Electronic monitoring (EM) is a technology with the potential to increase fisheries monitoring coverage, improving data quality and timeliness of data. This is particularly the case in the monitoring of bycatch. In many fisheries, bycatch events are rare and require high-levels of monitoring coverage (observer and/or video review), which can be time and cost-prohibitive. EM programs designed for bycatch monitoring should integrate artificial intelligence and machine learning (AI/ML) tool to efficiently detect rare events. There are a number of initiatives worldwide promoting aggregation of imagery into shared libraries to improve AI/ML development. To further our understanding of protected, endangered and threatened (PET) species bycatch, it is imperative to identify ways to aggregate and share AI/ML training datasets.