Fig. 1. Mitigating bias of multiple surgical AI systems across multiple hospitals.

a Multiple AI systems assess the skill-level of multiple surgical activities (e.g., needle handling and needle driving) from videos of intraoperative surgical activity. These AI systems are often deployed across multiple hospitals. b To examine bias, we stratify these systems' performance (e.g., AUC) across different sub-cohorts of surgeons (e.g., novices vs. experts). The bias of one of many AI systems is akin to a light bulb in an electric circuit connected in series: similar to how one defective light bulb leads to a defective circuit, one biased AI system is sufficient to disadvantage a surgeon sub-cohort. c To mitigate bias, we teach an AI system, through a strategy referred to as TWIX, to complement its skill assessments with predictions of the importance of video frames based on ground-truth annotations provided by human experts.