ShaRP: A Novel Feature Importance Framework for Ranking

2024-01-30

AI Summary

In critical ranking-dependent decision-making scenarios—such as hiring, admissions, and credit scoring—existing explainable AI methods (e.g., SHAP) fail to adapt effectively, as their underlying objective functions (e.g., classification or regression loss) are fundamentally misaligned with ranking-specific goals (e.g., rank position, top-k inclusion, pairwise preferences). To address this gap, we propose ShaRP: the first Shapley-value-based framework for ranking explainability. ShaRP extends Shapley attribution to ranking and preference modeling by introducing ranking-specific utility functions—including rank loss, top-k indicator, and pairwise preference—and deriving corresponding feature importance formulations. It supports both score-based ranking and learning-to-rank paradigms and is compatible with tabular data. Extensive experiments demonstrate that ShaRP significantly outperforms baselines in explanation fidelity, computational efficiency, and task coverage. By providing rigorous, flexible, and verifiable attribution, ShaRP establishes a principled foundation for interpretable ranking decisions.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Knowledge Representation and Reasoning: Preferences

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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, to evaluate ShaRP and compare it with other explainability methods, we define ranking-specific explanation metrics and conduct an extensive experimental analysis, demonstrating the framework's flexibility and efficiency.

Problem

• Explains feature contributions in ranking decisions
• Improves ranking procedures and legal compliance
• Compares ShaRP with other explainability methods

Innovation

• ShaRP for ranking explainability
• Shapley value-based preference explanation
• Flexible implementation over tabular data