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

Generative Artificial Intelligence, despite its inherent propensity for 'hallucination' or non-strategic misrepresentation, is emerging as a significant tool for scientific knowledge generation. This research posits that the reliability of scientific outcomes derived from generative AI is achieved not by eliminating hallucination at the model level, but through robust scientific workflows that leverage pre-existing knowledge to filter and qualify AI outputs, preventing the propagation of erroneous information.

Why it matters

This analysis is critical for organisations integrating generative AI into research and development, as it provides a framework for understanding and managing the inherent risks of AI-driven 'hallucination.' It highlights that the strategic value of generative AI in knowledge creation depends less on perfect model outputs and more on the design and execution of rigorous validation workflows. This shifts focus from solely technical AI development to the broader operational and epistemic processes surrounding AI deployment.

Key insights

• Generative AI, increasingly utilized in scientific contexts, is inherently susceptible to 'hallucination.'
• Hallucinations are defined as non-strategic misrepresentations introduced by the model's generative activity, distinct from issues originating in training data.
• New scientific knowledge can reliably arise from generative AI despite its hallucinatory tendencies.
• Reliability is achieved through scientific workflows that employ pre-existing domain knowledge to filter and qualify potentially hallucinatory AI outputs.
• This filtering mechanism prevents erroneous content from propagating into subsequent inferential steps.
• Scientific workflows themselves function as independent units of epistemic evaluation, ensuring the validity of AI-generated insights.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2504.08526