Bayesian modeling of human-AI complementarity.
Proc Natl Acad Sci U S A
; 119(11): e2111547119, 2022 03 15.
Article
em En
| MEDLINE
| ID: mdl-35275788
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.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article