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arXiv · 2307.15786

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

Amir Samadi·Amir Shirian·Konstantinos Koufos·Kurt Debattista·Mehrdad Dianati
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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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Amir Samadi, Amir Shirian, Konstantinos Koufos, Kurt Debattista, Mehrdad Dianati. 2023-07-28. SAFE: Saliency-Aware Counterfactual Explanations for DNN-based Automated Driving Systems. https://arxiv.org/abs/2307.15786

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