SHAP-IQ: Unified Approximation of any-order Shapley Interactions

Understanding feature interactions is crucial for AI transparency: The paper addresses a gap in explainable AI by developing methods to identify not just individual feature importance, but how features work together to influence model decisions—a more complete picture of how AI systems make predictions.

Multiple valid approaches to measuring interactions exist: The research confirms that three different mathematical definitions of "Shapley interactions" have been proposed in the field, each based on different principles. SHAP-IQ provides a unified framework that works across these definitions, helping standardize how organizations can measure feature interactions.

New method offers theoretical guarantees and efficiency: Unlike previous approximation techniques, SHAP-IQ provides mathematical guarantees about how accurate its explanations are and can estimate the uncertainty in its results—important for building trust in AI explanations used in high-stakes decisions.

Practical applicability across diverse AI systems: The method was tested on language models, image classifiers, and complex synthetic models, demonstrating that this approach to understanding feature interactions can work across different types of AI systems that organizations actually deploy.

Simplified calculation for standard feature importance: As a bonus finding, the research reveals a simpler way to calculate traditional Shapley values (the most common XAI metric), which could make feature attribution explanations faster and more accessible to practitioners.