The buzz around AI has been building and with it, the calls for regulation. As concerns over algorithmic bias, data security and job displacement mount, AI governance has moved from being a technical matter to the forefront of global rule-making. 

In an interview with Beijing Review reporter Peng Jiawei on March 8, Zhang Feng, a member of the 14th National Committee of the Chinese People's Political Consultative Conference, China's top political advisory body, and a researcher at the Institutes of Science and Development, the Chinese Academy of Sciences, shared her insights on the evolving landscape of global AI governance and how China can help bridge the widening divides that have opened up in the field. Edited excerpts of the interview follow: 

Beijing Review: What does the current global landscape of AI governance look like? 

Zhang Feng: Efforts to regulate AI are gathering pace worldwide, yet a shared rulebook is yet to be established. Instead, global AI governance is becoming increasingly multipolar, with three distinct approaches taking shape—those of China, the United States and the European Union.

The EU has adopted a risk-based approach to AI regulation. With the establishment of the European AI Office and the implementation of the Artificial Intelligence Act, it has built a governance framework centered on oversight, detailed rules and standard-setting. By leveraging the scale of its single market, the EU hopes that its mandatory compliance requirements will translate into international benchmarks. (The EU AI Act is the first comprehensive regulation of AI introduced by a major regulator. It classifies AI systems into four distinct risk levels, each triggering different regulatory requirements for AI deployment in the EU market—Ed.)

The U.S., by contrast, has leaned toward a more relaxed approach that prioritizes innovation. By removing regulations and cumbersome administrative processes that hinder AI development, the Donald Trump administration has gradually set up a system that focuses on deregulating AI, building AI infrastructure and keeping the country ahead in the AI race. The aim is to unlock the innovative capacity of the private sector by loosening federal oversight.

China has charted a third path, one that aims to balance development with security, and innovation with governance. In recent years, it has launched initiatives such as the Global AI Governance Initiative and the Global AI Governance Action Plan, calling for shaping a digital future that is inclusive, open, sustainable, fair, safe and reliable.

Looking ahead to the next few years, global AI governance is set to accelerate its move toward institutionalization, as more countries incorporate AI governance into their national strategies. The EU AI Act has already entered into force, while the Republic of Korea's Framework Act on the Development of Artificial Intelligence and Establishment of Trust—the country's overarching AI legislation—took effect this January.

China is also moving to strengthen its governance system. The outline of the 15th Five-Year Plan (2026-30), which was approved by national lawmakers at the recently concluded session of the National People's Congress (the country's top legislature—Ed.), calls for improving the legal framework, policy mechanisms, application standards and ethical guidelines governing the technology.

Meanwhile, global AI governance is also expanding to cover the entire life cycle of AI—from research and development, data governance and model training to deployment and evaluation. This will ensure that AI risks are managed in a coordinated and continuous way.

How does China's approach to AI governance differ from that of the EU and the U.S. at a deeper level? 

China sees itself as an active participant in, builder of and contributor to global AI governance. At a time when the global AI landscape is increasingly shaped by competing partnerships, China has chosen neither to simply follow the EU's regulatory model nor to adopt the U.S. approach, which treats AI governance as the preserve of a small group of countries.

Instead, China champions the idea that technology should serve the public good and contribute to a community with a shared future for humanity. Framing AI governance within the broader context of global public interests, it advocates for greater alignment of rules, mutual recognition of standards and closer coordination in governance while respecting differences in how each country approaches AI.

The ultimate goal is to move global AI governance beyond a zero-sum game toward a cooperative framework that can deliver systematic solutions for bridging the AI governance divide.

What role can China play in addressing the divide in AI governance? 

Many developing countries are still relatively weak when it comes to computing infrastructure, data resources, tech talent and AI management capacities. If left unaddressed, this gap could deepen the global digital divide and undermine the foundations of multilateral cooperation on AI governance.

Against this backdrop, China can leverage initiatives such as the Digital Silk Road to strengthen collaboration with other developing countries on digital infrastructure. This includes supporting the local construction of data centers, computing networks and smart platforms, which can significantly strengthen the host countries' digital capabilities. (The Digital Silk Road is the digital infrastructure arm of China's Belt and Road Initiative, which was launched in 2013 to enhance connectivity along and beyond the ancient Silk Road routes—Ed.)

The country can also establish dedicated funds to finance initiatives that provide partner countries with specialized AI models and the necessary computing resources to operate them. When applied to fields such as precision agriculture and infectious disease monitoring, these tools can effectively address local needs and improve community wellbeing.

Last but not least, it needs to support other developing countries in growing long-term, self-sustaining AI capacities through collaborative research projects, training programs, talent exchange and open-source collaborations.

Through multi-level engagement, China can help other countries narrow the digital divide while contributing to a more inclusive and equitable global AI ecosystem. As these partnerships deepen in practice, they will also foster broader recognition and understanding of China's solutions to AI governance.

The divide among countries aside, we also see a widening gap between the rapid pace of AI adoption and the ability of regulatory institutions and frameworks to keep up—or the AI governance gap. How can AI governance become more forward-looking and adaptive in the years ahead? 

From large language models to multimodal AI, and now to embodied intelligence and AI agents, the technology is evolving at breakneck speed, continually expanding both its technical forms and real-world applications. In such a fast-moving environment, governance models that rely primarily on ex-post regulation and static rules often struggle to keep pace with rapid AI development.

Making governance more forward-looking and adaptive requires action in three key areas.

First, AI governance needs to shift its focus from reacting to problems after they arise to forecasting and mitigating potential hazards. That means keeping a close watch on emerging technologies and studying the risks they may bring, so that policymakers can better anticipate the security, ethical and societal implications of new developments.

Second, the use of mechanisms such as regulatory sandboxes, pilot programs and dynamic risk assessment can allow innovation to be tested within controlled settings and regulatory rules to be adjusted accordingly. The adoption of these measures will allow governance to evolve alongside technological progress. (A regulatory sandbox is a controlled environment that is established to understand the opportunities and risks associated with specific innovations and to develop an appropriate regulatory environment to accommodate them effectively—Ed.)

Finally, strengthening AI governance requires the joint efforts of all stakeholders. With stronger industry self-regulation and clearer technical standards, a more coordinated model of AI governance can take shape—one in which government oversight, corporate responsibility and public participation all have a role to play. BR

Copyedited by Elsbeth van Paridon 

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