arXiv:2605.29249v1 Announce Type: cross Abstract: Many applications require statistically valid inference across many related tasks, while using only a handful of high-quality labels per hypothesis. In AI evaluation, these tasks may correspond to model behaviors across prompts, subgroups, or hypotheses; in social science surveys, they may correspond to related questions, populations, or measurement conditions. Prediction-powered inference (PPI) uses abundant but inexpensive proxy measurements to improve inference from limited, ground-truth labels, but commonly used methods treat tasks independ
The proliferation of AI models across domains intensifies the need for robust, generalizable evaluation methods as their societal impact grows.
This research addresses a critical bottleneck in both AI development and social science by enabling more reliable inference from limited data, which directly impacts trust and deployment.
The ability to perform statistically valid inference across many tasks with limited high-quality labels improves the rigor and efficiency of AI evaluation and potentially accelerates scientific discovery.
- · AI developers
- · Social scientists
- · AI ethicists and evaluators
- · Data-scarce research fields
- · Organizations relying on superficial AI evaluations
- · Methods requiring extensive ground-truth labeling
Improved reliability and generalizability of AI models through more robust testing and evaluation.
Faster iteration cycles for AI development and deployment, as evaluation becomes more efficient and accurate.
Enhanced public trust in AI systems due to transparent and statistically sound evaluation frameworks, potentially accelerating widespread adoption in sensitive domains.
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Read at arXiv cs.LG