research-article

Generating Perturbation-based Explanations with Robustness to Out-of-Distribution Data

Authors:

Caleb Chen Cao,

Lei ChenAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 3594 - 3605

https://doi.org/10.1145/3485447.3512254

Published: 25 April 2022 Publication History

Abstract

Perturbation-based techniques are promising for explaining black-box machine learning models due to their effectiveness and ease of implementation. However, prior works have faced the problem of Out-of-Distribution (OoD) — an artifact of randomly perturbed data becoming inconsistent with the original dataset, degrading the reliability of generated explanations, which is still under-explored according to our best knowledge. This work addresses the OoD issue by designing a simple yet effective module that can quantify the affinity between the perturbed data and the original dataset distribution. Specifically, we penalize the influences of unreliable OoD data for the perturbed samples by integrating the inlier scores and prediction results of the target models, thereby making the final explanations more robust. Our solution is shown to be compatible with the most popular perturbation-based XAI algorithms: RISE, OCCLUSION, and LIME. Extensive experiments confirmed that our methods exhibit superior performance in most cases with computational and cognitive metrics. In particular, we point out the degradation problem of RISE algorithm for the first time. With our design, the performance of RISE can be boosted significantly. Besides, our solution also resolves a fundamental problem with a faithfulness indicator, a commonly used evaluation metric of XAI algorithms that appears sensitive to the OoD issue.

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Chen ZZhang JNi JLi XBian YIslam MMondal AWei HLuo DSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Generating in-distribution proxy graphs for explaining graph neural networksProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692372(7712-7730)Online publication date: 21-Jul-2024
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Index Terms

Generating Perturbation-based Explanations with Robustness to Out-of-Distribution Data
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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Funding Sources

the Hong Kong RIF Project
Theme-based project
Hong Kong ITC ITF grants
Guangdong Basic and Applied Basic Research Foundation
Didi-HKUST joint research lab
National Key Research and Development Program of China Grant
the Hong Kong RGC GRF Project
the Hong Kong AOE Project
the Hong Kong CRF Project
Microsoft Research Asia Collaborative Research Grant
HKUST-NAVER/LINE AI Lab
HKUST-Webank joint research lab grants
China NSFC

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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https://doi.org/10.1016/j.ipm.2024.103900
Chen ZZhang JNi JLi XBian YIslam MMondal AWei HLuo DSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Generating in-distribution proxy graphs for explaining graph neural networksProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692372(7712-7730)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692372
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https://doi.org/10.1111/tops.12737
Longo LBrcic MCabitza FChoi JConfalonieri RSer JGuidotti RHayashi YHerrera FHolzinger AJiang RKhosravi HLecue FMalgieri GPáez ASamek WSchneider JSpeith TStumpf S(2024)Explainable Artificial Intelligence (XAI) 2.0Information Fusion10.1016/j.inffus.2024.102301106:COnline publication date: 25-Jun-2024
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