TY - GEN
T1 - Supporting joint human-computer judgment under uncertainty
AU - Miller, Sarah
AU - Kirlik, Alex
AU - Kosorukoff, Alex
AU - Tsai, Jennifer
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70350591800&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350591800&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:70350591800
SN - 9781605606859
T3 - Proceedings of the Human Factors and Ergonomics Society
SP - 408
EP - 412
BT - 52nd Human Factors and Ergonomics Society Annual Meeting, HFES 2008
T2 - 52nd Human Factors and Ergonomics Society Annual Meeting, HFES 2008
Y2 - 22 September 2008 through 26 September 2008
ER -