Challenges in interpretability of additive models

We review generalized additive models as a type of transparent'' model that has recently seen renewed interest in the deep learning community as neural additive models. We highlight multiple types of nonidentifiability in this model class and discuss challenges in interpretability, arguing for restraint when claiming interpretability’’ or ``suitability for safety-critical applications’’ of such models.