Before revision:  Representation-generating models are trained with humans-in-the-loop, implicitly incorporating the human decision-making model . We show that optimizing for human decision-making rather than accuracy is effective in promoting good decisionsin various classification tasks while inherently maintaining a sense of interpretability .
After revision:  Representation-generating models are trained with humans-in-the-loop, implicitly incorporating the human decision-making model directly into the representation learning paradigm with optimization achieved through a novel human-in-the-loop training procedure. We empirically demonstrate on various tasks and representational forms that the framework is capable of learning representations that better coincide with human decision-making rather than accuracy is effective in promoting good decisionsin various classification tasks while inherently maintaining a sense of interpretability .