arXiv:2605.20490v1 Announce Type: cross Abstract: In high-stakes automated decision-making, access to predictive uncertainty is essential for enabling users -- human or downstream systems -- to accept or reject predictions based on application-specific cost trade-offs. Such uncertainty-augmented (UA) systems -- i.e., systems that output both predictions and uncertainty scores -- are currently being assessed in the literature in a variety of ways, using separate metrics to evaluate the predictions and the uncertainty scores, setting a cost function with a fixed rejection cost or integrating ove
The proliferation of AI systems in high-stakes domains necessitates robust and standardized methods for evaluating their reliability, particularly concerning uncertainty quantification.
Advanced and principled evaluation metrics for uncertainty-augmented AI systems are crucial for fostering trust, ensuring safety, and enabling responsible deployment in critical applications.
The development of a unified metric family simplifies the complex evaluation landscape for uncertainty-augmented systems, moving away from disparate and application-specific assessments.
- · AI developers
- · High-stakes industries (e.g., healthcare, finance)
- · Regulatory bodies
- · Academic researchers
- · AI systems with poor uncertainty calibration
- · Developers relying on ad-hoc evaluation methods
Improved reliability and safety of AI deployments in critical sectors.
Accelerated adoption of AI in domains previously constrained by trust and safety concerns.
Potential for new regulatory frameworks and certifications based on standardized uncertainty evaluation.
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Read at arXiv cs.LG