How to Manage Misinformation in Large Language Models

Search engines and other information retrieval tools that utilize large language models (LLMs) are growing rapidly. But their dependence on online data introduces a critical vulnerability: the open internet is now a highly adversarial space, where distinguishing fact from falsehood is incredibly difficult. From state-backed influence campaigns to commercial content farms, many actors are attempting to shape what LLMs “learn,” and thus what they portray as “facts.” These distortions—ranging from fraudulent financial content to coordinated political manipulation—pose growing risks to the epistemic and ethical integrity of AI systems and the greater information ecosystem.

Managing these risks is no longer a technical task; it’s a trust and governance challenge central to AI’s legitimacy. This shift has transformed data collection from a technical exercise into a high-stakes trust problem. When misinformation, spam, or deliberate data poisoning enter a model’s training corpus, the system risks not only factual inaccuracies but also reputational and regulatory damage. Understanding how information is produced, distorted, and distributed online is therefore essential to anyone building or governing large-scale AI systems.