  Surgical action triplet recognition provides a better understanding of the
surgical scene. This task is of high relevance as it provides the surgeon with
context-aware support and safety. The current go-to strategy for improving
performance is the development of new network mechanisms. However, the
performance of current state-of-the-art techniques is substantially lower than
other surgical tasks. Why is this happening? This is the question that we
address in this work. We present the first study to understand the failure of
existing deep learning models through the lens of robustness and
explainability. Firstly, we study current existing models under weak and strong
$\delta-$perturbations via an adversarial optimisation scheme. We then analyse
the failure modes via feature based explanations. Our study reveals that the
key to improving performance and increasing reliability is in the core and
spurious attributes. Our work opens the door to more trustworthy and reliable
deep learning models in surgical data science.
