China has been signalling its intention to develop AI since 2013, but its efforts began in earnest after the “Sputnik moment” in 2016, when AlphaGo (a Google DeepMind AI program) defeated Go champion Lee Sedol (Roberts et al.
2019). In 2017, the State Council released the “A New Generation AI Development Plan” (AIDP). Shortly after, the Ministry of Information and Technology, one of the bodies tasked with implementing the plan, issued the “Three-Year Action Plan for Promoting Development of a New Generation Artificial Intelligence Industry” (Action Plan). The AIDP functions as a “wish list” of hundreds of applications but does not provide detailed implementation, while the Action Plan outlines specific progress needed in certain sectors and sets out measures to strengthen development and implementation efforts. As Sheehan (
2018) writes, “The hope is that if local officials cough up a sufficient number of these gifts… they will eventually add up to the plan’s headline goal: global leadership in AI”, although this goal may not be sole leadership as the US seems to desire for itself. If enough of these are in basic research, it may address the concerns Tse and Wang (
2017) raise about incentives for rapid results encouraging “new applications of pre-existing technology” instead of fundamental research. However, our quantitative analysis shows that both national and local plans still prioritise applications over basic research, despite the concerns Tse and Wang (
2017) raise about this decreasing the likelihood of AI breakthroughs emerging in China. Table
3 in the appendix shows the four national documents and Table
4 the 28 province, autonomous region, and city-level local documents we analysed. In addition, Table
5 provides a list of important Chinese terms and their English translations.
According to the AIDP, global leadership in AI development China’s primary goal. The AIDP lays out milestones for 2020 (enter the “first echelon” of international AI competitors),
6 2025 (achieve major breakthroughs and establish regulations), and 2030 (achieve “world-leading” AI and become “the world’s primary AI innovation centre”) (State Council,
2017a). What this means for the future of global AI competition is unclear, as becoming
a world leader would require it to meet or exceed the US in AI prowess, while achieving the latter goal of becoming the “primary” innovation centre would require it to take the lead in AI innovation from the US. To accomplish these goals, China is using a combination of central government, local government, and private-sector initiatives. These often-intertwined initiatives attempt to preserve social stability while encouraging innovation and technical progress, but raise questions about who the beneficiaries of AI are.
4.1 National development initiatives
China’s national policy documents reflect a drive for global leadership to be achieved by a harmonious balance of social control and innovation. However, national funding initiatives may not be keeping pace with requirements and are concentrated in prosperous areas, calling its goals into question.
While different administrations have distinct definitions for AI plans in the US, China has not had any regime transitions as it has worked to develop AI. The primary divisions are between national and local efforts. China’s AI development plans operate through a structure called “fragmented authoritarianism”, where the central government outlines overarching goals and delegates implementation to local governments while sharing power among central agencies (Lieberthal
1992; Zeng
2020). Economic-performance-based incentives motivate local politicians to compete for the best implementation in their area (Roberts et al.
2019). This is often thought of as an exclusively top-down approach, but it is, in fact, a combination of top-down guidance and bottom-up initiatives (Ding
2018; Zeng
2021). This creates regional competition that allows for successful initiatives to be promoted to national levels, but can also create coordination problems (Zeng
2021).
After the expiration of the seminal 2017 Action Plan in 2020, no subsequent plan was issued. Instead, AI seems to have been wrapped into China’s more extensive science and technology (S&T) goals in the 2021 “Fourteenth Five-Year Plan for the National Economic and Social Development of the People’s Republic of China and the Outline of the Long-Term Goals for 2035” (Five-Year Plan). The government’s decision to wrap AI back into its larger technology plans implies a return to its pre-2016 view of AI as “one technology among many” (Roberts et al.
2019), albeit with more emphasis on its importance and still guided by the AIDP.
These documents outline significant goals to be achieved through a harmonious balance of social control and innovation. The “Basic Principles” undergirding the AIDP are “technology-led”, “systems layout”, “market-dominant”, and “open-source and open” (State Council
2017a). The “market-dominant” principle is quite different from the US version of the same, emphasising the need to “better take advantage of government planning and guidance,… market regulation,… etc.” rather than adopt a free-market approach (State Council
2017a). The “technology-led” principle includes the goal of “disruptive breakthroughs”
7 (State Council
2017a), which is a sentiment worth interrogating. One of China’s goals in its technology development is to promote social stability, which is mentioned in both the AIDP and the Five-Year Plan (State Council
2017b; Xinhua News Agency,
2021a). Although the term “disruptive” refers to the technological implications rather than the political implications, disruptive “breakthrough” technologies often go hand-in-hand with social and political disruptions. This is demonstrated by the first three industrial revolutions, when technological innovations in steam power, electricity, and digitisation, respectively, caused massive social and political change (Schwab
2018). When AI is hailed as a key part of the “Fourth Industrial Revolution” (Schwab
2018), it seems that ensuring stability is seemingly incompatible with the disruption inherent to AI development. The “Artificial Intelligence Standardisation White Paper” notes that China must continue to innovate and drive AI development, but that since “the application boundary of innovative technology is difficult to control, it may trigger risk of abuse” (China Electronics Standardization Institute
2020). The Five-Year Plan cautions that the state needs to “preserve social stability and security” during development, which does not seem to correspond with the Silicon Valley innovation ethos of “move fast and break things” (Business Insider
2009). Against this backdrop, the plans emphasise the need for an “innovation-driven” strategy (Xinhua News Agency
2021b), backed by our quantitative analysis that illuminates the intense focus on “innovation”. The AIDP describes AI also as a tool for social control in pursuit of the “great rejuvenation of the Chinese nation” (State Council
2017a), indicating that the CCP sees AI as both a threat and an opportunity for social stability.
This particular balance is clarified in the drive for harmony endorsed in Chinese AI ethical principles. Different government institutions have approved three sets of AI ethics principles (Roberts et al.
2021a). Two of them feature the modern word for “harmony” (和谐,
hexie). These principle-sets seem to take a global view of human flourishing, but are inherently contradictory. 和 is the character for “harmony” found in Confucian texts and is formed of the radicals for “grain” and “mouth”, displaying its origins in an agricultural society. The character 谐 includes the radicals for “words/speech” and “all/every/everyone”, implying the need for accord in expressed opinions to achieve harmony. In the “Beijing AI Principles”, 和谐 is included in the principle of “harmony and cooperation” (和谐与合作,
hexie yu hezuo), with 合作 implying a sense of collaboration (literally “together work”) (Beijing Academy of Artificial Intelligence
2019b). The principle states that governance cooperation should occur at levels from academic to international (Beijing Academy of Artificial Intelligence
2019a). In the “Governance Principles for a New Generation of Artificial Intelligence”, 和谐 is paired with 友好 (Ministry of Science and Technology
2019), translated as “harmony and friendliness” (和谐友好,
hexie youhao) (MIIT
2018). It states that AI should be “based on the premise of safeguarding societal security and respecting human rights, avoid misuse, and prohibit abuse and malicious [applications]” (MIIT
2018). Thus, a harmonious balance must be struck between social stability and innovative development. “Harmony and friendliness” is repeated in the September 2021 “New Generation Artificial Intelligence Specifications”, which adds more specificity to principles in the previous documents, though the management, R&D, supply, and use specifications are still quite broad (Ministry of Science and Technology
2021). Still, this shows an active commitment to developing a national code of ethics that allows the CCP to define what counts as “promoting human well-being” (Ministry of Science and Technology
2021), among other values.
When it comes to maintaining social stability, the central government is taking an active paternalistic role in regulating AI. The draft “Internet Information Service Algorithmic Recommendation Management Provisions”, released on August 17 2021, contain sweeping regulations for recommendation algorithms, including that they may not “[upset]… social order” (Article 6) or “go against public order and good customs” by encouraging addiction or “high-value consumption” (Article 8). The government will categorise recommendation services and regulate accordingly (Article 19) (Translation
2021).
Broader policy documents address the need for balance. The Five-Year Plan references the need to “within stability, seek progress” or “seek progress in stability” (稳中求进,
wen zhong qiu jin) (Xinhua News Agency
2021a).
8 This phrase originated in the 2011 Central Economic Work Conference and referred to maintaining macroeconomic policy and social stability in concert with rapid economic development (Liu
2011). Ten years on, it is being applied to technology development as well. It goes hand-in-hand with the Action Plan’s call for “double initiating” (双创,
shuang chuang) platforms of innovation and entrepreneurship (创新创业,
chuangxin chuangye) (MIIT
2017).
9 The central government is attempting to allow an acceptable level of chaos, with the explicit goal of seeking advantages in domestic social control and global geopolitical clout.
However, funding slowdowns call these goals into question. While China’s approach to AI development has been summarised as “throwing money at the problem” (Webster et al.
2017), there are indications that the tap may not be as free-flowing as assumed. As with development plans, funding of AI projects and companies can also be separated into multiple categories. Nationally, funding is provided for projects of various scales by the government through the National Natural Science Foundation of China (NSFC), comparable to America’s NSF. The NSFC focuses on basic research and “pre-commercial, scientist-led projects” (Acharya and Arnold,
2019). NSFC funding for Information Science projects (which AI falls under) decreased from 2015 to 2016, but has been increasing ever since. However, in both the General Project and Key Program Project (for larger projects) categories, while funding has increased, the project approval rate has decreased, implying that applications are becoming more competitive, and funding is not keeping pace.
Funding is heavily concentrated in “first-tier” cities, with Beijing receiving nearly as much as the third- and fourth-ranked provinces of Jiangsu and Guangdong received, combined. Second-ranked Shanghai received about 60% of what Beijing did. After those four cities and provinces, funding drops steeply, with Hubei province receiving about 34.5% of Beijing’s total (National Natural Science Foundation of China
2019). Thus, the beneficiaries of AI development seem to be already-established research hubs which may be problematic given that provincial authorities are largely responsible for advancing the central government’s goals. Although these “first-tier” cities and provinces contribute heavily to central government revenue through taxes (Textor
2022), this allocation of funds merely entrenches the development gap between regions.
4.2 Local development initiatives
China’s local documents outline lofty goals. Provinces rich and poor aim to use AI development to benefit their local economies, with some success. However, funding concentration in wealthy provinces and headwinds in talent attraction may impede these—and thus national—goals.
In the “federated authoritarianism” model, provincial- and city-level governments are responsible for interpreting and implementing central government plans. Common themes in many local plans include establishing target values for the AI industry, establishing “open innovation platforms”, founding technology parks, cultivating AI companies and talent, encouraging collaborative development, and strengthening research and applications in specific sectors. This is supported by our word frequency and tf-idf analysis, which shows that application scenarios are prioritised above basic research, despite the dangers to this approach outlined by Tse and Wang (
2017).
It is unclear to what extent local plans are genuine development efforts versus paying lip service to the CCP’s goals. As outlined in our analysis of similar text chunks, many contain boilerplate language, especially regarding guiding ideology. Furthermore, the outlined goals are often lofty. Zeng (
2021) describes the regional targets as “grossly inflated” as they sum to more than double the national industry value target of 150 billion RMB by 2020, itself a high target considering that in 2019, the core industry was estimated at 57 billion RMB. It is also difficult to ascertain how many goals are being met, as there are limited follow-up reports. Hunan, which set a target value of 10 billion RMB by 2021 (Hunan Province Department of Industry and Information Technology
2019), appears to have achieved that in 2020 (Cao and Pang
2021). Guangdong, which set a goal of working with Tencent to develop medical imaging products (Guangdong Provincial Department of Science and Technology
2018), reported the release of an oesophageal cancer diagnosis tool (Yicai Global
2017), and SenseTime and Accenture agreed to construct innovation hubs in Shenzhen (Dou
2018; Han and Zha
2019).
However, economically disadvantaged province Heilongjiang may have been less successful in its 2020 goals. Heilongjiang set a goal of a 5 billion RMB AI industry by 2020 (General Office of the People’s Government of Heilongjiang Province
2018), but in 2019, the Jiusan Society (a minor political party that follows the CCP) released a proposal outlining issues faced by the province, including lack of R&D capacity, coordination difficulties, lack of infrastructure, and investment shortfalls (Heilongjiang Provincial Committee of Jiusan Society
2020), indicating that it was likely not on track to meet its goals. This accords with CSET’s reporting that funding is more difficult to access for lower-tier cities and provinces, and also with data from the Artificial Intelligence Industry Alliance (AIIA).
The AIIA was founded in October 2017 to “promote collaborative innovation in AI” (Luong and Arnold
2021). Government actors at state, provincial, and local levels form alliances with industry, providing funding, policy incentives, and supervision to promote development and local projects. While it has been suggested that these alliances may allow for less-wealthy provinces like Heilongjiang to access more investment (Luong and Arnold
2021), project data mimics NSFC allocations, with 71% in the “first-tier” cities of Beijing, Shanghai, Shenzhen, and Guangzhou (Liu
2020; Luong and Arnold
2021). This system allows the government to “pick winners” (Luong and Arnold
2021), which appear to be in economically advantaged areas. Complicating goals to attract talent and companies to settle in specific provinces is the fact that talent and companies are finite and scarce resources: there is a shortage of over 5 million AI workers in China (Zeng
2021), who may be inclined to go to provinces with more resources.
Thus, funding allocation indicates that the beneficiaries of the AI development process—independent of the results of that development—may be already-economically advantaged provinces. While it may not be inherently problematic to concentrate development in specific places (à la Silicon Valley), pressuring less-wealthy provinces into issuing lofty goals, which then require investing in development efforts in pursuit of an unlikely payoff, means that they may not have the resources to invest in projects that may be more likely to benefit the province, at a cost to the province and the nation as a whole.
4.3 Private-sector development initiatives
Private-sector work is the other key component of China’s AI development efforts, playing a significant part in AI development through state-sponsored initiatives and partnerships. While there are notable success stories, funding data questions how much these initiatives can achieve their goals.
China has appointed several “national champions” to lead the charge as “National New Generation Artificial Intelligence Open Innovation Platforms” (AIOIPs). Members of the “National AI Team” are granted increased government support, as well as preferential access to regional projects and public data. These companies are, in turn, expected to lead development, coordinate standards, and act as “open innovation platforms” to “[support] the entrepreneurship” of smaller enterprises (Ding
2018; Larsen
2019).
The emphasis on private companies leading innovation accords with the fragmented authoritarianism model but may negatively impact local areas. Concerns have been raised that a singular focus, such as Hefei’s 5 billion RMB “China Speech Valley” focused on intelligent speech, may not suit a city; diversity of capabilities could be necessary to sustain an AI ecosystem (Ding
2020). This may be a consequence of the fragmented authoritarianism model. Since cities and provinces cannot do everything outlined in the plan, they are incentivised to pick a speciality (similar to “national champion” companies), but staking an entire region’s economic development on one concept is considerably riskier.
The AIIA allows the state to play a significant role in public–private partnerships. Government officials and state-owned enterprises are overrepresented in AIIA leadership, indicating the state’s power in shaping alliance agendas. However, industry still plays a bottom-up role in determining project directions. AIIA application areas are broadly consistent with the priorities laid out in the Action Plan, but also focus heavily on AI-enabled business solutions, showing the influence of industry interests (Luong and Arnold
2021).
As with local and national funding, there are signs that AI investment in the private sector may be cooling off. Equity investment in China’s privately held AI companies has faded dramatically over the past several years. CSET estimates that the number of equity investments in private AI companies in China increased between 2015 and 2019. Total investment value nearly quintupled between 2015 and 2017, but then plummeted back to near-2015 levels over the next two years. Furthermore, they find that Chinese investors are “minor players” in international markets (Arnold et al.
2020). 2019 has been called the “capital winter” and is showing significant effects on the industry; 336 start-ups shut down in 2019 (Zeng
2021), which may complicate provincial efforts to attract and cultivate the companies on which the government relies to drive innovation and development.
China sees AI as a tool to enable it to compete with the West, but seems content to work quietly towards its goal of becoming the leading global AI power with little rhetoric of explicit competition in its policy documents. Its goal-oriented model of “fragmented authoritarianism” and enlisting of public and private actors—and willingness to rely more heavily on central guidance than free-market America—allows the central government to maintain social stability while guiding technological innovation, preserving a nebulous sense of harmony. AI seems to be moving from an elevated position to a critical tool in a more extensive technology toolbox, but funding data brings into question the feasibility of China’s ambitions.
National and private-sector data show that investment into AI may be slowing, and these effects are also being felt at local levels. While China’s fragmented authoritarianism development model seems to give opportunities to all provinces, AI funding is concentrated in highly developed areas. While some projects are successful (such as the China Speech Valley), not all provinces—especially less-wealthy ones—will be able to achieve their likely overly lofty goals considering the aforementioned funding headwinds and talent shortages.
It appears likely that the Chinese and US governments are spending on a similar scale when it comes to non-defence AI R&D spending and other investments (Hao
2019), and also coming to see AI as one technology among many, which may portend future competition on other emerging technologies. Particular to AI, though, is the potential for a values clash. The CCP’s emphasis on social stability means that AI is explicitly not being developed for those the CCP considers a threat, including the Uighurs of Xinjiang, who are subject to AI-supported profiling and detention (Mozur
2019). The same principle-sets that emphasise the need for “harmony” also endorse the need for AI with “human values”, but these are even less well-defined than Trump’s “American values” (Beijing Academy of Artificial Intelligence
2019a; National New Generation Artificial Intelligence Governance Expert Committee
2019). The Beijing AI Principles state that AI should serve the “overall interests of mankind”, and the AIDP principles say that AI should “serve the progress of human civilization” (Beijing Academy of Artificial Intelligence
2019a; National New Generation Artificial Intelligence Governance Expert Committee
2019), but ethnicity-based oppression does not serve humanity and must be condemned.
10 In the next section, we will undertake a philosophical analysis to establish a framework that attempts to account for these contradictions.