XAI-LAW: A Logic Programming Tool for Modeling, Explaining, and Learning Legal Decisions

TL;DR

This paper introduces XAI-LAW, a logic programming tool that models, explains, and learns legal decisions in the Italian Criminal Code using Answer Set Programming, enhancing interpretability and aiding legal experts.

Contribution

It presents a novel system combining ASP and inductive logic programming to model, explain, and learn legal rules from judicial cases, improving transparency in legal decision-making.

Findings

Successfully modeled Italian Criminal Code articles in ASP

Generated explanations for legal decisions using supportedness in stable models

Automatically learned legal rules from judicial case examples

Abstract

We propose an approach to model articles of the Italian Criminal Code (ICC), using Answer Set Programming (ASP), and to semi-automatically learn legal rules from examples based on prior judicial decisions. The developed tool is intended to support legal experts during the criminal trial phase by providing reasoning and possible legal outcomes. The methodology involves analyzing and encoding articles of the ICC in ASP, including "crimes against the person" and property offenses. The resulting model is validated on a set of previous verdicts and refined as necessary. During the encoding process, contradictions may arise; these are properly handled by the system, which also generates possible decisions for new cases and provides explanations through a tool that leverages the "supportedness" of stable models. The automatic explainability offered by the tool can also be used to clarify the logic behind judicial decisions, making the decision-making process more interpretable. Furthermore, the tool integrates an inductive logic programming system for ASP, which is employed to generalize legal rules from case examples.