Best Practices for Using Data Generated by AI or Machine Learning — NSF Award to Yale University (CT, $307,273)
Empirical analysts now routinely generate new data by deploying artificial intelligence (AI) or machine learning (ML) algorithms on large, unstructured data sets. Examples include quantifying sentiment or uncertainty in news text using large language models or natural language processing methods; measuring product char
| Award title | Best Practices for Using Data Generated by AI or Machine Learning |
|---|---|
| Award ID | 2521471 |
| Awardee | Yale University |
| City | NEW HAVEN |
| State | CT |
| Amount obligated | $307,273 |
| Principal investigator | Timothy Christensen |
| Program | Economics, Methodology, Measuremt & Stats |
| Start date | 08/15/2025 |
| Abstract | Empirical analysts now routinely generate new data by deploying artificial intelligence (AI) or machine learning (ML) algorithms on large, unstructured data sets. Examples include quantifying sentiment or uncertainty in news text using large language models or natural language processing methods; measuring product characteristics from review text and product images on online platforms; or imputing missing variables from demographic information. In standard practice, AI- or ML-generated data are |
| Source | NSF Awards |
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