A Design Space for Explainable Ranking and Ranking Models
Loading...
Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Item 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.
Description
@inproceedings{10.2312:evp.20221114,
booktitle = {EuroVis 2022 - Posters},
editor = {Krone, Michael and Lenti, Simone and Schmidt, Johanna},
title = {{A Design Space for Explainable Ranking and Ranking Models}},
author = {Hazwan, Ibrahim Al and Schmid, Jenny and Sachdeva, Madhav and Bernard, Jürgen},
year = {2022},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {10.2312/evp.20221114}
}