感知差距：为什么我们需要为辅助技术提供可访问的XAI
The Perceptual Gap: Why We Need Accessible XAI for Assistive Technologies
AI总结：
本文探讨了为辅助技术用户提供可访问的XAI的重要性，指出现有XAI方法缺乏对感官障碍用户的考虑，并提出了未来研究的方向。
摘要：
人工智能系统被广泛用于帮助有感官障碍的人，如视力或听力丧失，来感知或导航周围的世界。这包括描述他们无法触摸的图像或物体、阅读文档、自动为语音添加字幕等任务。目前，用于这些任务的模型基于深度神经网络，因此是黑箱。可解释人工智能（XAI）描述了能够解释模型为何给出特定输出的方法。然而，现有的XAI方法很少是可访问的，或为残疾用户量身定制。在本文中，我们调研了现有XAI工作，重点关注以人为中心和可访问性为中心的方法或评估。我们展示了几乎没有XAI工作考虑了有感官障碍的人，许多典型解释对他们来说难以理解，并提出了未来在可访问的人类中心XAI领域工作的可能方向。
英文摘要：
Artificial intelligence systems are widely used by people with sensory disabilities, like loss of vision or hearing, to help perceive or navigate the world around them. This includes tasks like describing an image or object they cannot touch, reading documents, automatically captioning speech, and so on. Presently, models used for these tasks are based on deep neural networks and are thusly black boxes. Explainable AI (XAI) describes methods that can explain why a model gave the output it did. However, existing XAI methodologies are rarely accessible or designed with disabled users in mind. In this paper, we survey existing work in XAI with a focus on human-centered and accessibility-centered approaches or evaluations. We show that there is next-to-no XAI work that accounts for people with sensory disabilities, that many typical explanations are difficult for them to comprehend, and propose possible avenues for future work in Accessible Human-Centered XAI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
