Abstract

Predictive policing involves the usage of analytical and statistical methods and algorithms by machines to identify ‘hotspots’ for crime, where there is usually a repeated trend in crime so as to ensure effective police intervention. Predictive policing tools rely on various data sources, including historical crime data, demographic information, socio-economic data, and even weather conditions. With a shift towards more preventive measures in dealing with crime, rather than reactionary measures, predictive policing becomes more important. This research paper explores the evolving landscape of predictive policing and its implications for the administration of justice, with a specific focus on its implementation in India. Predictive policing, leveraging advanced data analytics and machine learning algorithms, has emerged as a promising tool to enhance law enforcement strategies, optimize resource allocation, and prevent criminal activities. The study begins by providing an overview of predictive policing methodologies and their global applications, emphasizing their potential to augment traditional policing methods. The research is based on a survey of existing articles and documents from countries that have adopted predictive policing, although the material is not exhaustive. The paper will examine the barriers to preventive policing in India and analyse the impact of large-scale adoption of this technology in the country, suited to its unique socio-cultural and legal framework. In addition, the study will delve into the possible ethical dimensions of predictive policing. It examines the compliance of these initiatives with privacy laws, constitutional rights, and ethical standards, addressing potential biases in algorithms and the impact on individual liberties. The research underscores the need for a comprehensive policy framework that guides the responsible use of predictive policing tools in India. It makes recommendations to address legal and ethical considerations, ensuring transparency, fairness, and accountability in the deployment of these technologies.

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