Evaluation of Saliency-based Explainability Method

• URL: http://arxiv.org/abs/2106.12773v1
• Date: Thu, 24 Jun 2021 05:40:50 GMT
• Title: Evaluation of Saliency-based Explainability Method
• Authors: Sam Zabdiel Sunder Samuel, Vidhya Kamakshi, Namrata Lodhi and Narayanan C Krishnan
• Abstract summary: A class of Explainable AI (XAI) methods provide saliency maps to highlight part of the image a CNN model looks at to classify the image as a way to explain its working. These methods provide an intuitive way for users to understand predictions made by CNNs. Other than quantitative computational tests, the vast majority of evidence to highlight that the methods are valuable is anecdotal.
• Score: 2.733700237741334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A particular class of Explainable AI (XAI) methods provide saliency maps to highlight part of the image a Convolutional Neural Network (CNN) model looks at to classify the image as a way to explain its working. These methods provide an intuitive way for users to understand predictions made by CNNs. Other than quantitative computational tests, the vast majority of evidence to highlight that the methods are valuable is anecdotal. Given that humans would be the end-users of such methods, we devise three human subject experiments through which we gauge the effectiveness of these saliency-based explainability methods.

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