Computational Inference for Evidential Reasoning in Support of Judicial Proof

Abstract

The process of judicial proof accrues evidence to confirm or deny hypotheses about world events relevant to a legal case. Software applications that seek to support this process must provide the user with sophisticated capabilities to manipulate evidential reasoning for legal cases. This requires computational techniques to represent the actors, entities, events, and context of world situations to structure alternative hypotheses interpreting evidence and to execute processes that draw inferences about the truth of hypotheses by assessing the relevance and weight of evidence to confirm or deny the hypotheses. Bayesian inference networks are combined with knowledge representations from artificial intelligence to structure and analyze evidential argumentation. The infamous 1994 Raddad murder trial in Nice, France provides a backdrop against which we illustrate the application of these techniques to evidential reasoning in support of judicial proof.

We gratefully acknowledge the students of George Mason University course UNFT 819 and scientists at IET, Inc. for providing useful feedback on the models described in this Article. Special thanks go to Dr. Keung-Chi Ng for performing the sensitivity analyses reported in the Article.