构建AI可信性功能与规范间的语义桥梁，助力系统评估与落地。
Bridging the AI Trustworthiness Gap between Functions and Norms
- 提出语义语言框架，连接AI功能实现与法规要求
- 帮助开发者将抽象规范转化为可执行的可信性措施
- 适合政策制定者、开发者及合规团队参考
可信人工智能（TAI）因法规要求和功能优势日益受到关注。功能型可信AI（FTAI）关注如何实现可信系统，规范型可信AI（NTAI）关注需遵守的法规。然而，二者之间仍存在鸿沟，难以评估AI系统的可信性。本文主张应建立一座桥梁，通过引入一种概念性语义语言，使FTAI与NTAI得以对接。该语言可作为开发者的评估框架，帮助其从可信性角度审视系统；也可协助利益相关方将法规条文转化为具体实施步骤。本文梳理了当前研究现状，指出了二者之间的差距，探讨了构建语义语言的切入点及其预期影响，并提出了关键考量与未来行动方向。
原文摘要 · Abstract (English)
Trustworthy Artificial Intelligence (TAI) is gaining traction due to regulations and functional benefits. While Functional TAI (FTAI) focuses on how to implement trustworthy systems, Normative TAI (NTAI) focuses on regulations that need to be enforced. However, gaps between FTAI and NTAI remain, making it difficult to assess trustworthiness of AI systems. We argue that a bridge is needed, specifically by introducing a conceptual language which can match FTAI and NTAI. Such a semantic language can assist developers as a framework to assess AI systems in terms of trustworthiness. It can also help stakeholders translate norms and regulations into concrete implementation steps for their systems. In this position paper, we describe the current state-of-the-art and identify the gap between FTAI and NTAI. We will discuss starting points for developing a semantic language and the envisioned effects of it. Finally, we provide key considerations and discuss future actions towards assessment of TAI.
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