Understanding Censorship in Large Language Models: From Mechanisms to Governance

Large language models (LLMs) increasingly mediate access to information, yet their responses are shaped by training-data curation, alignment procedures, provider policies, inference-time moderation, and jurisdictional regulation. This paper examines LLM censorship as a sociotechnical phenomenon that extends beyond explicit refusals to include omissions, selective emphasis, framing effects, and geographically variable content controls. We synthesize recent empirical studies, provider case studies

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ethics.ai (16 June 2026), “Understanding Censorship in Large Language Models: From Mechanisms to Governance,” evidence record 863, https://ethics.ai/record/863 (originally published by arXiv).

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