Google Research

Onboarding Materials as Boundary Objects for Developing AI Assistants

Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, ACM (2021) (to appear)

Abstract

Deep neural networks (DNNs) routinely achieve state-of-the-art performance in a wide range of tasks. This case study reports on the development of onboarding (i.e., training) materials for a DNN-based medical AI Assistant to aid in the grading of prostate cancer. Specifically, we describe how the process of developing these materials deepened the team's understanding of end-user requirements, leading to changes in the development and assessment of the underlying machine learning model. In this sense, the onboarding materials served as a useful boundary object for a cross-functional team. We also present evidence of the utility of the subsequent onboarding materials by describing which information was found useful by participants in an experimental study.

Learn more about how we do research

We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work