arXiv:2605.25234v1 Announce Type: new Abstract: Epistemic uncertainty is often viewed as a reducible uncertainty that vanishes with increasing data. This perspective implicitly assumes parameter identifiability and equates epistemic uncertainty with predictive variability. In overparametrized neural networks, however, model parameters are typically non-identifiable due to symmetries and redundant representations. As a consequence, substantial parameter uncertainty can persist even when the underlying function is fully identified. In this work, we analyze epistemic uncertainty through the lens
The paper contributes to the ongoing research into the fundamental limitations and theoretical underpinnings of increasingly complex AI models, particularly in the context of overparameterization.
Understanding the nature of epistemic uncertainty in neural networks is crucial for developing robust, reliable, and trustworthy AI systems, especially for high-stakes applications.
This theoretical work refines our understanding of how uncertainty manifests in large AI models, potentially leading to new methods for quantifying and managing model confidence.
- · AI Safety Researchers
- · Developers of robust AI applications
- · Academic AI research institutions
- · Developers of overconfident AI systems
- · Users relying on black-box AI without uncertainty quantification
Improved understanding of AI model limitations and the nature of their predictions.
Development of new AI architectures or training methodologies specifically designed to better characterize and reduce 'reducible' uncertainty.
Enhanced trust and broader adoption of AI systems in sensitive domains once their uncertainty can be more reliably assessed and communicated.
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Read at arXiv cs.LG