Supporting joint human-computer judgment under uncertainty

Sarah Miller, Alex Kirlik, Alex Kosorukoff, Jennifer Tsai

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


In this paper we present a concept and interface design aimed at combining expert human judgment with computational support. The goal of this design is to leverage the strengths and simultaneously compensate for the weaknesses of both the expert and a computational model. In order to test the design, we created a task modeled after fantasy baseball, which requires competitors to predict the performance of actual Major League Baseball (MLB) players over the course of the season. The most substantial and challenging aspects of the design involved how to both welcome expert input on a case-by-case basis, yet also provide visual guidance for how these inputs should reflect an appropriate degree of regression to the mean, or reliance on base-rate information. Results showed that the joint human-model system resulted in better performance than a model, which was based, in part, on past performance. The joint system also outperformed unaided or partially-aided experts in some cases but only equally as well in other cases. Design implications and future directions are discussed.

Original languageEnglish (US)
Title of host publication52nd Human Factors and Ergonomics Society Annual Meeting, HFES 2008
Number of pages5
StatePublished - 2008
Event52nd Human Factors and Ergonomics Society Annual Meeting, HFES 2008 - New York, NY, United States
Duration: Sep 22 2008Sep 26 2008

Publication series

NameProceedings of the Human Factors and Ergonomics Society
ISSN (Print)1071-1813


Other52nd Human Factors and Ergonomics Society Annual Meeting, HFES 2008
Country/TerritoryUnited States
CityNew York, NY

ASJC Scopus subject areas

  • Human Factors and Ergonomics


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