一篇新研究论文探讨了在AI领域将可解释性需求整合到现有需求工程（RE）实践中的挑战。该研究涉及戴姆勒卡车（Daimler Truck）的八名从业人员，识别出了在需求获取、规范和验证阶段反复出现的问题。这些挑战包括概念模糊性、有限的可测试性以及监管不确定性，表明当前的需求工程方法在系统性地解决AI系统的可解释性需求方面支持不足。 AI
影响 强调了改进方法论的必要性，以确保AI系统在关键应用中是可理解和值得信赖的。
排序理由 该集群包含一篇讨论研究发现并提出框架的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2607.11771v1 Announce Type: cross Abstract: 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 e…
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 understa…