为需求分析定义可解释AI的三大标准，助力信任构建。
Defining Explainable AI for Requirements Analysis
- 提出源、深度、范围三维度界定可解释性要求
- 聚焦机器学习技术与应用需求的匹配问题
- 适合关注AI可信决策的研究者与工程实践者
可解释人工智能（XAI）近年来备受关注。人工智能（AI）领域，尤其是机器学习（ML）领域，逐渐认识到，在许多应用场景中，AI不仅需具备良好的决策性能，还需能解释其决策过程，并让我们相信其决策是基于正确原因。然而，不同应用对解释信息的要求各不相同。本文提出三个维度——源、深度、范围，用于分类不同应用的解释需求。重点探讨如何将这些需求与底层机器学习技术的能力相匹配。本文刻意避开已有文献充分讨论的内容，专注于机器学习范畴，但其原则适用于更广泛的AI领域。
原文摘要 · Abstract (English)
Explainable Artificial Intelligence (XAI) has become popular in the last few years. The Artificial Intelligence (AI) community in general, and the Machine Learning (ML) community in particular, is coming to the realisation that in many applications, for AI to be trusted, it must not only demonstrate good performance in its decisionmaking, but it also must explain these decisions and convince us that it is making the decisions for the right reasons. However, different applications have different requirements on the information required of the underlying AI system in order to convince us that it is worthy of our trust. How do we define these requirements? In this paper, we present three dimensions for categorising the explanatory requirements of different applications. These are Source, Depth and Scope. We focus on the problem of matching up the explanatory requirements of different applications with the capabilities of underlying ML techniques to provide them. We deliberately avoid including aspects of explanation that are already well-covered by the existing literature and we focus our discussion on ML although the principles apply to AI more broadly.
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