Related papers: Improving Expert Predictions with Conformal Prediction

Improving Expert Predictions with Conformal Prediction

URL: http://arxiv.org/abs/2201.12006v5
Date: Fri, 30 Jun 2023 13:33:32 GMT
Title: Improving Expert Predictions with Conformal Prediction
Authors: Eleni Straitouri and Lequn Wang and Nastaran Okati and Manuel Gomez Rodriguez
Abstract summary: existing systems typically require experts to understand when to cede agency to the system or when to exercise their own agency. We develop an automated decision support system that allows experts to make more accurate predictions and is robust to the accuracy of the predictor relies on.
Score: 14.850555720410677
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Automated decision support systems promise to help human experts solve multiclass classification tasks more efficiently and accurately. However, existing systems typically require experts to understand when to cede agency to the system or when to exercise their own agency. Otherwise, the experts may be better off solving the classification tasks on their own. In this work, we develop an automated decision support system that, by design, does not require experts to understand when to trust the system to improve performance. Rather than providing (single) label predictions and letting experts decide when to trust these predictions, our system provides sets of label predictions constructed using conformal prediction$\unicode{x2014}$prediction sets$\unicode{x2014}$and forcefully asks experts to predict labels from these sets. By using conformal prediction, our system can precisely trade-off the probability that the true label is not in the prediction set, which determines how frequently our system will mislead the experts, and the size of the prediction set, which determines the difficulty of the classification task the experts need to solve using our system. In addition, we develop an efficient and near-optimal search method to find the conformal predictor under which the experts benefit the most from using our system. Simulation experiments using synthetic and real expert predictions demonstrate that our system may help experts make more accurate predictions and is robust to the accuracy of the classifier the conformal predictor relies on.

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