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An increasingly common use case for machine learning models is augmenting theabilities of human decision makers. For classification tasks where neither thehuman or model are perfectly accurate, a key step in obtaining high performanceis combining their individual predictions in a manner that leverages theirrelative strengths. In this work, we develop a set of algorithms that combinethe probabilistic output of a model with the class-level output of a human. Weshow theoretically that the accuracy of our combination model is driven notonly by the individual human and model accuracies, but also by the model'sconfidence. Empirical results on image classification with CIFAR-10 and asubset of ImageNet demonstrate that such human-model combinations consistentlyhave higher accuracies than the model or human alone, and that the parametersof the combination method can be estimated effectively with as few as tenlabeled datapoints.

Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration