标题： 面向可信人工智能需求规范化的框架 显示英文标题

标题： Towards a Framework for Operationalizing the Specification of Trustworthy AI Requirements

摘要： 对AI增强系统可信度日益增长的担忧突显了需求工程（RE）在解决难以通过结构化方法指定的新兴、上下文相关属性中的作用。 在本文的简短愿景论文中，我们提出了两种互补方法的整合：AMDiRE，一种基于工件的需求工程（RE）方法，以及 PerSpecML，一种基于视角的方法，旨在支持机器学习（ML）增强系统的获取、分析和规范。 AMDiRE 提供了一种结构化、以工件为中心、与过程无关的方法和模板，促进结果的一致性和可追溯性；然而，它主要面向确定性系统。 相反，PerSpecML 引入了多视角指导，以揭示由ML增强系统数据驱动和非确定性行为引发的问题。 我们设想了一条将与可信度相关的需求数字化的路径，弥合利益相关者驱动的关注与结构化工件模型之间的差距。 最后，我们概述了与RE社区讨论的关键研究方向和开放挑战。 显示英文摘要

摘要： Growing concerns around the trustworthiness of AI-enabled systems highlight the role of requirements engineering (RE) in addressing emergent, context-dependent properties that are difficult to specify without structured approaches. In this short vision paper, we propose the integration of two complementary approaches: AMDiRE, an artefact-based approach for RE, and PerSpecML, a perspective-based method designed to support the elicitation, analysis, and specification of machine learning (ML)-enabled systems. AMDiRE provides a structured, artefact-centric, process-agnostic methodology and templates that promote consistency and traceability in the results; however, it is primarily oriented toward deterministic systems. PerSpecML, in turn, introduces multi-perspective guidance to uncover concerns arising from the data-driven and non-deterministic behavior of ML-enabled systems. We envision a pathway to operationalize trustworthiness-related requirements, bridging stakeholder-driven concerns and structured artefact models. We conclude by outlining key research directions and open challenges to be discussed with the RE community.