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

Charles Rathkopf

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arXiv · 2504.08526

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

Charles Rathkopf

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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.

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Charles Rathkopf. 2025-04-11. From Hallucination to Reliability: Generative Modeling and the Structure of Scientific Inference. https://arxiv.org/abs/2504.08526

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