Ordyniak, S. orcid.org/0000-0003-1935-651X, Paesani, G., Rychlicki, M. et al. (1 more author) (2024) Explaining Decisions in ML Models: a Parameterized Complexity Analysis. In: Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning (KR 2024). 21st International Conference on Principles of Knowledge Representation and Reasoning (KR 2024), 02-08 Nov 2024, Hanoi, Vietnam. IJCAI Organization , pp. 563-573. ISBN 978-1-956792-05-8
Abstract
This paper presents a comprehensive theoretical investigation into the parameterized complexity of explanation problems in various machine learning (ML) models. Contrary to the prevalent black-box perception, our study focuses on models with transparent internal mechanisms. We address two principal types of explanation problems: abductive and contrastive, both in their local and global variants. Our analysis encompasses diverse ML models, including Decision Trees, Decision Sets, Decision Lists, Ordered Binary Decision Diagrams, Random Forests, and Boolean Circuits, and ensembles thereof, each offering unique explanatory challenges. This research fills a significant gap in explainable AI (XAI) by providing a foundational understanding of the complexities of generating explanations for these models. This work provides insights vital for further research in the domain of XAI, contributing to the broader discourse on the necessity of transparency and accountability in AI systems.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Explanation, abduction and diagnosis, Computational aspects of knowledge representation, Knowledge representation languages |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Algorithms & Complexity |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/V00252X/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Aug 2024 11:09 |
Last Modified: | 08 Nov 2024 16:42 |
Published Version: | https://proceedings.kr.org/2024/53/ |
Status: | Published |
Publisher: | IJCAI Organization |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:215408 |