Bayesian modeling of human-AI complementarity.
Journal:
Proceedings of the National Academy of Sciences of the United States of America
Published Date:
Mar 11, 2022
Abstract
SignificanceWith the increase in artificial intelligence in real-world applications, there is interest in building hybrid systems that take both human and machine predictions into account. Previous work has shown the benefits of separately combining the predictions of diverse machine classifiers or groups of people. Using a Bayesian modeling framework, we extend these results by systematically investigating the factors that influence the performance of hybrid combinations of human and machine classifiers while taking into account the unique ways human and algorithmic confidence is expressed.