@inproceedings{fc1e0f4288d84078a3b675164b27852d,
title = "Why evaluating uncertainty visualization is error prone",
abstract = "Evaluating a visualization that depicts uncertainty is fraught with challenges due to the complex psychology of uncertainty. However, relatively little attention is paid to selecting and motivating a chosen interpretation or elicitation method for subjective probabilities in the uncertainty visualization literature. I survey existing evaluation work in uncertainty visualization, and examine how research in judgment and decision-making that focuses on subjective uncertainty elicitation sheds light on common approaches in visualization. I propose suggestions for practice aimed at reducing errors and noise related to how ground truth is defined for subjective probability estimates, the choice of an elicitation method, and the strategies used by subjects making judgments with an uncertainty visualization.",
keywords = "Elicitation, Subjective probability distribution, Uncertainty visualization",
author = "Jessica Hullman",
year = "2016",
month = oct,
day = "24",
doi = "10.1145/2993901.2993919",
language = "English (US)",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "143--151",
editor = "Michael Sedlmair and Heidi Lam and Petra Isenberg and Tobias Isenberg and Narges Mahyar",
booktitle = "Proceedings - Beyond Time and Errors",
note = "6th Workshop Beyond Time and Errors on Novel Evaluation Methods for Visualization, BELIV 2016 ; Conference date: 24-10-2016",
}