  Machine learning models that first learn a representation of a domain in
terms of human-understandable concepts, then use it to make predictions, have
been proposed to facilitate interpretation and interaction with models trained
on high-dimensional data. However these methods have important limitations: the
way they define concepts are not inherently interpretable, and they assume that
concept labels either exist for individual instances or can easily be acquired
from users. These limitations are particularly acute for high-dimensional
tabular features. We propose an approach for learning a set of transparent
concept definitions in high-dimensional tabular data that relies on users
labeling concept features instead of individual instances. Our method produces
concepts that both align with users' intuitive sense of what a concept means,
and facilitate prediction of the downstream label by a transparent machine
learning model. This ensures that the full model is transparent and intuitive,
and as predictive as possible given this constraint. We demonstrate with
simulated user feedback on real prediction problems, including one in a
clinical domain, that this kind of direct feedback is much more efficient at
learning solutions that align with ground truth concept definitions than
alternative transparent approaches that rely on labeling instances or other
existing interaction mechanisms, while maintaining similar predictive
performance.
