McMaster University
Quantifying Trust in Autonomous  Medical Advisory Systems — Artificial intelligence (AI) and machine learning (ML) are key to medical support and care in remote and austere environments. However, they introduce two primary challenges for trust and adoption, which are not adequately addressed in existing autonomous systems because they are not well understood; these include: 1) uncertainty in AI decision models, and 2) the impact of autonomous systems on human-machine interaction as a joint cognitive system (JCS). We propose to develop a framework to quantify trust in autonomous medical advisory systems (AMAS) decisions and optimize for integration into a joint cognitive system (JCS).