arXiv:2606.26990v1 Announce Type: new Abstract: Uncertainty estimates in machine learning are typically evaluated using generic metrics such as the negative log-likelihood and expected calibration error, yet good performance on such metrics does not necessarily imply high utility in downstream decisions. We introduce decision-alignment, a criterion that reveals which evaluation metrics meaningfully align with downstream utilities. Applying this framework, we show that many widely used uncertainty metrics are either misaligned with common decision problems or encode pathological prior beliefs a
The increasing deployment of AI in high-stakes domains necessitates more trustworthy and reliable AI systems, making robust uncertainty quantification critical.
This research provides a framework for evaluating AI uncertainty estimates based on their actual utility in decision-making, which is crucial for building reliable and auditable AI.
The focus for evaluating AI uncertainty shifts from generic statistical metrics to decision-aligned metrics, directly impacting how AI models are developed and trusted.
- · AI safety researchers
- · Developers of high-stakes AI systems
- · Industries relying on AI for critical decisions
- · AI testing and validation platforms
- · AI models with uncalibrated uncertainty
- · Generic uncertainty metrics
- · AI developers ignoring decision utility
AI models will be developed with an increased emphasis on decision-aligned uncertainty quantification methods.
Improved trust and adoption of AI systems in critical applications like healthcare, finance, and autonomous vehicles.
New regulatory standards and certifications for AI will likely incorporate decision-aligned uncertainty evaluation as a key requirement.
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Read at arXiv cs.LG