Challenges in interpretability of additive models

Xinyu Zhang; Julien Martinelli; ST John

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arXiv · 2504.10169

Challenges in interpretability of additive models

Xinyu Zhang·Julien Martinelli·ST John

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Abstract

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.

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Xinyu Zhang, Julien Martinelli, ST John. 2025-04-14. Challenges in interpretability of additive models. https://arxiv.org/abs/2504.10169

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