透过人工智能的眼睛：增强现实中的情境化可解释性
Seeing through the Eyes of AI: Situated Explainability in Augmented Reality
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中文总结 AI 辅助
本文提出利用增强现实在用户工作空间中实时呈现空间可解释性信息，以解决传统2D解释与物理环境脱节的问题，并展示已知可解释性方法在AR中的应用及用户见解。
中文摘要 AI 辅助
可解释人工智能（AI）使人类能够理解和解释AI模型的决策。可解释性不是让模型成为黑箱，而是帮助人类理解AI模型的行为。现有的可解释AI方法通常在2D显示器上使用预先记录的数据呈现解释，要求用户将显示的信息与模型决策所涉及的物理对象和现实世界位置相关联。用户被迫将数据探索和采集与AI模型解释分离开来。对于在物理环境中工作的AI系统，这种分离会使解释难以在上下文中理解。我们提出使用增强现实（AR）来增强对AI模型的理解，通过在用户的工作空间中直接、实时地提供空间可解释性信息，让用户在探索世界的同时获得解释。我们展示了如何将已知的可解释性方法应用于AR，并提供了用户对此类应用体验的见解。
英文摘要
Explainable Artificial Intelligence (AI) enables humans to understand and interpret decisions of AI models. Instead of having a black box, explainability supports humans in understanding AI models' behavior. Existing explainable AI approaches often present explanations on 2D displays using pre-recorded data, requiring users to relate the displayed information back to the physical objects and real world locations involved in a model's decision. Users are forced to decouple data exploration and capture from AI model interpretation. For AI systems that work within physical environments, this separation can make explanations difficult to interpret in context. We propose using Augmented Reality (AR) to enhance the understanding of AI models by enabling spatial explainability information directly in a user's workspace, in real time, as they explore the world. We show how known explainability methods can be applied in AR and provide insights into user experiences with such an application.
发表机构
- Adelaide University（阿德莱德大学）
- Graz University of Technology（格拉茨工业大学）
- Aarhus University（奥胡斯大学）
- University of Stuttgart（斯图加特大学）
机构由 AI 辅助整理，请以论文原文为准。
