Abstract
Predictive policing (Pp) relates to identifying potentially related offences, similar criminal attributes and potential criminal activity, in order to take actionable measures in deterring crime. Similarly, Legal Decision Making Process (LDMP) considers some level of probabilistic reasoning in deriving logical evidence from crime incidents. Bayesian Networks (BN) have great potential in contributing to the area of Pp and LDMP. Being based on probabilistic reasoning, they can assess uncertainty in crime related attributes and derive useful evidence based on crime incident observations or evidential data. For example, in a particular context of crime investigation, BN based inference could help collect useful evidence about a crime scenario or incident. Such evidence promotes effective legal decision making process and can assist public safety and security agencies in allocating resources in an optimal fashion. This chapter reports on various application areas of BN in the crime domain, highlights the potential of BN and presents “thought experiments” on how offender characteristics could be modelled for decision support in legal matters. The chapter further reports on the performance of empirical analysis in the legal decision support process, in order to elucidate the practical relevance and challenges of using BN in the crime domain.
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