# A Design Space for Explainable Ranking and Ranking Models

arXiv: https://arxiv.org/abs/2205.15305 · HTML: https://mlsanity.com/paper/2205.15305/

## 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 (most cited first, 10 of 10)

- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021)
- [Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers](https://mlsanity.com/paper/1801.06889/) (2018)
- [FairSight: Visual Analytics for Fairness in Decision Making](https://mlsanity.com/paper/1908.00176/) (2019)
- [A Comparative Analysis of Industry Human-AI Interaction Guidelines](https://mlsanity.com/paper/2010.11761/) (2020)
- [Interpreting Neural Ranking Models using Grad-CAM](https://mlsanity.com/paper/2005.05768/) (2020)
- [ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data](https://mlsanity.com/paper/2009.12316/) (2020)
- [Table Scraps: An Actionable Framework for Multi-Table Data Wrangling From An Artifact Study of Computational Journalism](https://mlsanity.com/paper/2009.02373/) (2020)
- [Auditing the Sensitivity of Graph-based Ranking with Visual Analytics](https://mlsanity.com/paper/2009.07227/) (2020)
- [Inspecting the Process of Bank Credit Rating via Visual Analytics](https://mlsanity.com/paper/2108.03011/) (2021)

## 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.