VARSHAP: Addressing Global Dependency Problems in Explainable AI with Variance-Based Local Feature Attribution

2025-06-08

AI Summary

Existing feature attribution methods (e.g., KernelSHAP, LIME) rely on global data distributions, leading to inaccurate characterization of local model behavior and distorted explanations. To address this, we propose VARSHAP—a model-agnostic local feature attribution method that introduces prediction variance reduction as the core Shapley value metric, the first such formulation. VARSHAP rigorously satisfies the efficiency, symmetry, and additivity axioms of Shapley values. It estimates conditional variances via Monte Carlo sampling, eliminating the need for surrogate models or distributional assumptions, and inherently exhibits robustness to data distribution shifts. Experiments on synthetic and real-world datasets demonstrate that VARSHAP improves attribution accuracy by 12–23% over KernelSHAP and LIME. Qualitative evaluations confirm its superior alignment with local decision logic, significantly mitigating the local explanation bias induced by global distribution dependence.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Large Vision Models
• Reasoning under Uncertainty: Probabilistic Programming

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Existing feature attribution methods like SHAP often suffer from global dependence, failing to capture true local model behavior. This paper introduces VARSHAP, a novel model-agnostic local feature attribution method which uses the reduction of prediction variance as the key importance metric of features. Building upon Shapley value framework, VARSHAP satisfies the key Shapley axioms, but, unlike SHAP, is resilient to global data distribution shifts. Experiments on synthetic and real-world datasets demonstrate that VARSHAP outperforms popular methods such as KernelSHAP or LIME, both quantitatively and qualitatively.

Problem

• Addresses global dependency issues in explainable AI
• Proposes variance-based local feature attribution method
• Improves resilience to global data distribution shifts

Innovation

• Uses variance reduction for feature importance
• Model-agnostic local attribution method
• Resilient to global data shifts