Machine Teaching (MT) is an interactive process where a human and a machine interact with the goal of training a machine learning model (ML) for a specified task. The human teacher communicates their task expertise and the machine student gathers the required data and knowledge to produce an ML model. MT systems are developed to jointly minimize the time spent on teaching and the learner's error rate. The design of human-AI interaction in an MT system not only impacts the teaching efficiency, but also indirectly influences the ML performance by affecting the teaching quality. In this paper, we build upon our previous work where we proposed an MT framework with three components, viz., the teaching interface, the machine learner, and the knowledge base, and focus on the human-AI interaction design involved in realizing the teaching interface. We outline design decisions that need to be addressed in developing an MT system beginning from an ML task. The paper follows the Socratic method entailing a dialogue between a curious student and a wise teacher.
翻译:机器教学(MT)是一个互动过程,人类和机器在其中与培训机器学习模式(ML)的目标发生互动,目的是为特定任务培训机器学习模式(ML),人类教师交流他们的任务专长,机器学生收集所需的数据和知识以制作ML模型。开发MT系统是为了共同尽量减少教学时间和学习者误差率。在MT系统中设计人与AI互动不仅影响教学效率,而且通过影响教学质量间接影响ML的绩效。在本文中,我们以我们先前的工作为基础,提出了包含三个组成部分的MT框架,即教学界面、机器学习者和知识基础,并侧重于实现教学界面过程中涉及的人类与AI互动设计。我们概述了从ML任务开始的MT系统开发过程中需要解决的设计决定。本文遵循Scoric方法,要求一个好奇的学生和一个明智的教师进行对话。