arXiv:2603.02673v2 Announce Type: replace-cross Abstract: Functional ANOVA offers a principled framework for interpretability by decomposing a model's prediction into main effects and higher-order interactions. For independent features, this decomposition is well-defined, strongly linked with SHAP values, and serves as a cornerstone of additive explainability. However, the lack of an explicit closed-form expression for general dependent distributions has forced practitioners to rely on costly sampling-based approximations. We completely resolve this limitation for categorical inputs. By bridgi
This research addresses a long-standing limitation in AI interpretability by providing a closed-form solution for categorical inputs, driven by the increasing need for transparency in complex models.
Improved interpretability of AI models is crucial for trust, regulatory compliance, and debugging, especially as AI systems become more ubiquitous in sensitive applications.
The ability to exactly decompose model predictions for categorical inputs makes complex models more transparent and auditable without relying on costly approximations.
- · AI interpretability researchers
- · Compliance and regulatory bodies
- · Industries deploying AI in sensitive applications
- · Model developers
- · Developers of less precise approximation methods
This enables more rigorous analysis and debugging of AI models with categorical features.
Increased transparency could accelerate adoption of AI in highly regulated sectors like finance and healthcare.
Greater trust in AI systems might lead to broader deployment in critical infrastructure, potentially raising new questions about systemic risk.
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Read at arXiv cs.LG