解析欧盟人工智能法案中可解释性框架的落地路径
Unlocking the Black Box: Analysing the EU Artificial Intelligence Act's Framework for Explainability in AI
- 梳理可解释AI技术与治理实践的适配方案
- 指出法律执行中的标准缺失与监督难题
- 适合政策制定者与AI合规研究人员参考
人工智能缺乏可解释性是产业与监管共同面临的核心挑战，尤其在医疗、信贷评分、执法及刑事司法等关乎问责、伦理与公平的领域尤为突出。欧盟《人工智能法案》将可解释性列为基本原则之一，但具体XAI技术与要求仍待实践验证。本文探讨多种有望推进XAI的方法与技术，分析其在AI治理与政策实施中的障碍，并重点考察XAI如何融入欧盟法律体系，强调标准制定、监督机制与执法有效性等关键问题。
原文摘要 · Abstract (English)
The lack of explainability of Artificial Intelligence (AI) is one of the first obstacles that the industry and regulators must overcome to mitigate the risks associated with the technology. The need for eXplainable AI (XAI) is evident in fields where accountability, ethics and fairness are critical, such as healthcare, credit scoring, policing and the criminal justice system. At the EU level, the notion of explainability is one of the fundamental principles that underpin the AI Act, though the exact XAI techniques and requirements are still to be determined and tested in practice. This paper explores various approaches and techniques that promise to advance XAI, as well as the challenges of implementing the principle of explainability in AI governance and policies. Finally, the paper examines the integration of XAI into EU law, emphasising the issues of standard setting, oversight, and enforcement.
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