A Design Space for Explainable Ranking and Ranking Models

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dc.contributor.authorHazwan, Ibrahim Alen_US
dc.contributor.authorSchmid, Jennyen_US
dc.contributor.authorSachdeva, Madhaven_US
dc.contributor.authorBernard, Jürgenen_US
dc.contributor.editorKrone, Michaelen_US
dc.contributor.editorLenti, Simoneen_US
dc.contributor.editorSchmidt, Johannaen_US
dc.date.accessioned2022-06-02T15:29:05Z
dc.date.available2022-06-02T15:29:05Z
dc.date.issued2022
dc.description.abstractItem ranking systems support users in multi-criteria decision-making tasks. Users need to trust rankings and ranking algorithms to reflect user preferences nicely while avoiding systematic errors and biases. However, today only few approaches help end users, model developers, and analysts to explain rankings. We report on the study of explanation approaches from the perspectives of recommender systems, explainable AI, and visualization research and propose the first cross-domain design space for explainers of item rankings. In addition, we leverage the descriptive power of the design space to characterize a) existing explainers and b) three main user groups involved in ranking explanation tasks. The generative power of the design space is a means for future designers and developers to create more target-oriented solutions in this only weakly exploited space.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEuroVis 2022 - Posters
dc.identifier.doi10.2312/evp.20221114
dc.identifier.isbn978-3-03868-185-4
dc.identifier.pages35-37
dc.identifier.pages3 pages
dc.identifier.urihttps://doi.org/10.2312/evp.20221114
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evp20221114
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleA Design Space for Explainable Ranking and Ranking Modelsen_US
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