  Human-in-the-loop machine learning is widely used in artificial intelligence
(AI) to elicit labels for data points from experts or to provide feedback on
how close the predicted results are to the target. This simplifies away all the
details of the decision-making process of the expert. In this work, we allow
the experts to additionally produce decision rules describing their
decision-making; the rules are expected to be imperfect but to give additional
information. In particular, the rules can extend to new distributions, and
hence enable significantly improving performance for cases where the training
and testing distributions differ, such as in domain adaptation. We apply the
proposed method to lifelong learning and domain adaptation problems and discuss
applications in other branches of AI, such as knowledge acquisition problems in
expert systems. In simulated and real-user studies, we show that decision rule
elicitation improves domain adaptation of the algorithm and helps to propagate
expert's knowledge to the AI model.
