# ShaRP: Explaining Rankings and Preferences with Shapley Values

arXiv: https://arxiv.org/abs/2401.16744 · HTML: https://mlsanity.com/paper/2401.16744/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 17 of 17)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [Explaining by Removing: A Unified Framework for Model Explanation](https://mlsanity.com/paper/2011.14878/) (2020)
- [Evaluating and Aggregating Feature-based Model Explanations](https://mlsanity.com/paper/2005.00631/) (2020)
- [Algorithms to estimate Shapley value feature attributions](https://mlsanity.com/paper/2207.07605/) (2022)
- [A study on the Interpretability of Neural Retrieval Models using DeepSHAP](https://mlsanity.com/paper/1907.06484/) (2019)
- [EXS: Explainable Search Using Local Model Agnostic Interpretability](https://mlsanity.com/paper/1809.03857/) (2018)
- [An unexpected unity among methods for interpreting model predictions](https://mlsanity.com/paper/1611.07478/) (2016)
- [Learning Inconsistent Preferences with Gaussian Processes](https://mlsanity.com/paper/2006.03847/) (2020)
- [A Nutritional Label for Rankings](https://mlsanity.com/paper/1804.07890/) (2018)
- [Posthoc Interpretability of Learning to Rank Models using Secondary Training Data](https://mlsanity.com/paper/1806.11330/) (2018)
- [Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank](https://mlsanity.com/paper/2212.12722/) (2022)
- [Identifying Biased Subgroups in Ranking and Classification](https://mlsanity.com/paper/2108.07450/) (2021)

## Built on by (most cited first, 3 of 3)

- [ExplainerPFN: Towards tabular foundation models for model-free zero-shot feature importance estimations](https://mlsanity.com/paper/2601.23068/) (2026)
- [Output Scouting: Auditing Large Language Models for Catastrophic Responses](https://mlsanity.com/paper/2410.05305/) (2024)
- [CQD-SHAP: Explainable Complex Query Answering via Shapley Values](https://mlsanity.com/paper/2510.15623/) (2025)

## Abstract (arXiv)

Algorithmic decisions in critical domains such as hiring, college admissions, and lending are often based on rankings. Given the impact of these decisions on individuals, organizations, and population groups, it is essential to understand them - to help individuals improve their ranking position, design better ranking procedures, and ensure legal compliance. In this paper, we argue that explainability methods for classification and regression, such as SHAP, are insufficient for ranking tasks, and present ShaRP - Shapley Values for Rankings and Preferences - a framework that explains the contributions of features to various aspects of a ranked outcome. ShaRP computes feature contributions for various ranking-specific profit functions, such as rank and top-k, and also includes a novel Shapley value-based method for explaining pairwise preference outcomes. We provide a flexible implementation of ShaRP, capable of efficiently and comprehensively explaining ranked and pairwise outcomes over tabular data, in score-based ranking and learning-to-rank tasks. Finally, we develop a comprehensive evaluation methodology for ranking explainability methods, showing through qualitative, quantitative, and usability studies that our rank-aware QoIs offer complementary insights, scale effectively, and help users interpret ranked outcomes in practice.