Automated Machine Learning (AutoML) technology can lower barriers in data work yet still requires human intervention to be functional. However, the complex and collaborative process resulting from humans and machines trading off work makes it difficult to trace what was done, by whom (or what), and when. In this research, we construct a taxonomy of data work artifacts that captures AutoML and human processes. We present a rigorous methodology for its creation and discuss its transferability to the visual design process. We operationalize the taxonomy through the development of AutoMLTrace, a visual interactive sketch showing both the context and temporality of human-ML/AI collaboration in data work. Finally, we demonstrate the utility of our approach via a usage scenario with an enterprise software development team. Collectively, our research process and findings explore challenges and fruitful avenues for developing data visualization tools that interrogate the sociotechnical relationships in automated data work.

翻译：自动化机器学习（AutoML）技术可降低数据工作的准入门槛，但实际运行仍需人工干预。然而，由于人机交替协作的复杂过程，导致追踪任务由谁（或何种机制）、在何时完成变得困难。本研究构建了一套涵盖AutoML与人工流程的数据工作制品分类体系，提出严谨的分类创建方法论，并探讨其向可视化设计流程的可迁移性。通过开发AutoMLTrace这一视觉交互式图谱，我们将该分类体系付诸实践，展示数据工作中人机协作的上下文关系与时间维度。最后，通过企业软件开发团队的使用场景验证了本方法的实用性。本研究的流程与成果揭示了在自动化数据工作中开发探究社会技术关系的数据可视化工具所面临的挑战与潜在路径。