A Synthetic Prediction Market for Estimating Confidence in Published Work

Sarah Rajtmajer, Christopher Griffin, Jian Wu, Robert Fraleigh, Laxmaan Balaji, Anna Squicciarini, Anthony Kwasnica, David Pennock, Michael McLaughlin, Timothy Fritton, Nishanth Nakshatri, Arjun Menon, Sai Ajay Modukuri, Rajal Nivargi, Xin Wei, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Explainably estimating confidence in published scholarly work offers opportunity for faster and more robust scientific progress. We develop a synthetic prediction market to assess the credibility of published claims in the social and behavioral sciences literature. We demonstrate our system and detail our findings using a collection of known replication projects. We suggest that this work lays the foundation for a research agenda that creatively uses AI for peer review.

Original languageEnglish (US)
Title of host publicationIAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations
PublisherAssociation for the Advancement of Artificial Intelligence
Pages13218-13220
Number of pages3
ISBN (Electronic)1577358767, 9781577358763
StatePublished - Jun 30 2022
Event36th AAAI Conference on Artificial Intelligence, AAAI 2022 - Virtual, Online
Duration: Feb 22 2022Mar 1 2022

Publication series

NameProceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022
Volume36

Conference

Conference36th AAAI Conference on Artificial Intelligence, AAAI 2022
CityVirtual, Online
Period2/22/223/1/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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