弥合透明度鸿沟：可解释人工智能能从《人工智能法案》中学到什么？
Bridging the Transparency Gap: What Can Explainable AI Learn From the AI Act?
- University of Edinburgh（爱丁堡大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文揭示可解释AI与欧盟《人工智能法案》在透明度理解上的根本差异，提出“透明度鸿沟”概念，并从四个实践维度探讨弥合该鸿沟的路径。
AI中文摘要：
欧盟提出了《人工智能法案》，该法案为人工智能系统引入了详细的透明度要求。其中许多要求可以通过可解释人工智能（XAI）领域来解决，然而，XAI与法案在透明度的含义上存在根本性差异。法案将透明度视为支持更广泛价值观（如问责制、人权和可持续创新）的手段。相比之下，XAI将透明度狭义地视为目的本身，侧重于解释复杂的算法属性，而不考虑社会技术背景。我们将这种差异称为“透明度鸿沟”。如果不解决透明度鸿沟，XAI可能会留下一系列透明度问题得不到解决。为了开始弥合这一鸿沟，我们概述并澄清了XAI与欧洲法规——《人工智能法案》及相关的《通用数据保护条例》（GDPR）——如何看待透明度的基本定义所用的术语。通过比较XAI与法规的不同观点，我们得出了四个实际工作可以弥合透明度鸿沟的轴心：界定透明度的范围、澄清XAI的法律地位、解决合格评定中的问题，以及为数据集构建可解释性。
英文摘要：
The European Union has proposed the Artificial Intelligence Act which introduces detailed requirements of transparency for AI systems. Many of these requirements can be addressed by the field of explainable AI (XAI), however, there is a fundamental difference between XAI and the Act regarding what transparency is. The Act views transparency as a means that supports wider values, such as accountability, human rights, and sustainable innovation. In contrast, XAI views transparency narrowly as an end in itself, focusing on explaining complex algorithmic properties without considering the socio-technical context. We call this difference the ``transparency gap''. Failing to address the transparency gap, XAI risks leaving a range of transparency issues unaddressed. To begin to bridge this gap, we overview and clarify the terminology of how XAI and European regulation -- the Act and the related General Data Protection Regulation (GDPR) -- view basic definitions of transparency. By comparing the disparate views of XAI and regulation, we arrive at four axes where practical work could bridge the transparency gap: defining the scope of transparency, clarifying the legal status of XAI, addressing issues with conformity assessment, and building explainability for datasets.
