From Abstract to Actionable: Pairwise Shapley Values for Explainable AI

• URL: http://arxiv.org/abs/2502.12525v1
• Date: Tue, 18 Feb 2025 04:20:18 GMT
• Title: From Abstract to Actionable: Pairwise Shapley Values for Explainable AI
• Authors: Jiaxin Xu, Hung Chau, Angela Burden,
• Abstract summary: We propose Pairwise Shapley Values, a novel framework that grounds feature attributions in explicit, human-relatable comparisons. Our method introduces pairwise reference selection combined with single-value imputation to deliver intuitive, model-agnostic explanations. We demonstrate that Pairwise Shapley Values enhance interpretability across diverse regression and classification scenarios.
• Score: 0.8192907805418583
• License:
• Abstract: Explainable AI (XAI) is critical for ensuring transparency, accountability, and trust in machine learning systems as black-box models are increasingly deployed within high-stakes domains. Among XAI methods, Shapley values are widely used for their fairness and consistency axioms. However, prevalent Shapley value approximation methods commonly rely on abstract baselines or computationally intensive calculations, which can limit their interpretability and scalability. To address such challenges, we propose Pairwise Shapley Values, a novel framework that grounds feature attributions in explicit, human-relatable comparisons between pairs of data instances proximal in feature space. Our method introduces pairwise reference selection combined with single-value imputation to deliver intuitive, model-agnostic explanations while significantly reducing computational overhead. Here, we demonstrate that Pairwise Shapley Values enhance interpretability across diverse regression and classification scenarios--including real estate pricing, polymer property prediction, and drug discovery datasets. We conclude that the proposed methods enable more transparent AI systems and advance the real-world applicability of XAI.

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