The Global AI Regulation Race: EU, US and China

The stronger AI gets, the more urgent regulation becomes. Major global economies are answering "how to regulate AI" in different ways.

EU: Risk-based Classification at Core
The AI Act classifies AI systems into four risk tiers: unacceptable, high, limited, and minimal. High-risk systems (hiring, medical, credit scenarios) must meet mandatory requirements on data governance, human oversight, and transparency. Biggest impact on companies going overseas.

US: Industry Self-regulation + Executive Orders
The US leans toward "light touch": industry self-regulation, corporate commitments (like safety testing pledges), and executive orders — avoiding one-size-fits-all legislation to protect innovation.

China: Generative AI-specific Regulation
China has issued the Interim Measures for the Management of Generative AI Services. Core requirements: algorithm filing, content labeling, truthfulness obligations, and minor protection. Deep-synthesis content must be clearly labeled. For enterprises, "filing + labeling + content safety" is the baseline.

General Compliance Advice for Enterprises:
1. Build an AI asset inventory: Which models are used? What data is processed?
2. Content labeling and traceability: Tag AI-generated content, retain generation logs
3. Data compliance: Legal training data sources, desensitized user data
4. Safety assessment: Assess high-risk scenarios before launch
5. Watch local rules: Comply separately for target overseas markets

Regulation isn't a "straightjacket" — it's the necessary path for AI to go from "lab toy" to "trusted infrastructure."