Bridging cognitive gaps between user and model in interactive dimension reduction

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dc.contributor.authorWang, Mingen
dc.contributor.authorWenskovitch, Johnen
dc.contributor.authorHouse, Leanna L.en
dc.contributor.authorPolys, Nicholas F.en
dc.contributor.authorNorth, Christopher L.en
dc.date.accessioned2022-04-26T12:55:32Zen
dc.date.available2022-04-26T12:55:32Zen
dc.date.issued2021-06en
dc.description.abstractInteractive machine learning (ML) systems are difficult to design because of the "Two Black Boxes" problem that exists at the interface between human and machine. Many algorithms that are used in interactive ML systems are black boxes that are presented to users, while the human cognition represents a second black box that can be difficult for the algorithm to interpret. These black boxes create cognitive gaps between the user and the interactive ML model. In this paper, we identify several cognitive gaps that exist in a previously-developed interactive visual analytics (VA) system, Andromeda, but are also representative of common problems in other VA systems. Our goal with this work is to open both black boxes and bridge these cognitive gaps by making usability improvements to the original Andromeda system. These include designing new visual features to help people better understand how Andromeda processes and interacts with data, as well as improving the underlying algorithm so that the system can better implement the intent of the user during the data exploration process. We evaluate our designs through both qualitative and quantitative analysis, and the results confirm that the improved Andromeda system outperforms the original version in a series of high-dimensional data analysis tasks. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd.en
dc.description.notesThis work was supported in part by NSF grant CSSI-2003387 and NSF I/UCRC CNS-1822080 via the NSF Center for Space, Highperformance, and Resilient Computing (SHREC).en
dc.description.sponsorshipNSFNational Science Foundation (NSF) [CSSI-2003387]; NSF I/UCRC via the NSF Center for Space, Highperformance, and Resilient Computing (SHREC) [CNS-1822080]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.visinf.2021.03.002en
dc.identifier.issn2468-502Xen
dc.identifier.issue2en
dc.identifier.urihttp://hdl.handle.net/10919/109748en
dc.identifier.volume5en
dc.language.isoenen
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectInteractive machine learningen
dc.subjectVisual analyticsen
dc.subjectDimension reductionen
dc.subjectUsabilityen
dc.subjectCognitive gapsen
dc.titleBridging cognitive gaps between user and model in interactive dimension reductionen
dc.title.serialVisual Informaticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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