A Design Space for Explainable Ranking and Ranking Models

• Published in: arXiv.org
• Main Authors: I Al Hazwani, Schmid, J, Sachdeva, M, Bernard, J
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 27.05.2022
• Subjects: Algorithms; Decision making; Design; End users; Multiple criterion; Ranking; Ratings & rankings; Recommender systems; Systematic errors; User groups

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...