  When machine predictors can achieve higher performance than the human
decision-makers they support, improving the performance of human
decision-makers is often conflated with improving machine accuracy. Here we
propose a framework to directly support human decision-making, in which the
role of machines is to reframe problems rather than to prescribe actions
through prediction. Inspired by the success of representation learning in
improving performance of machine predictors, our framework learns human-facing
representations optimized for human performance. This "Mind Composed with
Machine" framework incorporates a human decision-making model directly into the
representation learning paradigm and is trained with a novel human-in-the-loop
training procedure. We empirically demonstrate the successful application of
the framework to various tasks and representational forms.
