arXiv:2509.08846v2 Announce Type: replace Abstract: Evaluation of per-sample uncertainty quantification from neural networks is essential for decision-making involving high-risk applications. A common approach is to use the predictive distribution from Bayesian or approximation models and decompose the corresponding predictive uncertainty into epistemic (model-related) and aleatoric (data-related) components. However, additive decomposition has recently been questioned. In this work, we propose an intuitive framework for uncertainty estimation and decomposition based on the signal-to-noise rat
The continuous drive for more reliable and interpretable AI systems, especially in high-stakes applications, necessitates advancements in uncertainty quantification methods like this new framework.
Improved uncertainty estimation in neural networks is critical for deploying AI in sensitive domains, enabling more robust decision-making and reducing risks associated with unquantified predictions.
This new framework offers a potentially more intuitive and robust method for decomposing and understanding predictive uncertainty, moving beyond previous questioned additive decomposition approaches.
- · AI safety researchers
- · High-risk industries (e.g., healthcare, autonomous driving)
- · Developers of foundational AI models
- · AI systems lacking transparent uncertainty quantification
- · Legacy uncertainty estimation methods
- · Applications where opaque AI decisions are tolerated
Neural networks in critical applications will become more trustworthy and easier to validate.
Increased adoption of AI in regulatory-heavy and safety-critical sectors due to enhanced reliability.
New industry standards and compliance frameworks may emerge, mandating specific uncertainty quantification methodologies for AI deployments.
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Read at arXiv cs.LG