Explainable AI through the Learning of Arguments
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.
Cite
@article{arxiv.2202.00383,
title = {Explainable AI through the Learning of Arguments},
author = {Jonas Bei and David Pomerenke and Lukas Schreiner and Sepideh Sharbaf and Pieter Collins and Nico Roos},
journal= {arXiv preprint arXiv:2202.00383},
year = {2022}
}
Comments
Presented at the 33rd BeNeLux AI Conference (BNAIC/BENELEARN 2021)