A brief review of China's AI industry and regulations

post by Elliot Mckernon (elliot) · 2024-03-14T12:19:00.775Z · LW · GW · 0 comments

Contents

  China’s AI Industry
    Scope and motivation
  Interim Measures for the Management of Generative AI Services, 2023
        Terminology
      Chapter I: General Provisions
      Chapter 2: Development and Governance of Technology
      Chapter 3: Service Specifications
      Chapter IV: Oversight Inspections and Legal Responsibility
      Commentary
  Provisions on the Administration of Deep Synthesis Internet Information Services, 2022
        Terminology 
      Chapter 1: General Provisions
      Chapter II: Ordinary Provisions
      Chapter III: Data and Technical Management Specifications
      Chapter IV: Oversight Inspections and Legal Responsibility
      Chapter V: Supplementary Provisions
      Commentary
  Provisions on the Management of Algorithmic Recommendations in Internet Information Services, 2021
        Terminology
      Chapter I: General Provisions
      Chapter II: Regulation of Information Services
      Chapter III: User Rights Protection
      Chapter IV: Supervision and Management
      Chapter V: Legal Liability
      Chapter VI: Supplementary provisions
      Commentary
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China has enacted three sets of AI regulations since 2021. I haven’t seen a concise breakdown of their content in one place, and I’ve been researching the legislation for a governance project at Convergence Analysis, so here is my concise summary of what I found. I’ll close each section by quoting some expert opinions on the legislation.

I’ll focus on what is being regulated rather than by which government agency, and I’ll omit what I consider “fluff”, such as the highlighted article 1 here. Also, note that I’m relying on other peoples’ translations and haven’t checked their quality. I’ve drawn from multiple translations for each point, but I wouldn’t rely on my precise parsing of the prose. 

China’s AI Industry

The AI industry in China is huge and growing rapidly, with a forecasted market size of $38.89 billion in 2024 (37% the size of the US’s forecasted market). China’s 2017 AI development plan states that AI has become a “focus of international competition”, and the 13th Five-Year Plan announced the goal for China to be a global AI leader by 2030. According to Stanford University’s DigiChina, a central concept in Xi Jinping’s leadership is “indigenous innovation” (自主创新), “building on a long-standing tradition of emphasizing self-reliance in industry and technology”.

Chinese AI research output is on par with US research output in share of top global publications and citations, according to a 2022 comparison by CSET. The 2023 AI Index Report found that 78% of Chinese citizens agreed that products & services using AI have more benefits than drawbacks - the highest proportion of surveyed countries, and more than double American citizens' 35% agreement. 

Court rulings on AI and copyright are also different in China. In the US and the EU, material generated by AI can’t be copyrighted, but a Beijing court recently ruled that AI-generated content is copyrightable (note that some argue that precedent is less binding in the Chinese legal system, while others still expect this decision to have a huge impact).

Chinese researchers have developed several notable LLMs, such as Beijing Academy of Artificial Intelligence’s Wu Dao 2.0 in 2021 and Huawei Technologies’ PanGu-Σ in 2023. Wu Dao 2.0 has been called “world's largest model”, with some opining “Wu Dao 2.0 is 10x larger than GPT-3. Imagine what it can do” (see also Forbes and Politico). However, while Wu Dao 2.0 and PanGu-Σ did have more parameters than their concurrent occidental counterparts, that doesn’t mean they’re more powerful. Wu Dao 2.0 and PanGu-Σ use a different architecture called mixture of experts, in which different groups of parameters are used for different inputs. 

MoE models are sparsely activated; only a fraction of the whole is active at once. Such models are computationally inexpensive relative to their number of parameters, but can be outperformed by smaller dense models. Ultimately, we don’t know how Wu Dao 2.0’s performance compares to others as its developers haven’t publicly released the model or whitepapers on its training & performance. Some claim that Wu Dao 2.0 beats GPT-3 on important benchmarks, while others argue that developers in China developers won’t be able to build competitive models for some time: 

China has neither the resources nor any interest in competing with the US in developing artificial general intelligence (AGI) primarily via scaling Large Language Models -  The AGI Race Between the US and China Doesn’t Exist [LW · GW] by Eva_B

China could be less important than you'd otherwise think. We should still regard them as a key player in the transformative AI landscape nonetheless. - Is China overhyped as an AI superpower? by Julian

The US has responded to China’s growing AI industry in 2022 by imposing strict controls on exports of certain computer chips necessary for advanced AI, as well as the materials and methods necessary to manufacture their own chips. For more on this, check out CSIS’s report or, for a deep dive on the effects of chip embargos, Deric Cheng’s upcoming evaluation of AI chip registration policies.

Scope and motivation

The Chinese government has numerous policies that are relevant to AI governance, but I’m only going to summarize the following three:

These are the three pieces of legislation we’re consulting for our upcoming report on the state of global AI governance. In particular, these three “contain the most targeted and impactful regulations to date, creating concrete requirements for how algorithms and AI are built and deployed in China” according to Matt Sheehan, author of a much deeper analysis of Chinese AI governance, which I’ll quote throughout this post such as now: 

[these regulations] share three structural similarities: the choice of algorithms as a point of entry; the building of regulatory tools and bureaucratic know-how; and the vertical and iterative approach that is laying the groundwork for a capstone AI law…Vertical regulations target a specific application or manifestation of a technology, [contrasting] horizontal regulations, such as the European Union’s AI Act, that are comprehensive umbrella laws attempting to cover all applications of a given technology.

Summarizing Sheehan’s analysis of the motivation behind these regulations, they serve 3 primary and 1 auxiliary functions:

Interim Measures for the Management of Generative AI Services, 2023

Sources: Pillsbury law, Carnegie Endowment for International Peace, China Law Translate, DigiChina’s Translation, CASI Translation

Summary: Generative AI is to be both supported and regulated. GAI must adhere to core socialist values, respect protected characteristics, and adhere to IP & consent laws. Developers are responsible for violations, and must label GAI output in line with 2022 regulation. The government will support industry innovation and, on the international front, carry out fair exchange and participate in global AI regulation. 

Terminology

Note that “public opinion properties” and “social mobilization capabilities” will both come up in the 2022 and 2021 legislation below, but I’ll only define them here. 

Chapter I: General Provisions

Chapter 2: Development and Governance of Technology

Chapter 3: Service Specifications

Chapter IV: Oversight Inspections and Legal Responsibility

Commentary

Jenny Shang, Chunbin Xu, and Wenjun Cai at Pillsbury Law point out that the final version of the regulations are significantly lighter than early drafts, citing that: 

...the new requirements:

  • Relax the requirements on data training by replacing the previous requirement on Providers of “ensuring the authenticity and accuracy of data” with “taking effective measures to improve transparency, authenticity and accuracy of data;”
  • Replace the requirements on the Providers of “taking measures to prevent the generation of false information” with requirements of “taking effective measures to enhance the transparency of Generative AI Services and improve the accuracy and reliability of generated content;”
  • Eliminate the three-month timeline for Providers to improve data models after detecting violations of any laws or regulations, thereby granting Providers more freedom to enhance their AI models; and
  • Remove the requirement for real identity verification.”

Matt Sheehan at Carnegie, mentioned earlier, writes:

By rolling out a series of more targeted AI regulations, Chinese regulators are steadily building up their bureaucratic know-how and regulatory capacity. Reusable regulatory tools like the algorithm registry can act as regulatory scaffolding that can ease the construction of each successive regulation, a particularly useful step as China prepares to draft a national AI law in the years ahead. [...]

The specific requirements and restrictions […] will reshape how the technology is built and deployed in the country, and their effects will not stop at its borders. They will ripple out internationally as the default settings for Chinese technology exports. They will influence everything from the content controls on language models in Indonesia to the safety features of autonomous vehicles in Europe. China is the largest producer of AI research in the world, and its regulations will drive new research as companies seek out techniques to meet regulatory demands. As U.S.- and Chinese-engineered AI systems increasingly play off one another in financial markets and international airspace, understanding the regulatory constraints and fail-safe mechanisms that shape their behavior will be critical to global stability.

Matt O’Shaugnessy, also at Carnegie, writes:

Parts of the draft regulation would make real progress in shielding millions of people from potential harms of AI, if enforced in a uniform and meaningful way…these requirements bring to mind principles that are often promoted as supporting democratic values.

At the same time, the draft demonstrates an obvious intention to strengthen government control over China’s technology and information ecosystems […] The draft’s vague language would give regulators substantial leverage to impose their will on tech companies. Requirements are focused on the private-sector actors developing and deploying generative AI systems; absent is any description of government AI use.

Qiheng Chen, on the topic of open-source models, writes:

a notable gap in China’s generative AI regulations is the lack of specific guidance for open-source providers, which leads to ambiguity. The Interim Measures do not distinguish between open-source and API model providers. Imposing the same responsibilities on open-source and API providers could inadvertently hamper innovation.

Provisions on the Administration of Deep Synthesis Internet Information Services, 2022

Sources: China Law Translate, DigiChina, Allenovery, China Briefing

Summary: Deepfakes and similar synthetic imagery, text, video, audio etc must respect social norms, and must not be used to harm the nation’s image or security interests; to spread false information; or to recreate someone’s image without consent. Synthetic output must be watermarked, and in many cases, conspicuously labeled. 

Terminology 

Chapter 1: General Provisions

Chapter II: Ordinary Provisions

Chapter III: Data and Technical Management Specifications

Chapter IV: Oversight Inspections and Legal Responsibility


Chapter V: Supplementary Provisions

Commentary

Matt Sheehan at Carnegie, quoted above also, provides some useful context for this legislation:

The deep synthesis regulation was years in the making, but in the end it suffered from particularly poor timing. It was finalized on November 25, 2022, just five days before the release of ChatGPT. 

[...] During the policy incubation process, the technology company Tencent managed to introduce and popularize the term “deep synthesis” to describe synthetic generation of content, replacing the politically radioactive “deepfakes” with a more innocuous-sounding technical term.

Paol Triolo, technology policy lead at Albright Stonebridge, told CNBC: 

Chinese authorities are clearly eager to crack down on the ability of anti-regime elements to use deepfakes of senior leaders, including Xi Jinping, to spread anti-regime statement. But the rules also illustrate that Chinese authorities are attempting to tackle tough online content issues in ways few other countries are doing, seeking to get ahead of the curve as new technologies such as AI-generated content start to proliferate online.

Kendra Schaefer, partner at Trivium China, writes:

China is able to institute these rules because it already has systems in place to control the transmission of content in online spaces, and regulatory bodies in place that enforce these rules. So, these rules underscore the policy problem of our age: How can Western democracies fight a war against disinformation and prevent the erosion of trust and truth online, but without resorting to censorship?

I’ll also note that I struggled to find any information on how these laws have been applied since coming into effect in early 2023. There seems to have been at least one case where a face-swapping app was court-ordered to issue an apology and compensate individuals who’d been wronged. 

Provisions on the Management of Algorithmic Recommendations in Internet Information Services, 2021

Sources: China Law Translate, DigiChina, Finnegan, Carnegie 

Summary: Algorithms used to recommend content (e.g. a news feed in an app) must protect the rights of minors, the elderly, and workers.They must not spread false information, abuse their power, or disrupt economic or social order. Under some conditions, such algorithms must be registered with the government. 

Terminology

Chapter I: General Provisions

Chapter II: Regulation of Information Services

Chapter III: User Rights Protection

Chapter IV: Supervision and Management

Chapter V: Legal Liability

Chapter VI: Supplementary provisions

Commentary

Matt Sheehan at Carnegie, quoted above also, writes:

The term ‘algorithmic recommendation’ [...] first emerged during a 2017 CCP backlash against ByteDance’s news and media apps, in which user feeds were dictated by algorithms. The party viewed this as threatening its ability to set the agenda of public discourse and began looking for ways to rein in algorithms used for information dissemination […]

As policy discussions on recommendation algorithms took shape, new concerns emerged that caused authorities to add provisions addressing them. Prominent among these was public outcry over the role algorithms play in creating exploitative and dangerous work conditions for delivery workers. [...]

the recommendation algorithm regulation created an important new tool for regulators: the algorithm registry (算法备案系统, literally “algorithm filing system”). The registry is an online database of algorithms that have “public opinion properties or . . . social mobilization capabilities.” Developers of these algorithms are required to submit information on how their algorithms are trained and deployed, including which datasets the algorithm is trained on. They are also required to complete an “algorithm security self-assessment report” (算法安全自评估报告. Here, “security,” 安全，can also be translated as “safety”). ” 

Lionel Lavenue, Joseph Myles, and Andrew Schneider at Finnegan write about international law implications of this legislation compared to previous legislation (the DSL & PIPL):

the regulations may allow Chinese litigants to refuse or delay discovery. For example, in [a 2021 court case], Chinese-based defendant ZHP invoked the DSL and the PIPL to avoid producing documents, arguing that the documents at issue were “state secrets.” In a published opinion on the issue, Judge Robert B. Kugler held that the PIPL and DSL did not shield discovery, and he warned that Chinese defendants must “know from the outset they risk serious consequences if and when they fail to obey a U.S. court’s order to compel discovery [...] the IISARM regulations add another layer of bureaucracy. Thus, if litigants want to obtain information for discovery from China, they are likely to run into new administrative slowdowns.

Steven Rolf, author of China's Regulations on Algorithms, compares these regulations with the draft EU AI Act (note that the draft EU act has since undergone significant redrafting):

The major distinguishing feature of [the EU AI Act] is its emphasis on upholding fundamental individual rights – such as privacy, ethical decision-making and data security – against (principally US-based) tech firms [...] From the perspective of individuals, then, Europe’s regulatory drive is preferable to that of China’s – which places little emphasis on privacy or fundamental rights. But it does little to tackle issues beyond individual concerns. As one report argues, recommendation algorithms ‘may cause societal-level harms, even when they cause only negligible harms to individuals’ (by, for instance, tipping the balance in an election by discouraging wavering voters from turning out) [...] Even in an age of growing algorithmic regulation, then, China’s ‘social’ model contrasts with the emerging ‘individualist’ European regulatory model. China’s emergent regulatory system targets areas hardly touched by Europe’s flagship regulations

For more information on other Chinese legislation that may relate to AI, check out Making Sense of China’s AI Regulations by Ashyana-Jasmine Kachra at Holistic AI, which also features concise summaries of China’s AI industry and legislation with lovely visuals, and which was published only after I’d written the majority of this post, alas. 

Thank you to Deric Cheng for his encouragement, and Deric and Justin Bullock for their feedback on this post. 

If you’re interested in global AI legislation, over the next few months we’ll be publishing deep dives into topics like AI chip registration policies, and a series of posts analyzing EU, Chinese, and US AI legislation on specific topics such as model registries and risk assessments. You can find the first post here: AI Regulatory Landscape Review: Incident Reporting [LW · GW]. Ultimately, this research will culminate in a State of the AI Regulatory Landscape in 2024 report later this year. If you’d like to get updates on this work, check out Convergence Analysis and sign up to our newsletter! 

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