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)技术可以降低数据工作的门槛,但仍需要人类干预才能发挥作用。然而,由人类和机器贸易工作而产生的复杂和协作过程使得难以追踪是谁(或什么)做了什么以及何时完成。在本研究中,我们构建了一个数据工作文物分类法,捕捉了AutoML和人类过程。我们提出了一种严格的方法论来进行创作,讨论了它在视觉设计过程中的可转移性。我们通过开发AutoMLTrace来实现分类法的操作,它是一种显示人类与机器在数据工作中协作的上下文和时间性的视觉交互式草图。最后,我们通过对一个企业软件开发团队的使用场景来展示我们的方法的效用。总体来看,我们的研究过程和发现探讨了开发数据可视化工具的挑战和有成效的途径,这些工具可以查询自动化数据工作中的社会技术关系。