Nonparametric causal factor models for reliable generative AI — NSF Award to University of Chicago (IL, $200,000)
Modern artificial intelligence systems produce striking images, text, and other creative outputs, but it is often unclear what these systems have actually learned internally. This makes it difficult to ensure that these models are reliable, safe, and trustworthy when deployed in the real world. Although these models ca
| Award title | Nonparametric causal factor models for reliable generative AI |
|---|---|
| Award ID | 2610618 |
| Awardee | University of Chicago |
| City | CHICAGO |
| State | IL |
| Amount obligated | $200,000 |
| Principal investigator | Nikhyl Aragam |
| Program | STATISTICS |
| Start date | 06/01/2026 |
| Abstract | Modern artificial intelligence systems produce striking images, text, and other creative outputs, but it is often unclear what these systems have actually learned internally. This makes it difficult to ensure that these models are reliable, safe, and trustworthy when deployed in the real world. Although these models can imitate patterns in data, the process through which they do so does not necessarily correspond to meaningful causes, stable mechanisms, or interpretable concepts that stakeholder |
| Source | NSF Awards |
$799/mo
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