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Addressing the limitations of individual attribution scores via the Shapleyvalue (SV), the field of explainable AI (XAI) has recently explored intricateinteractions of features or data points. In particular, extensions of the SV,such as the Shapley Interaction Index (SII), have been proposed as a measure tostill benefit from the axiomatic basis of the SV. However, similar to the SV,their exact computation remains computationally prohibitive. Hence, we proposewith SVARM-IQ a sampling-based approach to efficiently approximateShapley-based interaction indices of any order. SVARM-IQ can be applied to abroad class of interaction indices, including the SII, by leveraging a novelstratified representation. We provide non-asymptotic theoretical guarantees onits approximation quality and empirically demonstrate that SVARM-IQ achievesstate-of-the-art estimation results in practical XAI scenarios on differentmodel classes and application domains.

SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification