Multi-Granular Evaluation of Diverse Counterfactual Explanations

Yining Yuan; Kevin McAreavey; Shujun Li; Weiru Liu

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Paper

Multi-Granular Evaluation of Diverse Counterfactual Explanations

Topics: Constraint Satisfaction; Data Science; Explainable Artificial Intelligence; Interpretable Artificial Intelligence; Machine Learning

In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, 186-197, 2024 , Rome, Italy

Authors: Yining Yuan ¹ ; Kevin McAreavey ¹ ; Shujun Li ² and Weiru Liu ¹

Affiliations: ¹ School of Engineering Mathematics and Technology, University of Bristol, U.K. ; ² School of Computing, University of Kent, U.K.

Keyword(s): Counterfactual Explanations, Explainable AI.

Abstract: As a popular approach in Explainable AI (XAI), an increasing number of counterfactual explanation algorithms have been proposed in the context of making machine learning classifiers more trustworthy and transparent. This paper reports our evaluations of algorithms that can output diverse counterfactuals for one instance. We first evaluate the performance of DiCE-Random, DiCE-KDTree, DiCE-Genetic and Alibi-CFRL, taking XGBoost as the machine learning model for binary classification problems. Then, we compare their suggested feature changes with feature importance by SHAP. Moreover, our study highlights that synthetic counterfactuals, drawn from the input domain but not necessarily the training data, outperform native counter-factuals from the training data regarding data privacy and validity. This research aims to guide practitioners in choosing the most suitable algorithm for generating diverse counterfactual explanations.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Yuan, Y.; McAreavey, K.; Li, S. and Liu, W. (2024). Multi-Granular Evaluation of Diverse Counterfactual Explanations. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 186-197. DOI: 10.5220/0012349900003636

@conference{icaart24,
author={Yining Yuan. and Kevin McAreavey. and Shujun Li. and Weiru Liu.},
title={Multi-Granular Evaluation of Diverse Counterfactual Explanations},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={186-197},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012349900003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Multi-Granular Evaluation of Diverse Counterfactual Explanations
SN - 978-989-758-680-4
IS - 2184-433X
AU - Yuan, Y.
AU - McAreavey, K.
AU - Li, S.
AU - Liu, W.
PY - 2024
SP - 186
EP - 197
DO - 10.5220/0012349900003636
PB - SciTePress