Considering Interpretability

Good look at what interpretability is.  My view is that to interpret means to understand what it means in developed context, and in future.    The context I usually have to most closely deal with is how is it interpreted by decision makers.  Thoughtful and considerable article here,  I am reviewing,

In Machine Learning, the concept of Interpretability is both important and slippery
Via ACMQueue
https://arxiv.org/abs/1606.03490

Computer Science > Machine Learning
The Mythos of Model Interpretability
By Zachary C. Lipton

(Submitted on 10 Jun 2016 (v1), last revised 6 Mar 2017 (this version, v3))
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.  ... "