Abstract
Saliency maps can explain how deep neural networks classify images. But are they actually useful for humans? The present systematic review of 68 user studies found that while saliency maps can enhance human performance, null effects or even costs are quite common. To investigate what modulates these effects, the empirical outcomes were organised along several factors related to the human tasks, AI performance, XAI methods, images to be classified, human participants and comparison conditions. In image-focused tasks, benefits were less common than in AI-focused tasks, but the effects depended on the specific cognitive requirements. Moreover, benefits were usually restricted to incorrect AI predictions in AI-focused tasks but to correct ones in image-focused tasks. XAI-related factors had surprisingly little impact. The evidence was limited for image- and human-related factors and the effects were highly dependent on the comparison conditions. These findings may support the design of future user studies.
Abstract (translated)
突出度图可以解释深度神经网络如何对图像进行分类。但这些方法对于人类来说实际上有用吗？目前对68个用户研究的系统综述发现，尽管突出度图可以提高人类的表现，但无效应或甚至成本相当常见。为了研究这些影响，实证结果按与人类任务、人工智能性能、XAI方法、要分类的图像以及人类参与者和比较条件相关分成几个因素进行了组织。在图像关注任务中，增强人类表现的效果不如在人工智能关注任务中，但具体认知要求不同。此外，在人工智能关注任务中，增强效果通常仅限于错误的AI预测，而在图像关注任务中，增强效果通常是正确的。XAI相关因素对影响的影响非常小。证据对于图像和人类相关因素来说有限，而影响则高度依赖于比较条件。这些发现可能支持未来用户研究的设计。
URL
https://arxiv.org/abs/2404.16042
