Human-AI Interaction Design in Machine Teaching
This work addresses the problem of optimizing human-AI collaboration for training machine learning models, but it appears incremental as it extends prior research without introducing new methods or results.
The paper tackles the design of human-AI interaction in Machine Teaching systems to improve teaching efficiency and indirectly enhance machine learning performance, building on a previous framework with a focus on the teaching interface using a Socratic dialogue approach.
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.