Explainable Automated Reasoning in Law using Probabilistic Epistemic Argumentation
This work addresses the need for transparent and objective decision support in law, though it appears incremental as it builds on existing non-classical reasoning approaches.
The paper tackled the problem of applying automated reasoning to legal cases by introducing a probabilistic epistemic argumentation framework, which models legal cases, handles evidence uncertainty, and generates explanations for decisions while ensuring polynomial-time reasoning.
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.