Faithful Counterfactual Visual Explanations (FCVE)

Article

Khan, B., Tariq, S., Zia, T., Ahsan, M. and Windridge, D. 2024. Faithful Counterfactual Visual Explanations (FCVE). Knowledge-Based Systems. 294. https://doi.org/10.1016/j.knosys.2024.111668
TypeArticle
TitleFaithful Counterfactual Visual Explanations (FCVE)
AuthorsKhan, B., Tariq, S., Zia, T., Ahsan, M. and Windridge, D.
Abstract

Deep learning models in computer vision have made remarkable progress, but their lack of transparency and interpretability remains a challenge. The development of explainable AI can enhance the understanding and performance of these models. However, existing techniques often struggle to provide convincing explanations that non-experts easily understand, and they cannot accurately identify models’ intrinsic decision-making processes. To address these challenges, we propose to develop a counterfactual explanation (CE) model that balances plausibility and faithfulness. This model generates easy-to-understand visual explanations by making minimum changes necessary in images without altering the pixel data. Instead, the proposed method identifies internal concepts and filters learned by models and leverages them to produce plausible counterfactual explanations. The provided explanations reflect the internal decision-making process of the model, thus ensuring faithfulness to the model.

KeywordsExplainable AI; Visual explanation; Counterfactual
Sustainable Development Goals9 Industry, innovation and infrastructure
Middlesex University ThemeCreativity, Culture & Enterprise
Research GroupArtificial Intelligence group
PublisherElsevier
JournalKnowledge-Based Systems
ISSN0950-7051
Electronic1872-7409
Publication dates
Online26 Mar 2024
Print21 Jun 2024
Publication process dates
Submitted20 Nov 2023
Accepted18 Mar 2024
Deposited09 Apr 2024
Output statusPublished
Accepted author manuscript
License
File Access Level
Open
Copyright Statement

© 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

Digital Object Identifier (DOI)https://doi.org/10.1016/j.knosys.2024.111668
Web of Science identifierWOS:001229329700001
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