From Hallucination to Reliability: Generative Modeling and the Structure of Scientific Inference

Abstract:Generative AI is increasingly used in science, but is unavoidably prone to hallucination. I develop a reliabilist account of how generative AI nevertheless gives rise to new scientific knowledge. I analyze hallucinations as non-strategic misrepresentations of the target phenomenon, introduced by a model's generative activity, rather than inherited from training data. Through case studies of AlphaFold and SEEDS, I show how scientific workflows draw on pre-existing knowledge of target phenomena to filter or qualify hallucinatory outputs, thereby preventing their erroneous content from propagating into downstream inference. Finally, I show that workflows are units of epistemic evaluation in their own right.

Computers and Society (cs.CY); Artificial Intelligence (cs.AI)

arXiv:2504.08526 [cs.CY]

(or arXiv:2504.08526v3 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2504.08526 Focus to learn more arXiv-issued DOI via DataCite

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