Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method

Pattern Recognition 127 (2022): 108604 Explainable Artificial Intelligence (XAI) has in recent years become a well-suited framework to generate human understandable explanations of "black-box" models. In this paper, a novel XAI visual explanation algorithm known as the Similarity Differenc...

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Bibliographic Details
Main Authors Muddamsetty, Satya M, Jahromi, Mohammad N. S, Ciontos, Andreea E, Fenoy, Laura M, Moeslund, Thomas B
Format Journal Article
LanguageEnglish
Published 26.01.2021
Subjects
Computer Science - Artificial Intelligence
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Human-Computer Interaction
Computer Science - Learning
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DOI10.48550/arxiv.2101.10710

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Summary:Pattern Recognition 127 (2022): 108604 Explainable Artificial Intelligence (XAI) has in recent years become a well-suited framework to generate human understandable explanations of "black-box" models. In this paper, a novel XAI visual explanation algorithm known as the Similarity Difference and Uniqueness (SIDU) method that can effectively localize entire object regions responsible for prediction is presented in full detail. The SIDU algorithm robustness and effectiveness is analyzed through various computational and human subject experiments. In particular, the SIDU algorithm is assessed using three different types of evaluations (Application, Human and Functionally-Grounded) to demonstrate its superior performance. The robustness of SIDU is further studied in the presence of adversarial attack on "black-box" models to better understand its performance. Our code is available at: https://github.com/satyamahesh84/SIDU_XAI_CODE.
DOI:10.48550/arxiv.2101.10710