Counterfactuals in Fuzzy Relational Models-Reference-Cited by-同舟云学术

Counterfactuals in Fuzzy Relational Models

Published:2024-03-29 Issue: Volume: Page:
ISSN:
Container-title:
language:
Short-container-title:

Author:

Al-Hmouz Rami¹,Pedrycz Witold¹,Ammari Ahmed¹

Affiliation:

1. Sultan Qaboos University

Abstract

Abstract Given rapidly growing requirements for explainability, counterfactual explanations have gained interest in Machine Learning systems. This study investigates this timely problem in fuzzy relational systems described by fuzzy relational equations and develops a detailed solution to the counterfactual problems encountered in this setting. An underlying optimization problem is formulated, and its gradient-based solution is constructed. It is also demonstrated that the non-uniqueness of the derived solution is conveniently formalized and quantified by admitting a result coming in the form of information granules of a higher type, namely type-2 or interval-valued fuzzy set. The construction of the solution in this format is realized by invoking the principle of justifiable granularity. We also discuss ways of designing fuzzy relations and elaborate on methods of carrying out counterfactual explanation in rule-based models. Illustrative examples are included to present the performance of the method and interpret the obtained results.

Publisher

Research Square Platform LLC

Reference19 articles.

1. Christoph M (2023) Interpretable Machine Learning: A Guide for Making Black Box Models Explainable

2. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible;Barredo AA;AI Inform Fusion,2020

3. Kabra M, Robie A, Branson K (2015) Understanding classifier errors by examining influential neighbors, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June, pp. 3917–3925

4. Bibal A (2016) Interpretability of Machine Learning Models and Representations: an Introduction, in ESANN 2016 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (April), pp. 77–82

5. Biran O, Cotton C (2017) Explanation and Justification in Machine Learning: A Survey, in International Joint Conference on Artificial Intelligence Workshop on Explainable Artificial Intelligence (IJCAIXAI)