WebAbstract. Explanation methods shed light on the decision process of black-box classifiers such as deep neural networks. But their usefulness can be compromised because they … Webwork on exploring the vulnerabilities of black box expla-nations. For instance, there has been work demonstrating that explanations can be unstable, changing drastically even with small perturbations to inputs (Dombrowski et al. 2024; Ghorbani, Abid, and Zou 2024). Finally, recent work has argued that black box explanations can often be mislead-
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http://proceedings.mlr.press/v119/lakkaraju20a/lakkaraju20a-supp.pdf WebRobust and Stable Black Box Explanations By: Himabindu Lakkaraju, Nino Arsov and Osbert Bastani As machine learning black boxes are increasingly being deployed in real-world … binghui shen
Robust and Stable Black Box Explanations
WebSep 5, 2024 · Example explanations on VGG16. (a) and (b) are the explanations by two independent runs of LIME [34], a blackbox explanation method. (c) is the MeTFA-significant LIME explanation, where the yellow ... WebFeb 24, 2024 · The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes raised the question of how to evaluate explanations of machine learning (ML) models. While interpretability and explainability are often presented as a subjectively validated binary property, we consider it a multi-faceted concept. WebWe propose a novel framework for generating robust and stable explanations of black box models based on adversarial training. Our framework optimizes a minimax objective that … binghui shen city of hope