Building the case for restricted use of predictive policing tools in India
Keywords:Preventive detention, algorithmic outcomes, restricted use, marginalised communities, criminal justice norms
The use of predictive policing by law enforcement agencies has lately proliferated across several states in India. Predictive policing uses machine learning models to analyse substantial amounts of crime data to map and predict crimes, offenders, identities of the perpetrators and victims of crime. The aim is to enable the efficient allocation of limited resources at the disposal of law enforcement agencies for crime prevention. However, evidence suggests that predictive policing, in its current form, suffers from serious limitations. Inferior quality datasets are being used to train algorithms and citizens are unable to contest inaccurate algorithmic outcomes that could lead to their preventive detentions, often to the detriment of criminal justice norms and constitutional fundamental rights of citizens. More importantly, in the absence of oversight mechanisms restricting its use, the use of predictive policing has ended up reinforcing and amplifying police biases in law enforcement. This paper focuses, in particular, on the many ways in which predictive policing increases the risk of preventive detentions under section 151 of the Code of Criminal Procedure (CrPC), 1973 and proposes recommendations to preclude possibilities of unlawful preventive detentions using predictive policing tools. In part 1, the paper reviews the practice of preventive detention of potential offenders by the police in India—under section 151 of the CrPC. In part 2, I build the case for restricted use of predictive policing tools to only predict the location, type and time of the crime and not potential offenders or victims. In the last part, I recommend substantive measures and procedural safeguards to secure the reliable and accountable use of place-based predictive policing tools in India.
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