An Argumentative Explanation Framework for Generalized Reason Model with Inconsistent Precedents
This work addresses a gap in AI and Law for practitioners needing explanations in inconsistent precedent scenarios, though it is incremental as it builds on existing frameworks.
The paper tackles the problem of explaining reasoning in AI and Law when precedents are inconsistent, by extending the derivation state argumentation framework to provide argumentative explanations for a generalized reason model.
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.