Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study

TL;DR

This study evaluates the effectiveness of saliency map explanations for CNNs through a user study, revealing they help understand certain features but are limited in predicting network outputs for new images.

Contribution

It provides empirical insights into how saliency maps influence user understanding and highlights the need to explore explanations beyond instance-level methods.

Findings

Saliency maps help users learn about specific image features.

Saliency maps offer limited assistance in predicting CNN outputs for new images.

Implications for designing more effective explainable AI tools.

Abstract

Convolutional neural networks (CNNs) offer great machine learning performance over a range of applications, but their operation is hard to interpret, even for experts. Various explanation algorithms have been proposed to address this issue, yet limited research effort has been reported concerning their user evaluation. In this paper, we report on an online between-group user study designed to evaluate the performance of "saliency maps" - a popular explanation algorithm for image classification applications of CNNs. Our results indicate that saliency maps produced by the LRP algorithm helped participants to learn about some specific image features the system is sensitive to. However, the maps seem to provide very limited help for participants to anticipate the network's output for new images. Drawing on our findings, we highlight implications for design and further research on explainable AI. In particular, we argue the HCI and AI communities should look beyond instance-level explanations.