Understanding and supporting knowledge decomposition for machine teaching

Abstract

Machine teaching (MT) is an emerging field that studies non-machine learning (ML) experts incrementally building semantic ML models in efficient ways. While MT focuses on the types of knowledge a human teacher provides a machine learner, not much is known about how people perform or can be supported in this essential task of identifying and expressing useful knowledge. We refer to this process as knowledge decomposition. To address the challenges of this type of Human-AI collaboration, we seek to build foundational frameworks for understanding and supporting knowledge decomposition. We present results of a study investigating what types of knowledge people teach, what cognitive processes they use, and what challenges they encounter when teaching a learner to classify text documents. From our observations, we introduce design opportunities for new tools to support knowledge decomposition. Our findings carry implications for applying the benefits of knowledge decomposition to MT and ML.