What is DeepSeek's stance on AI regulation?

DeepSeek supports AI regulation that balances innovation with accountability, focusing on clear technical standards and developer-focused guidelines. The company recognizes that well-designed rules can prevent harm without stifling progress. Their stance emphasizes creating frameworks that address concrete risks—like data privacy breaches or biased decision-making—while giving developers flexibility to experiment. For example, they advocate for mandatory testing protocols for high-stakes applications (e.g., medical diagnostics tools) but oppose blanket restrictions on open-source AI models used in non-critical contexts.

A key part of their approach involves advocating for transparency requirements that align with developer workflows. DeepSeek has publicly endorsed documentation standards like model cards and datasheets, which force teams to explicitly document training data sources, accuracy metrics, and failure modes. They’ve open-sourced tools to automate parts of this process, such as their Model Audit Toolkit that generates compliance reports from training logs. However, they argue against regulations requiring full disclosure of model architectures, noting this could expose security vulnerabilities. Instead, they propose tiered disclosure rules based on an AI system’s potential impact—a chatbot for customer support would have lighter requirements than an autonomous vehicle control system.

For developers, DeepSeek emphasizes practical compliance strategies. They’ve built API features like real-time bias detection during model training and version-controlled dataset tracking, which help teams meet emerging EU AI Act requirements. Their engineering blog provides concrete examples, like modifying a recommendation algorithm to log demographic parity metrics without rewriting entire pipelines. While critical of vague “ethical AI” mandates, they actively participate in shaping technical standards through groups like the MLCommons AI Safety Working Group, focusing on measurable benchmarks for issues like model robustness. This combination of developer tools and targeted policy engagement reflects their view that effective regulation starts with implementable technical specifications rather than abstract principles.