Predictive Policing – In Defense of ‘True Positives’

Abstract

Predictive policing has triggered a heated debate around the issue of ‘false positives’. Biased machine training can wrongly classify individuals as high risk simply as a result of belonging to a particular ethnic group and many agree such persons should not have to shoulder the burden of over-policing due to an inherent stochastic problem. The paper takes a pragmatic stand and argues that ‘true positives’, i.e. individuals who have been correctly identified as perpetrators, offer the best opportunity to address the issue of biased profiling.

The first reason is purely pragmatic – they are already party to a criminal investigation and, as such, have a strong incentive to challenge law enforcement methods and scrutinize policing methods on an individual basis. The second reason is more general (and commonly subscribed to) – that discriminatory stops and searches are inherently unfair, threaten social peace, and frustrate targeted groups.

To create an efficient legal tool against discriminatory law enforcement, defence should be entitled to contest a conviction for biased predictive policing, with a specific exclusionary rule protecting ‘true positives’ against the use of tainted evidence.

Keywords: predictive policing, profiling, criminal proceedings, exclusionary rules, reasonable suspicion