systems risk perpetuating and amplifying discrimination under the guise of scientific objectivity. Furthermore, the lack of transparency in how these predictive tools operate limits accountability and the ability of individuals to contest decisions that impact their rights. In the Indian context, these concerns highlight a pressing need to reconcile the benefits of predictive policing with constitutional protections of equality, due process, and privacy, ensuring these tools are implemented with appropriate safeguards, oversight, and fairness.
Keywords: predictive policing, artificial intelligence, algorithmic policing, crime forecasting, due process, equality before law
How to Cite?: Debalina Roy, "Predictive Policing Algorithms: The Promise and Peril of AI-Driven Crime Control", Volume 15 Issue 4, April 2026, International Journal of Science and Research (IJSR), Pages: 44-53, https://www.ijsr.net/getabstract.php?paperid=SR26325204616, DOI: https://dx.dx.doi.org/10.21275/SR26325204616