Language:

Bayesian modeling of human-AI complementarity

Proceedings of the National Academy of Sciences - PNAS, 2022-03, Vol.119 (11), p.e2111547119-e2111547119 [Peer Reviewed Journal]

Full text available

Citations Cited by

Title:
Bayesian modeling of human-AI complementarity
Author: Steyvers, Mark ; Tejeda, Heliodoro ; Kerrigan, Gavin ; Smyth, Padhraic
Subjects: Algorithms ; Artificial Intelligence ; Artificial neural networks ; Bayes Theorem ; Bayesian analysis ; Classification ; Classifiers ; Complementarity ; Human performance ; Humans ; Hybrid systems ; Image classification ; Machine learning ; Mathematical models ; Motivation ; Neural networks ; Physical Sciences ; Predictions ; Social Sciences
Is Part Of: Proceedings of the National Academy of Sciences - PNAS, 2022-03, Vol.119 (11), p.e2111547119-e2111547119
Description: 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.
Publisher: United States: National Academy of Sciences
Language: English
Identifier: ISSN: 0027-8424
EISSN: 1091-6490
DOI: 10.1073/pnas.2111547119
PMID: 35275788
Source: Geneva Foundation Free Medical Journals at publisher websites
MEDLINE
PubMed Central

Back to results list


INSPIRE LIBRARY - TON DUC THANG UNIVERSITY	(84-028) 37 755 057	Feedback
19 Nguyen Huu Tho St. Dist.7, HCM	thuvien@tdtu.edu.vn	Feedback

Searching Remote Databases, Please Wait