TY - GEN
T1 - Evaluating Forecasting, Knowledge, and Visual Analytics
AU - Lu, Yafeng
AU - Steptoe, Michael
AU - Buchanan, Verica
AU - Cooke, Nancy
AU - Maciejewski, Ross
N1 - Funding Information:
This work was supported by the U.S. Department of Homeland Security under Grant Award 2017-ST-061-QA0001 and 17STQAC00001-03-03. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In this paper, we explore the intersection of knowledge and the forecasting accuracy of humans when supported by visual analytics. We have recruited 40 experts in machine learning and trained them in the use of a box office forecasting visual analytics system. Our goal was to explore the impact of visual analytics and knowledge in human-machine forecasting. This paper reports on how participants explore and reason with data and develop a forecast when provided with a predictive model of middling performance (R2 ≈ .7). We vary the knowledge base of the participants through training, compare the forecasts to the baseline model, and discuss performance in the context of previous work on algorithmic aversion and trust.
AB - In this paper, we explore the intersection of knowledge and the forecasting accuracy of humans when supported by visual analytics. We have recruited 40 experts in machine learning and trained them in the use of a box office forecasting visual analytics system. Our goal was to explore the impact of visual analytics and knowledge in human-machine forecasting. This paper reports on how participants explore and reason with data and develop a forecast when provided with a predictive model of middling performance (R2 ≈ .7). We vary the knowledge base of the participants through training, compare the forecasts to the baseline model, and discuss performance in the context of previous work on algorithmic aversion and trust.
UR - http://www.scopus.com/inward/record.url?scp=85123805588&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123805588&partnerID=8YFLogxK
U2 - 10.1109/TREX53765.2021.00011
DO - 10.1109/TREX53765.2021.00011
M3 - Conference contribution
AN - SCOPUS:85123805588
T3 - Proceedings - 2021 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2021
SP - 32
EP - 39
BT - Proceedings - 2021 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2021
Y2 - 24 October 2021
ER -