Towards an Automated Approach to Offender Profiling

Offender profiling seeks to infer characteristics of an offender from the observed features of crimes he or she has committed. Traditionally such an approach has been subjective and required expert opinion. Here we propose an approach based on Language Modelling which automates offender profiling allowing inferences to be drawn from large volumes of data held in police archives. An empirical study focuses on two characteristics: gender and ethnic appearance. However, the approach is generally applicable to any characteristic of a categorical nature. Language models are transparent in that they allow us to firstly indicate which actions lead to a particular profile and secondly afford identification of those features of criminal behaviour associated with a group of offenders sharing a common characteristic.