Eliciting good teaching from humans for machine learners

We propose using computational teaching algorithms to improve human teaching for machine learners. We investigate example sequences produced naturally by human teachers and find that humans often do not spontaneously generate optimal teaching sequences for arbitrary machine learners. To elicit better teaching, we propose giving humans teaching guidance, which are instructions on how to teach, derived from computational teaching algorithms or heuristics. We present experimental results demonstrating that teaching guidance substantially improves human teaching in three different problem domains. This provides promising evidence that human intelligence and flexibility can be leveraged to achieve better sample efficiency when input data to a learning system comes from a human teacher. We aim to improve Interactive Machine Learning by influencing the human teacher.We propose Teaching Guidance: instructions for teachers, to improve their input.Teaching Guidance is derived from optimal or heuristic teaching algorithms.We performed experiments to compare human teaching with and without teaching guidance.We found that Teaching Guidance substantially improves the data provided by teachers.

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