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Explaining AI systems is fundamental both to the development of highperforming models and to the trust placed in them by their users. The Shapleyframework for explainability has strength in its general applicability combinedwith its precise, rigorous foundation: it provides a common, model-agnosticlanguage for AI explainability and uniquely satisfies a set of intuitivemathematical axioms. However, Shapley values are too restrictive in onesignificant regard: they ignore all causal structure in the data. We introducea less restrictive framework, Asymmetric Shapley values (ASVs), which arerigorously founded on a set of axioms, applicable to any AI system, andflexible enough to incorporate any causal structure known to be respected bythe data. We demonstrate that ASVs can (i) improve model explanations byincorporating causal information, (ii) provide an unambiguous test for unfairdiscrimination in model predictions, (iii) enable sequentially incrementalexplanations in time-series models, and (iv) support feature-selection studieswithout the need for model retraining.

Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability