We propose a new, complementary approach to interpretability, in which machines are not considered as experts whose role it is to suggest what should be done and why, but rather as advisers. The objective of these models is to communicate to a human decision-maker not what to decide but how to decide. In this way, we propose that machine learning pipelines will be more readily adopted, since they allow a decision-maker to retain agency. Specifically, we develop a framework for learning representations by humans, for humans, in which we learn representations of inputs ( " advice") that are effective for human decision-making. 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 .
The task of optimizing machines to support human decision-making is often conflated with that of optimizing machines for accuracy even though they are materially different. Whereas it is typical for learning systems to prescribe actions through prediction, here we propose an approach in which the role of machines is to reframe problems in order to directly support human decisions. Inspired by the success of representation learning in promoting machine performance, we frame the problem as one of learning representations that are conducive to good human performance. This " Man Composed with Machine'' framework incorporates a 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 processes and can lead to good decisions .
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We propose a new, complementary approach to interpretability, in which machines
The task of optimizing machines to support human decision-making is often conflated with that of optimizing machines for accuracy even though they are materially different. Whereas it is typical for learning systems to prescribe actions through prediction, here we propose an approach in which the role of machines is to reframe problems in order to directly support human decisions. Inspired by the success of representation learning in promoting machine performance, we frame the problem as one of learning representations that
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not considered as experts whose role it is to suggest what should be done and why, but rather as advisers. The objective of these models is to communicate to a human decision-maker not what to decide but how to decide. In this way, we propose that machine learning pipelines will be more readily adopted, since they allow a decision-maker to retain agency. Specifically, we develop a framework for learning representations by humans, for humans, in which we learn representations of inputs (
conducive to good human performance. This
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advice") that are effective for human decision-making. Representation-generating models are trained with humans-in-the-loop, implicitly incorporating the
Man Composed with Machine'' framework incorporates a
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. We show that optimizing for
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
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rather than accuracy is effective in promoting good decisionsin various classification tasks while inherently maintaining a sense of interpretability
processes and can lead to good decisions