Hallucination, reliability, and the role of generative AI in science
Fuente:
arXiv
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915724719554560 |
|---|---|
| author | Rathkopf, Charles |
| author_facet | Rathkopf, Charles |
| contents | Generative AI increasingly supports scientific inference, from protein structure prediction to weather forecasting. Yet its distinctive failure mode, hallucination, raises epistemic alarm bells. I argue that this failure mode can be addressed by shifting from data-centric to phenomenon-centric assessment. Through case studies of AlphaFold and GenCast, I show how scientific workflows discipline generative models through theory-guided training and confidence-based error screening. These strategies convert hallucination from an unmanageable epistemic threat into bounded risk. When embedded in such workflows, generative models support reliable inference despite opacity, provided they operate in theoretically mature domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08526 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hallucination, reliability, and the role of generative AI in science Rathkopf, Charles Computers and Society Artificial Intelligence Generative AI increasingly supports scientific inference, from protein structure prediction to weather forecasting. Yet its distinctive failure mode, hallucination, raises epistemic alarm bells. I argue that this failure mode can be addressed by shifting from data-centric to phenomenon-centric assessment. Through case studies of AlphaFold and GenCast, I show how scientific workflows discipline generative models through theory-guided training and confidence-based error screening. These strategies convert hallucination from an unmanageable epistemic threat into bounded risk. When embedded in such workflows, generative models support reliable inference despite opacity, provided they operate in theoretically mature domains. |
| title | Hallucination, reliability, and the role of generative AI in science |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2504.08526 |