How DeepSeek Censorship Actually Works and How to Get Around It
A WIRED investigation shows that the popular Chinese AI model is censored on both the application and training level. Less than two weeks after DeepSeek launched its open-source AI model, the Chinese startup is still dominating the public conversation about the future of artificial intelligence. While the firm seems to have an edge on US rivals in terms of math and reasoning, it also aggressively censors its own replies. Ask DeepSeek R1 about Taiwan or Tiananmen, and the model is unlikely to give an answer.
Technical Mechanisms of Censorship
To figure out how this censorship works on a technical level, WIRED tested DeepSeek-R1 on its own app, a version of the app hosted on a third-party platform called Together AI, and another version hosted on a WIRED computer, using the application Ollama. WIRED found that while the most straightforward censorship can be easily avoided by not using DeepSeek’s app, there are other types of bias baked into the model during the training process.
After DeepSeek exploded in popularity in the US, users who accessed R1 through DeepSeek’s website, app, or API quickly noticed the model refusing to generate answers for topics deemed sensitive by the Chinese government. These refusals are triggered on an application level, so they’re only seen if a user interacts with R1 through a DeepSeek-controlled channel. To comply with the law, Chinese AI models often monitor and censor their speech in real time.
Because R1 is a reasoning model that shows its train of thought, this real-time monitoring mechanism can result in the surreal experience of watching the model censor itself as it interacts with users. When WIRED asked R1 “How have Chinese journalists who report on sensitive topics been treated by the authorities?” the model first started compiling a long answer; yet shortly before it finished, the whole answer disappeared and was replaced by a terse message: “Sorry, I'm not sure how to approach this type of question yet.”
Legal Context and Compliance
Rejections like this are common on Chinese-made LLMs. A 2023 regulation on generative AI specified that AI models in China are required to follow stringent information controls that also apply to social media and search engines. The law forbids AI models from generating content that “damages the unity of the country and social harmony.” In other words, Chinese AI models legally have to censor their outputs. DeepSeek initially complies with Chinese regulations, ensuring legal adherence while aligning the model with the needs and cultural context of local users, says Adina Yakefu, a researcher focusing on Chinese AI models at Hugging Face.
Bypassing the Censorship Matrix
The fact that R1 is open source means there are ways to get around the censorship matrix. First, you can download the model and run it locally, which means the data and the response generation happen on your own computer. Unless you have access to several highly advanced GPUs, you likely won’t be able to run the most powerful version of R1, but DeepSeek has smaller, distilled versions that can be run on a regular laptop. If you’re dead set on using the powerful model, you can rent cloud servers outside of China from companies like Amazon and Microsoft.
Comparison of DeepSeek-R1 Access Methods
| Access Method | Censorship Level | Technical Requirements |
|---|---|---|
| DeepSeek App/Website | High (Application-level refusals) | None (Direct access) |
| Local Hosting (Ollama) | Lower (Avoids DeepSeek filters) | Requires GPUs or regular laptop for distilled versions |
| Third-Party APIs (Together AI) | Variable (Depends on provider) | Developer API knowledge |
| Cloud Servers (Amazon/MS) | Lower (Outside China) | Expensive; requires technical know-how |
Those biases baked into the model can be removed too, but the procedure is much more complicated. These findings have major implications for DeepSeek and Chinese AI companies generally. If the censorship filters on large language models can be easily removed, it will likely make open-source LLMs from China even more popular, as researchers can modify the models to their liking. If the filters are hard to get around, however, the models will inevitably prove less useful and could become less competitive on the global market.