Classification Rules Explain Machine Learning

Matteo Cristani, Francesco Olvieri, Tewabe Workneh, Luca Pasetto, Claudio Tomazzoli

2022

Abstract

We introduce a general model for explainable Artificial Intelligence that identifies an explanation of a Machine Learning method by classification rules. We define a notion of distance between two Machine Learning methods, and provide a method that computes a set of classification rules that, in turn, approximates another black box method to a given extent. We further build upon this method an anytime algorithm that returns the best approximation it can compute within a given interval of time. This anytime method returns the minimum and maximum difference in terms of approximation provided by the algorithm and uses it to determine whether the obtained approximation is acceptable. We then illustrate the results of a few experiments on three different datasets that show certain properties of the approximations that should be considered while modelling such systems. On top of this, we design a methodology for constructing approximations for ML, that we compare to the no-methods approach typically used in current studies on the explainable artificial intelligence topic.

Paper Citation

in Harvard Style

Cristani M., Olvieri F., Workneh T., Pasetto L. and Tomazzoli C. (2022). Classification Rules Explain Machine Learning. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 897-904. DOI: 10.5220/0010927300003116

in Bibtex Style

@conference{icaart22,
author={Matteo Cristani and Francesco Olvieri and Tewabe Workneh and Luca Pasetto and Claudio Tomazzoli},
title={Classification Rules Explain Machine Learning},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={897-904},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010927300003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Classification Rules Explain Machine Learning
SN - 978-989-758-547-0
AU - Cristani M.
AU - Olvieri F.
AU - Workneh T.
AU - Pasetto L.
AU - Tomazzoli C.
PY - 2022
SP - 897
EP - 904
DO - 10.5220/0010927300003116