  The wide adoption of Machine Learning technologies has created a rapidly
growing demand for people who can train ML models. Some advocated the term
"machine teacher" to refer to the role of people who inject domain knowledge
into ML models. One promising learning paradigm is Active Learning (AL), by
which the model intelligently selects instances to query the machine teacher
for labels. However, in current AL settings, the human-AI interface remains
minimal and opaque. We begin considering AI explanations as a core element of
the human-AI interface for teaching machines. When a human student learns, it
is a common pattern to present one's own reasoning and solicit feedback from
the teacher. When a ML model learns and still makes mistakes, the human teacher
should be able to understand the reasoning underlying the mistakes. When the
model matures, the machine teacher should be able to recognize its progress in
order to trust and feel confident about their teaching outcome. Toward this
vision, we propose a novel paradigm of explainable active learning (XAL), by
introducing techniques from the recently surging field of explainable AI (XAI)
into an AL setting. We conducted an empirical study comparing the model
learning outcomes, feedback content and experience with XAL, to that of
traditional AL and coactive learning (providing the model's prediction without
the explanation). Our study shows benefits of AI explanation as interfaces for
machine teaching--supporting trust calibration and enabling rich forms of
teaching feedback, and potential drawbacks--anchoring effect with the model
judgment and cognitive workload. Our study also reveals important individual
factors that mediate a machine teacher's reception to AI explanations,
including task knowledge, AI experience and need for cognition. By reflecting
on the results, we suggest future directions and design implications for XAL.
