Explainable Automated Reasoning in Law using Probabilistic Epistemic Argumentation

TL;DR

This paper presents a probabilistic epistemic argumentation framework for automated legal reasoning, enhancing transparency and handling uncertainty in legal decision support systems.

Contribution

It introduces a novel scheme to model legal cases with probabilistic epistemic argumentation, enabling automatic explanations and efficient reasoning.

Findings

Framework handles cyclic structures and imprecise probabilities

Guarantees polynomial-time probabilistic reasoning

Supports automatic generation of explanations for legal decisions

Abstract

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.