The Mythos of Model Interpretability

Abstract

Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? We want models to be not only good, but interpretable. And yet the task of interpretation appears underspecified. Papers provide diverse and sometimes non-overlapping motivations for interpretability, and offer myriad notions of what attributes render models interpretable. Despite this ambiguity, many papers proclaim interpretability axiomatically, absent further explanation. In this paper, we seek to refine the discourse on interpretability. First, we examine the motivations underlying interest in interpretability, finding them to be diverse and occasionally discordant. Then, we address model properties and techniques thought to confer interpretability, identifying transparency to humans and post-hoc explanations as competing notions. Throughout, we discuss the feasibility and desirability of different notions, and question the oft-made assertions that linear models are interpretable and that deep neural networks are not.

The authors' own abstract, copied from arxiv.org.

How to cite it

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Lipton, Z. C. (2016). The Mythos of Model Interpretability. Preprint, not yet peer-reviewed. https://arxiv.org/abs/1606.03490

Lipton, Zachary C.. "The Mythos of Model Interpretability." Preprint, not yet peer-reviewed, 2016, https://arxiv.org/abs/1606.03490.

@article{lipton2016the,
  title = {The Mythos of Model Interpretability},
  author = {Zachary C. Lipton},
  journal = {Preprint, not yet peer-reviewed},
  year = {2016},
  url = {https://arxiv.org/abs/1606.03490},
}

What it is

Shows that “interpretable” is used to mean several incompatible things, and that simpler models are not automatically more honest about what they do. Free on arXiv.

Topics