ConLUX: Concept-Based Local Unified Explanations

ICLR 2025 Conference Submission1760 Authors

Keywords: local model-agnostic explanations, post-hoc XAI, concept-based XAI

TL;DR: We have proposed ConLUX, a general framework that automatically extracts high-level concepts and incorporates them into existing local model-agnostic explanation techniques.

Abstract:

With the rapid advancements of various machine learning models, there is a significant demand for model-agnostic explanation techniques, which can explain these models across different architectures.
Mainstream model-agnostic explanation techniques generate local explanations based on basic features (e.g., words for text models and (super-)pixels for image models). However, these explanations often do not align with the decision-making processes of the target models and end-users, resulting in explanations that are unfaithful and difficult for users to understand.
On the other hand, concept-based techniques provide explanations based on high-level features (e.g., topics for text models and objects for image models), but most are model-specific or require additional pre-defined external concept knowledge.
To address this limitation, we propose ConLUX, a general framework to provide concept-based local explanations for any machine learning models.
Our key insight is that we can automatically extract high-level concepts from large pre-trained models, and uniformly extend existing local model-agnostic techniques to provide unified concept-based explanations.
We have instantiated ConLUX on four different types of explanation techniques: LIME, Kernel SHAP, Anchor, and LORE, and applied these techniques to text and image models.
Our evaluation results demonstrate that 1) compared to the vanilla versions, ConLUX offers more faithful explanations and makes them more understandable to users, and 2) by offering multiple forms of explanations, ConLUX outperforms state-of-the-art concept-based explanation techniques specifically designed for text and image models, respectively.

Primary Area: interpretability and explainable AI

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 1760