A Synthetic Prediction Market for Estimating Confidence in Published Work
| Autor: | Rajtmajer, S., Christopher Griffin, Wu, J., Fraleigh, R., Balaji, L., Squicciarini, A., Kwasnica, A., Pennock, D., Mclaughlin, M., Fritton, T., Nakshatri, N., Menon, A., Modukuri, S. A., Nivargi, R., Wei, X., Lee Giles, C. |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computers and Society Computer Science - Machine Learning Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence Computers and Society (cs.CY) Computer Science - Multiagent Systems General Medicine Information Retrieval (cs.IR) Machine Learning (cs.LG) Multiagent Systems (cs.MA) Computer Science - Information Retrieval |
| Zdroj: | Scopus-Elsevier |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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