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.

翻译：我们主张，对机器学习（ML）模型或其构建过程的解释，可视为敏感性分析（SA）的一种形式——这是一种在环境建模、工程或经济学等诸多领域中用于解释复杂系统的通用方法论。我们面向研究人员与实践者，呼吁关注基于统一SA框架的ML解释方法的优势，以及充分认可相关研究的必要性。通过形式化描述以下三点，我们弥合了两个领域间的鸿沟：（a）ML过程本身就是一个适合SA的系统，（b）现有ML解释方法如何与该视角相关联，以及（c）其他SA技术如何应用于ML。