Explainable AI through the Learning of Arguments

TL;DR

This paper explores how learning arguments, as interpretable rules, can enhance explainable AI by applying case-based argument learning to larger datasets and comparing it with decision trees and the HeRO algorithm.

Contribution

It extends Verheij's case model approach for learning arguments to larger datasets and compares its effectiveness with decision trees and the HeRO algorithm.

Findings

Verheij's approach can be adapted for larger datasets.

Learning arguments provides interpretable models.

Comparison shows strengths and limitations of each method.

Abstract

Learning arguments is highly relevant to the field of explainable artificial intelligence. It is a family of symbolic machine learning techniques that is particularly human-interpretable. These techniques learn a set of arguments as an intermediate representation. Arguments are small rules with exceptions that can be chained to larger arguments for making predictions or decisions. We investigate the learning of arguments, specifically the learning of arguments from a 'case model' proposed by Verheij [34]. The case model in Verheij's approach are cases or scenarios in a legal setting. The number of cases in a case model are relatively low. Here, we investigate whether Verheij's approach can be used for learning arguments from other types of data sets with a much larger number of instances. We compare the learning of arguments from a case model with the HeRO algorithm [15] and learning a decision tree.