From Abstract to Actionable: Pairwise Shapley Values for Explainable AI

Shapley values are a rigorous but hard-to-use tool for AI transparency: While Shapley values are mathematically sound for explaining AI decisions, existing methods often rely on abstract concepts or require heavy computation, making them difficult for non-experts to understand and impractical to deploy at scale.

Pairwise comparisons make AI explanations more intuitive: The paper's key innovation grounds explanations in concrete, side-by-side comparisons between similar real examples rather than abstract baselines, making it easier for stakeholders to understand why an AI system made a particular decision (e.g., why one house was valued higher than another).

The new method is faster and more practical: By reducing computational overhead while maintaining mathematical rigor, Pairwise Shapley Values make explainability feasible for real-world applications in high-stakes domains like real estate, drug discovery, and materials science—areas where understanding AI decisions is critical for accountability.

Explainability works across different types of AI problems: The method was tested on diverse scenarios (regression and classification tasks), suggesting it could be broadly applicable across different industries and use cases, not just specialized applications.

Better explanations support trust and accountability: By making AI decisions more transparent and understandable to humans, this approach helps organizations meet regulatory requirements and build justified trust with users and stakeholders in sensitive domains.