Statistical Tests for Replacing Human Decision Makers with Algorithms

Kai Feng, Han Hong, Ke Tang and Jingyuan Wang

Papers from arXiv.org

Abstract: This paper proposes a statistical framework of using artificial intelligence to improve human decision making. The performance of each human decision maker is benchmarked against that of machine predictions. We replace the diagnoses made by a subset of the decision makers with the recommendation from the machine learning algorithm. We apply both a heuristic frequentist approach and a Bayesian posterior loss function approach to abnormal birth detection using a nationwide dataset of doctor diagnoses from prepregnancy checkups of reproductive age couples and pregnancy outcomes. We find that our algorithm on a test dataset results in a higher overall true positive rate and a lower false positive rate than the diagnoses made by doctors only.

Date: 2023-06, Revised 2024-12
New Economics Papers: this item is included in nep-ain, nep-big and nep-cmp
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