SAFE: Saliency-Aware Counterfactual Explanations for DNN-based Automated Driving Systems

• URL: http://arxiv.org/abs/2307.15786v1
• Date: Fri, 28 Jul 2023 19:56:01 GMT
• Title: SAFE: Saliency-Aware Counterfactual Explanations for DNN-based Automated Driving Systems
• Authors: Amir Samadi, Amir Shirian, Konstantinos Koufos, Kurt Debattista and Mehrdad Dianati
• Abstract summary: A CF explainer computes the minimum modifications required to cross the model's decision boundary. Current deep generative CF models often work with user-selected features rather than focusing on the discriminative features of the black-box model. We propose in this paper a novel approach that leverages saliency maps to generate more informative CF explanations.
• Score: 10.40211479079817
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A CF explainer identifies the minimum modifications in the input that would alter the model's output to its complement. In other words, a CF explainer computes the minimum modifications required to cross the model's decision boundary. Current deep generative CF models often work with user-selected features rather than focusing on the discriminative features of the black-box model. Consequently, such CF examples may not necessarily lie near the decision boundary, thereby contradicting the definition of CFs. To address this issue, we propose in this paper a novel approach that leverages saliency maps to generate more informative CF explanations. Source codes are available at: https://github.com/Amir-Samadi//Saliency_Aware_CF.

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