Linguistically Communicating Uncertainty in Patient-Facing Risk
  Prediction Models
Adarsa Sivaprasad Ehud Reiter
This paper addresses the unique challenges associated with uncertainty
quantification in AI models when applied to patient-facing contexts within
healthcare. Unlike traditional eXplainable Artificial Intelligence (XAI)
methods tailored for model developers or domain experts, additional
considerations of communicating in natural language, its presentation and
evaluating understandability are necessary. We identify the challenges in
communication model performance, confidence, reasoning and unknown knowns using
natural language in the context of risk prediction. We propose a design aimed
at addressing these challenges, focusing on the specific application of
in-vitro fertilisation outcome prediction.