BibTex
@inproceedings{Nowak:2022:10.20380/GI2022.13,
author = {Nowak, Stan and Bartram, Lyn},
title = {I’m Not Sure: Designing for Ambiguity in Visual Analytics},
booktitle = {Proceedings of Graphics Interface 2022},
series = {GI 2022},
year = {2022},
issn = {0713-5424},
location = {Montr{\'e}al, Quebec},
pages = {114 -- 126},
numpages = {12},
doi = {10.20380/GI2022.13},
publisher = {Canadian Information Processing Society},
}
Abstract
Ambiguity, the state in which alternative interpretations are plausible or even desirable, is an inexorable part of complex sensemaking. Its challenges are compounded when analysis involves risk, is constrained, and needs to be shared with others. We report on several studies with avalanche forecasters that illuminated these challenges and identified how visualization designs can better support ambiguity. Like many complex analysis domains, avalanche forecasting relies on highly heterogeneous and incomplete data where the relevance and meaning of such data is context-sensitive, dependant on the knowledge and experiences of the observer, and mediated by the complexities of communication and collaboration. In this paper, we characterize challenges of ambiguous interpretation emerging from data, analytic processes, and collaboration and communication and describe several management strategies for ambiguity. Our findings suggest several visual analytics design approaches that explicitly address ambiguity in complex sensemaking around risk.