Jen Rogers
Automated Machine Learning (AutoML) technology can lower bar- riers in data work yet still requires human intervention to be func- tional. 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 Au- toML 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 AutoML Trace a visual interactive sketch showing both the con- text and temporality of human-ML/AI collaboration in data work. Finally, we demonstrate the utility of our approach via a usage sce- nario 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. Availability of Supplemental Materials: https://osf.io/3nmyj/ ?view_only=19962103d58b45d289b5c83421f48b36 This work is licensed under a Creative Commons Attribution International 4.0 License. CHI ’23, April 23–28, 2023, Hamburg, Germany © 2023 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9421-5/23/04. https://doi.org/10.1145/3544548.3580819 CCS CONCEPTS • Human-centered computing →Visualization theory, concepts and paradigms; • Computing methodologies →Artificial intelli- gence.