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This paper addresses a critical bottleneck in deploying complex AI systems by introducing ExCIR, a metric that resolves the misranking of correlated features inherent in standard post-hoc explainers like SHAP and LIME. By leveraging a lightweight single-pass formulation grounded in information theory, the method offers stable feature attribution without the computational overhead of costly data perturbations. This advancement is essential for scaling interpretable AI to high-dimensional domains such as medical diagnostics and autonomous driving where trustworthiness is paramount.
Explainability of Complex AI Models with Correlation Impact Ratio
arXiv:2601.06701v2 Announce Type: replace-cross Abstract: Complex AI systems make better predictions but often lack transparency, limiting trustworthiness, interpretability, and safe deployment. Common post hoc AI explainers, such as LIME, SHAP, HSIC, and SAGE, are model agnostic but are too restricted in one significant regard: they tend to misrank correlated features and require costly perturbations, which do not scale to high dimensional data. We introduce ExCIR (Explainability through Correlation Impact Ratio), a theoretically grounded, simple, and reliable metric for explaining the contribution of input features to model outputs, which remains stable and consistent under noise and sampling variations. We demonstrate that ExCIR captures dependencies arising from correlated features through a lightweight single pass formulation. Experimental evaluations on diverse datasets, including EEG, synthetic vehicular data, Digits, and Cats-Dogs, validate the effectiveness and stability of ExCIR across domains, achieving more interpretable feature explanations than existing methods while remaining computationally efficient. To this end, we further extend ExCIR with an information theoretic foundation that unifies the correlation ratio with Canonical Correlation Analysis under mutual information bounds, enabling multi output and class conditioned explainability at scale.
Abstract:Complex AI systems make better predictions but often lack transparency, limiting trustworthiness, interpretability, and safe deployment. Common post hoc AI explainers, such as LIME, SHAP, HSIC, and SAGE, are model agnostic but are too restricted in one significant regard: they tend to misrank correlated features and require costly perturbations, which do not scale to high dimensional data. We introduce ExCIR (Explainability through Correlation Impact Ratio), a theoretically grounded, simple, and reliable metric for explaining the contribution of input features to model outputs, which remains stable and consistent under noise and sampling variations. We demonstrate that ExCIR captures dependencies arising from correlated features through a lightweight single pass formulation. Experimental evaluations on diverse datasets, including EEG, synthetic vehicular data, Digits, and Cats-Dogs, validate the effectiveness and stability of ExCIR across domains, achieving more interpretable feature explanations than existing methods while remaining computationally efficient. To this end, we further extend ExCIR with an information theoretic foundation that unifies the correlation ratio with Canonical Correlation Analysis under mutual information bounds, enabling multi output and class conditioned explainability at scale.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Applications (stat.AP)
arXiv:2601.06701 [cs.LG]
(or arXiv:2601.06701v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2601.06701 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1109/TAI.2026.3732283 Focus to learn more DOI(s) linking to related resources
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