Explainable Automated Reasoning in Law using Probabilistic Epistemic
  Argumentation
  Applying automated reasoning tools for decision support and analysis in law
has the potential to make court decisions more transparent and objective. Since
there is often uncertainty about the accuracy and relevance of evidence,
non-classical reasoning approaches are required. Here, we investigate
probabilistic epistemic argumentation as a tool for automated reasoning about
legal cases. We introduce a general scheme to model legal cases as
probabilistic epistemic argumentation problems, explain how evidence can be
modeled and sketch how explanations for legal decisions can be generated
automatically. Our framework is easily interpretable, can deal with cyclic
structures and imprecise probabilities and guarantees polynomial-time
probabilistic reasoning in the worst-case.
