Reliable Methods for Estimation, Prediction and Causal Inference with Multiple AI-Generate — NSF Award to University of Wisconsin-
Artificial intelligence (AI) systems, including modern machine learning models and large language models, are now routinely used to generate predicted labels and synthetic data across science, medicine, and policy. Researchers increasingly rely on AI-generated datasets to replace expensive or scarce human-labeled data,
| Award title | Reliable Methods for Estimation, Prediction and Causal Inference with Multiple AI-Generate |
|---|---|
| Award ID | 2610561 |
| Awardee | University of Wisconsin-Madison |
| City | MADISON |
| State | WI |
| Amount obligated | $250,000 |
| Principal investigator | Jiwei Zhao |
| Program | STATISTICS |
| Start date | 07/01/2026 |
| Abstract | Artificial intelligence (AI) systems, including modern machine learning models and large language models, are now routinely used to generate predicted labels and synthetic data across science, medicine, and policy. Researchers increasingly rely on AI-generated datasets to replace expensive or scarce human-labeled data, but the quality of such outputs is often unknown and can vary widely across AI systems. Treating AI predictions as ground truth can lead to incorrect scientific conclusions, misle |
| Source | NSF Awards |
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