AF'fective Design: Supporting Atrial Fibrillation Post-treatment with Explainable AI

Abstract

This paper reports the preliminary design results of AF'fective, an AI driven patient monitoring system designed to facilitate the post-treatment of patients with Atrial Fibrillation (AF). In-depth interviews, co-design and prototype testing sessions were carried out with 16 cardiologists to investigate the context surrounding AF treatment, evaluate different explainable AI strategies and better understand how explainable AI could be designed and used to support AF post-treatment. Through the design process, we learnt key lessons such as the pitfalls of over-justification and how augmenting machine explanations with data sources that allow for self-interpretation could enhance perceived control over the decision making process and increase user acceptance in the system.