Model interpretability refers to the degree to which a human can understand the cause of a decision made by a machine learning model. In toxicology, it is crucial to understand why a model predicts a particular substance as toxic or non-toxic to ensure reliable and safe applications. This is particularly important in regulatory contexts where decisions can have significant health and environmental impacts.
