Not Just How Much, But Where: Decomposing Epistemic Uncertainty into Per-Class Contributions
arXiv:2602.21160v3 Announce Type: replace-cross Abstract: In safety-critical classification, the cost of failure is often asymmetric, yet Bayesian deep learning summarises epistemic uncertainty with a single scalar, mutual information (MI), that cannot distinguish whether a model's ignorance involves a benign or safety-critical class. We decompose MI into a per-class vector $C_k(x)=\sigma_k^{2}/(2\mu_k)$, with $\mu_k{=}\mathbb{E}[p_k]$ and $\sigma_k^2{=}\mathrm{Var}[p_k]$ across posterior samples. The decomposition follows from a second-order Taylor expansion of the entropy; the $1/\mu_k$ weig
The increasing deployment of AI in safety-critical applications necessitates more granular understanding of model uncertainties, pushing research towards practical solutions for real-world reliability issues.
Improving the interpretability and reliability of AI models in high-consequence domains directly impacts safety, regulatory compliance, and broader societal trust in autonomous systems.
AI models can now provide a more nuanced understanding of their 'ignorance' by identifying which specific classes contribute to epistemic uncertainty, rather than just a single aggregate score.
- · AI Safety Researchers
- · Safety-critical AI Development (e.g., autonomous vehicles, medical diagnostics)
- · Regulatory Bodies for AI
- · Insurance Industry
- · Developers relying solely on aggregate uncertainty metrics
- · AI systems with opaque uncertainty quantification
Enhanced ability to pinpoint weaknesses and biases in AI classification models, especially concerning specific critical classes.
Accelerated development and adoption of AI in highly regulated and risk-averse industries due to improved trust and explainability.
Potential for new regulatory frameworks that require per-class uncertainty reporting for AI deployments in safety-critical contexts.
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Read at arXiv cs.LG