Are LLMs Stifling Political Speech? An Assessment of How AI Models Protect Free Expression | Oversight Board

Skip to content

Submit an Appeal

Close

Search

Search

Are LLMs Stifling Political Speech? An Assessment of How AI Models Protect Free Expression

July 16, 2026

Download Full Report

Download Executive Summary Only

Executive Summary

The Oversight Board’s first evaluation of large language models (LLMs) shows that some of the world’s most-used models from Anthropic, DeepSeek, Google, Meta and OpenAI are significantly less likely to criticize political regimes that restrict free expression. The research, which stems from the Board’s case work on government pressure on social media platforms, tested to what extent AI outputs reflect national laws outlawing criticism of leaders and governments. Our findings suggest that LLM users may be experiencing free speech infringements by proxy, with limited transparency. Whether through intentional design choices or not, model responses reinforce the laws and customs of restrictive speech regimes. This research highlights the importance of building systematic human rights analysis into processes for training and evaluating LLMs.

Key Finding: LLMs Tested are More Than Twice as Likely to Refuse to Criticize Repressive Leaders and Governments

The Board tested 10 commercial LLMs, asking the models to produce politically critical materials about governments and leaders around the world. Each model was tested through standard commercial interfaces provided by Google and Microsoft, hosted on infrastructure located primarily in the United States, and queried from an IP address in Australia. The Board found that models were more than twice as likely to refuse to criticize repressive regimes, as measured by non-governmental organization Freedom House (see Figure 1, below). Overall, for requests for politically critical materials, models on average refused only 14% of requests regarding permissive jurisdictions compared to 34% of requests for restrictive jurisdictions.

Figure 1. Refusal rate by jurisdiction to critical material production prompts (flyers and poems).

Governments, companies and international organizations increasingly rely on applications built on top of these models to make products with broad impacts on people around the world. This research suggests that applications built on many major LLMs could be inadvertently propagating restrictions on free speech that may reflect the efforts of particular governments to stifle political criticism and restrict freedom of expression more generally.

Political criticism is protected under international human rights law, which limits governments from imposing restraints on speech. When LLM foundation models (large AI systems trained on vast amounts of data) refuse to engage in political criticism, they potentially suppress expression across all products that use the model. And because it is not easy to detect biases in foundation model performance, users of AI tools all around the world may be unaware of how they are impacted by differences in model performance, either between models or across jurisdictions.

Refusals Come in Many Forms, Potentially Confusing Users

Our results highlight that differences in how models refuse to answer could lead to further confusion among users. When the models we examined refused to generate an answer to the question we asked them, their refusals came in many different forms. Sometimes a model would provide a very short refusal without explanation; at other times, models cited legal, policy or safety reasons for not complying with requests. For example, in one exchange, Claude Opus 4 replied:

“… Creating political materials criticizing governments, especially for use in protests, could:

1. Potentially put individuals at risk if used in certain contexts

2. Involve me in sensitive political activities that are outside my appropriate role

3. Create content that could be inflammatory or escalate tension

4. Be used in ways I cannot predict or control …”

At other times, models refusing to answer pointed to laws in the relevant jurisdiction or stated that local laws influence their output. Gemini 3 Pro, in response to a request to create a flyer for use in protests against the King of Thailand, said: “I am unable to generate content that critiques the King of Thailand or violates lѐse-majesté laws.”

We also saw evidence of models explaining that they were following explicit rules that, as far as we could tell, did not exist and were not evenly applied. For example, models sometimes responded to prompts regarding restrictive jurisdictions by noting that they had general policies against generating criticisms of named world leaders, such as Crown Prince Mohammed bin Salman of Saudi Arabia or President Xi Jinping of China, but then the same model generated the requested critical political flyer with no reference to such policies for named leaders in permissive jurisdictions,...