Given the importance of integrating of explainability into machine learning, at present, there are a lack of pedagogical resources exploring this. Specifically, we have found a need for resources in explaining how one can teach the advantages of explainability in machine learning. Often pedagogical approaches in the field of machine learning focus on getting students prepared to apply various models in the real world setting, but much less attention is given to teaching students the various techniques one could employ to explain a model's decision-making process. Furthermore, explainability can benefit from a narrative structure that aids one in understanding which techniques are governed by which questions about the data. We provide a pedagogical perspective on how to structure the learning process to better impart knowledge to students and researchers in machine learning, when and how to implement various explainability techniques as well as how to interpret the results. We discuss a system of teaching explainability in machine learning, by exploring the advantages and disadvantages of various opaque and transparent machine learning models, as well as when to utilize specific explainability techniques and the various frameworks used to structure the tools for explainability. Among discussing concrete assignments, we will also discuss ways to structure potential assignments to best help students learn to use explainability as a tool alongside any given machine learning application. Data science professionals completing the course will have a birds-eye view of a rapidly developing area and will be confident to deploy machine learning more widely. A preliminary analysis on the effectiveness of a recently delivered course following the structure presented here is included as evidence supporting our pedagogical approach.

本研究提供了一个关于如何结构化学习过程以更好地向机器学习领域的学生和研究人员传授知识的教学观点，包括何时以及如何实现各种可解释性技术以及如何解释其结果。同时，讨论了利用各种不透明和透明的机器学习模型，以及何时利用特定的可解释性技术和用于可解释性工具的各种框架的优缺点。最终，作者还提出了结构潜在任务以最好地帮助学生学习如何将解释性作为给定机器学习应用的工具的方式。

机器学习中的可解释性：教育视角