Morphology in evolutionary biology is used to quantify visible characteristics of specimens, a crucial aspect in addressing the biodiversity crisis. To investigate the impact of anthropogenic impacts, researchers have constructed extensive image databases. Obviously, these databases make the field optimal for the integration of machine learning. However, traditional methods used in morphometrics are grounded in diagnostic structures proposed by biologists. In contrast to that, machine learning approaches autonomously extract features without explicit biological motivation.

This talk focuses on the potential misunderstandings that can arise when applying machine learning in morphometrics. Specifically, the focus is on the biological interpretation of machine learning models, exploring instances where models demonstrate high accuracy yet struggle with coherent biological interpretation. The presentation showcases experiments that highlight the tension between excellent quantitative results but often lacks biological interpretation.