想知道工业界怎么搞AI可解释性需求？Daimler Truck的8位工程师实战经验，告诉你现有方法哪里不好使。
这篇论文报告了在Daimler Truck公司进行的定性研究，涉及8位从业者。通过think-aloud协议和小组讨论，研究者分析了需求获取、规格和验证阶段。初步发现包括概念模糊、可测试性不足和验证碎片化等挑战。研究指出当前RE实践难以系统化处理可解释性需求，并提出了一个基于实证的可解释RE框架愿景。
Evaluating RE Practices for Explainability: Synthesizing Insights from Daimler Truck into an Explainable RE Framework Proposal
Explainability has emerged as a critical requirement for AI-based systems, particularly in safety-critical and regulated domains. Although prior research has proposed frameworks, patterns, and user-centered approaches to support explainability, there is limited empirical understanding of how existing Requirements Engineering (RE) practices support explainability requirements across the RE lifecycle, especially in an industrial context. This paper reports early findings from an ongoing industry-based study investigating how explainability requirements are elicited, specified, and validated using established RE techniques. We conducted a multi-phase qualitative study with eight practitioners at Daimler Truck, employing think-aloud protocols and moderated group discussions across requirements elicitation, specification, and validation steps. Our preliminary analysis reveals recurring challenges across all steps, including conceptual ambiguity during elicitation, limited testability and expressiveness during specification, and fragmented validation due to vague criteria and regulatory uncertainty. These findings indicate that current RE practices provide limited support to systematically address explainability requirements. The paper contributes empirical insights into step-specific and cross-cutting challenges and outlines a research vision toward developing an empirically grounded RE framework for explainable AI-based systems.