一句话：提出用因果模型将领域先验知识系统化地融入机器学习软件的需求与数据规格，并在工业故障检测中演示。
本文是一篇愿景论文，关注机器学习（ML）软件系统在需求工程（RE）中数据要求难以规格化的问题。作者提出把因果建模作为一种需求工程活动，使高层的领域先验知识能够被系统地整合到 ML 软件系统设计中。论文给出一个工作流，从高层先验知识中推导出低层模型要求与数据要求。该方法在一个工业故障检测系统上得到演示，说明其用于 ML 需求规格的可行性。最后，论文讨论了将因果建模确立为 RE 实践所需的未来研究。由于是愿景论文，摘要未报告量化评估或完整工具实现。
Specifying data requirements for machine learning (ML) software systems remains a challenge in requirements engineering (RE). This vision paper explores causal modelling as an RE activity that allows the systematic integration of prior domain knowledge into the design of ML software systems. We propose a workflow to elicit low-level model and data requirements from high-level prior knowledge using causal models. The approach is demonstrated on an industrial fault detection system. This paper outlines future research needed to establish causal modelling as an RE practice.