mlsanityData to 14 Oct 2026

A Design Space for Explainable Ranking and Ranking Models

arXiv 2205.15305 ↗First posted 27 May 2022cs.LG cs.IRcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 38 references that are in the map. First posted on arXiv 27 May 2022.

Builds on

10 of 38 references.

References · 10

  1. 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 254
  2. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  3. 2018Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontierscited by 117
  4. 2019FairSight: Visual Analytics for Fairness in Decision Makingcited by 59
  5. 2020A Comparative Analysis of Industry Human-AI Interaction Guidelinescited by 10
  6. 2020Interpreting Neural Ranking Models using Grad-CAMcited by 5
  7. 2020ML-based Visualization Recommendation: Learning to Recommend Visualizations from Datacited by 5
  8. 2020Table Scraps: An Actionable Framework for Multi-Table Data Wrangling From An Artifact Study of Computational Journalismcited by 2
  9. 2020Auditing the Sensitivity of Graph-based Ranking with Visual Analyticscited by 1
  10. 2021Inspecting the Process of Bank Credit Rating via Visual Analyticscited by 1

Built on by

Not cited yet.