Open AccessBayesian modeling of human–AI complementarity
Author(s) -
Mark Steyvers,
Heliodoro Tejeda,
Gavin Kerrigan,
Padhraic Smyth
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2111547119
Subject(s) - complementarity (molecular biology) , computer science , machine learning , artificial intelligence , bayesian probability , biology , genetics
Significance With 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.