arXiv:2507.00260v3 Announce Type: replace-cross Abstract: When predictors are statistically dependent, the appropriate definition of feature importance depends on the operational goal. Conditional-incremental measures are well-suited for feature selection, acquisition, and compression, where shared predictive information is treated as redundancy. For post-hoc interpretation, however, the goal is often to attribute predictive signals across correlated measurement channels. We introduce Disentangled Feature Importance (DFI), a population-level attribution framework for this setting. DFI maps cov
The increasing complexity and opacity of AI models necessitate improved interpretation methods for their outputs, especially in correlated data environments.
Disentangled Feature Importance (DFI) offers a new framework for understanding how AI models attribute predictive signals, moving beyond traditional feature importance with dependent predictors.
This framework could lead to more robust, interpretable, and trustworthy AI models, particularly in domains where data features are highly correlated.
- · AI interpretability researchers
- · High-stakes AI application developers
- · Regulators of AI systems
- · Black-box AI models in regulated industries
- · Inadequate feature importance methodologies
Improved understanding of how complex AI models make decisions in real-world scenarios with correlated features.
Faster adoption and regulatory approval for AI systems that can clearly demonstrate their attribution logic.
Enhanced ability to debug biased or faulty AI models by precisely identifying the contributing features, even in complex interdependencies.
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Read at arXiv cs.LG