arXiv:2607.08347v1 Announce Type: cross Abstract: Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled. However, existing estimators fail to exploit the informative predictions of powerful black--box models, even though such predictions are increasingly available in settings where labels remain expensive. To address this, we propose \textbf{Prediction--Powered Active Testing (PPAT)}, a novel label--efficient risk estimation framework that combines the unbiased LURE estimator \citep{farquhar2021statistical} with a pr
The increasing availability of powerful but expensive black-box AI models necessitates more efficient and accurate methods for risk estimation, even as the cost of obtaining human labels remains high.
This development allows for more accurate and cost-effective evaluation of AI systems, accelerating their deployment and integration into critical applications.
The ability to accurately quantify risk for AI systems using fewer human labels significantly reduces the bottleneck of validation and deployment for advanced AI models.
- · AI developers
- · Companies deploying AI
- · AI-driven quality assurance services
- · Traditional, labor-intensive AI testing methodologies
More robust and reliable AI systems become available faster due to efficient testing.
Reduced operational costs for AI model validation will drive further AI adoption across industries.
Enhanced trust in AI systems could accelerate the development and integration of AI agents into complex decision-making processes.
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