arXiv:2606.19569v1 Announce Type: new Abstract: Uncertainty quantification (UQ) is essential for reliable decision-making in safety-critical applications in probabilistic machine learning. For regression problems, dominant scalar UQ approaches - notably, those based on proper scoring rules - measure uncertainty via pointwise predictive risk. This can lead to counterintuitive results when the target statistic is not the conditional expectation. We propose an alternative framework, in which uncertainty is characterised by the volume of the most probable subset of a distribution's support. QUEST
The increasing sophistication and widespread deployment of AI in safety-critical applications necessitate more robust and interpretable uncertainty quantification methods to ensure reliability, especially as AI systems move from research to production environments.
Improved uncertainty quantification methods, particularly those moving beyond scalar approaches, enhance the trustworthiness and practical applicability of AI in high-stakes domains, directly impacting decision-making quality and safety.
The focus shifts from pointwise predictive risk to characterizing uncertainty through the volume of most probable subsets, offering a more comprehensive and potentially less counterintuitive measure for probabilistic machine learning.
- · AI safety researchers
- · Safety-critical AI applications (e.g., autonomous vehicles, medical AI)
- · Probabilistic machine learning developers
- · AI systems with poor UQ
- · Traditional scalar UQ methods
More reliable and deployable AI systems in critical sectors.
Accelerated adoption of AI in previously risk-averse industries due to enhanced trust and explainability.
New regulatory frameworks and standards for AI trustworthiness that incorporate advanced UQ metrics.
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Read at arXiv cs.LG