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While machine learning classifier models become more widely adopted, opaque “black-box” models remain mostly inscrutable for a variety of reasons. Since their applications increasingly involve decisions impacting the lives of humans, there is increasing demand that their predictions be understandable to humans. Of particular interest in eXplainable AI (XAI) is the interpretability of explanations, i.e., that a model’s prediction should be understandable in terms of the input features. One popular approach is LIME, which offers a model-agnostic framework for explaining any classifier. However, questions remain about the limitations and vulnerabilities of such post-hoc explainers. We have built a tool for generating synthetic tabular data sets which enables us to probe the explanation system opportunistically based on its architecture. In this paper, we report on our success in revealing a scenario where LIME’s explanation violates local faithfulness.
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A Reusable Model-agnostic Framework for Faithfully Explainable Recommendation and System Scrutability
State-of-the-art industrial-level recommender system applications mostly adopt complicated model structures such as deep neural networks. While this helps with the model performance, the lack of system explainability caused by these nearly blackbox models also raises concerns and potentially weakens the users’ trust in the system. Existing work on explainable recommendation mostly focuses on designing interpretable model structures to generate model-intrinsic explanations. However, most of them have complex structures, and it is difficult to directly apply these designs onto existing recommendation applications due to the effectiveness and efficiency concerns. However, while there have been some studies on explaining recommendation models without knowing their internal structures (i.e., model-agnostic explanations), these methods have been criticized for not reflecting the actual reasoning process of the recommendation model or, in other words, faithfulness . How to develop model-agnostic explanation methods and evaluate them in terms of faithfulness is mostly unknown. In this work, we propose a reusable evaluation pipeline for model-agnostic explainable recommendation. Our pipeline evaluates the quality of model-agnostic explanation from the perspectives of faithfulness and scrutability. We further propose a model-agnostic explanation framework for recommendation and verify it with the proposed evaluation pipeline. Extensive experiments on public datasets demonstrate that our model-agnostic framework is able to generate explanations that are faithful to the recommendation model. We additionally provide quantitative and qualitative study to show that our explanation framework could enhance the scrutability of blackbox recommendation model. With proper modification, our evaluation pipeline and model-agnostic explanation framework could be easily migrated to existing applications. Through this work, we hope to encourage the community to focus more on faithfulness evaluation of explainable recommender systems.
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- PAR ID:
- 10445534
- Date Published:
- Journal Name:
- ACM Transactions on Information Systems
- Volume:
- 42
- Issue:
- 1
- ISSN:
- 1046-8188
- Page Range / eLocation ID:
- 1 to 29
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3605357
