We argue that interpretations of machine learning (ML) models or the
model-building process can bee seen as a form of sensitivity analysis (SA), a
general methodology used to explain complex systems in many fields such as
environmental modeling, engineering, or economics. We address both researchers
and practitioners, calling attention to the benefits of a unified SA-based view
of explanations in ML and the necessity to fully credit related work. We bridge
the gap between both fields by formally describing how (a) the ML process is a
system suitable for SA, (b) how existing ML interpretation methods relate to
this perspective, and (c) how other SA techniques could be applied to ML.

我们认为机器学习（ML）模型的解释或模型构建过程可以被视为敏感性分析（SA）的一种形式，SA 是一种用于解释复杂系统的通用方法，可用于环境模拟、工程或经济等多个领域。我们旨在通过正式描述（a）ML 过程适合进行 SA 的系统性，（b）现有的 ML 解释方法与这一视角的相关性，以及（c）如何将其他 SA 技术应用于 ML 来弥合这两个领域之间的差距。