一句话：通过海事图像识别案例，揭示 ML 需求工程中数据科学家“模型优先”与 UX 设计师“用户优先”的理念冲突及其落地影响。
本文考察机器学习系统需求工程中 UX 设计师与数据科学家之间的张力。作者以海事服务提供商 Marcomp 的图像识别 ML 项目为案例，研究两类角色在需求获取上的根本差异。数据科学家采取“模型优先”路径，关注模型准确率，并期望用户适应模型约束；UX 设计师则优先考虑用户工作流，质疑方案是否真正满足用户需求。研究发现，尽管双方都采用迭代方式，但其对需求的理解不同：数据科学家为提升模型性能而迭代，UX 设计师为提升用户价值而迭代。在管理层推动 AI 落地的背景下，这种分歧被放大，最终系统提升了行政效率，却干扰了用户工作流。本文以描述性案例故事，深化了对 AI 系统需求工程中软件开发实践与数据科学方法差异的理解。
This short-paper examines the tensions between user experience (UX) designers and data scientists in requirements engineering for machine learning (ML) systems. Through a case study at Marcomp, a maritime service provider, we investigated how an image recognition ML project revealed fundamental differences in approaches to requirements elicitation. Data scientists adopted a model-first trajectory, focusing on the model's accuracy and expecting users to adapt to its constraints. In contrast, UX designers prioritized user workflows, questioning whether the solution satisfied user needs. Our findings demonstrate that while both groups work iteratively, they conceptualize requirements differently: data scientists iterate to improve model performance, whereas UX designers iterate to enhance user value. This disparity, amplified by management's push to implement AI technologies, resulted in a system that improved administrative efficiency but disrupted users' workflows. In this short-paper, we contribute a descriptive story to deepen our understanding of how the perspectives of software development practices and data science methods differ in requirements engineering processes for AI systems.