An Argumentative Explanation Framework for Generalized Reason Model with Inconsistent Precedents

TL;DR

This paper extends an argumentation framework to explain reasoning in AI and Law models that handle inconsistent precedents, broadening the applicability of case-based reasoning.

Contribution

It introduces an argumentative explanation method for the generalized reason model accommodating inconsistent precedents, filling a gap in existing explanation approaches.

Findings

Extended the derivation state argumentation framework (DSA-framework)

Provided a new explanation method for generalized reason models

Enhanced reasoning with inconsistent precedents in AI and Law

Abstract

Precedential constraint is one foundation of case-based reasoning in AI and Law. It generally assumes that the underlying set of precedents must be consistent. To relax this assumption, a generalized notion of the reason model has been introduced. While several argumentative explanation approaches exist for reasoning with precedents based on the traditional consistent reason model, there has been no corresponding argumentative explanation method developed for this generalized reasoning framework accommodating inconsistent precedents. To address this question, this paper examines an extension of the derivation state argumentation framework (DSA-framework) to explain the reasoning according to the generalized notion of the reason model.