"Hello AI" Uncovering the Onboarding Needs of Medical Practitioners for Human AI Collaborative Decision Making
Although rapid advances in machine learning have made it increasingly applicable to expert decision making, the delivery of accurate algorithmic predictions alone is insufficient for effective human AI collaboration. In this work, we investigate the key types of information medical experts desire when they are first introduced to a diagnostic AI assistant. In a qualitative lab study, we interviewed 21 pathologists before, during, and after being presented deep neural network (DNN) predictions for prostate cancer diagnosis, to learn the types of information that they desired about the AI assistant. Our findings reveal that, far beyond understanding the local, case specific reasoning behind any model decision, clinicians desired upfront information about basic, global properties of the model, such as its known strengths and limitations, its subjective point of view, and its overall design objective what it's designed to be optimized for. Participants compared these information needs to the collaborative mental models they develop of their medical colleagues when seeking a second opinion: the medical perspectives and standards that those colleagues embody, and the compatibility of those perspectives with their own diagnostic patterns. These findings broaden and enrich discussions surrounding AI transparency for collaborative decision making, providing a richer understanding of what experts find important in their introduction to AI assistants before integrating them into routine practice.
Proc. ACM Hum. Comput. Interact.