Here Be Dragons: Lessons from China’s AI Strategy

By Mark Robbins

January 18th, 2019

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Here Be Dragons: Lessons from China’s AI Strategy In 2017 Vladimir Putin boldly declared that “whoever leads (in artificial intelligence) will rule the world.”1Although perhaps overstated, the late 2010s have nonetheless seen a groundswell of interest in AI and increasing attention to AI by government. While standard global powers like China, the United States and Russia claim to seek global leadership in AI, Canada has a fragmented approach to carving out its own leadership niche in AI. With an emergent AI strategy providing $125 million in funding through Budget 2018 to be exercised through the Canadian Institute for Advanced Research (CIFAR). As Canada enters the AI race, it’s important to consider the policy actions being undertaken by the biggest player in the AI race: the People’s Republic of China. Certainly, the United States may still lead in AI industries by many metrics, as the old proverb goes, “what matters is not about how far you are, but the direction in which you are going.” Especially when it comes to the standpoint of policy leadership in supporting AI industries, the United States is clearly behind China in both ambitious an action. China has identified AI-industries as a national priority since at least 2015. While many elements of the Chinese approach are incompatible with the Western political systems and cultural values more generally, there are some lessons to be learned from the Chinese case which could have wider applicability for other jurisdictions. More generally, it is important to learn about the emerging Chinese leadership position in AI so that decision-makers in Canada are able to adapt and react accordingly, should a passing of the torch occur with US ceding its global leadership to China. Background - AI to the Present From a technical standpoint, the development of artificial intelligence in many ways follows the development of computing more generally, with early AI technologies having emerged in the 1950s and 1960s when computing power was become increasingly available to researchers. Following a period of steady progress into the 1970s, discovery research into AI declined sharply in the mid-1970s with a drop in public interest, and a drop in research funding shortly thereafter. AI research at this time was highly dependent on public funding due to the high costs of computing. Rising disillusionment with the disappointing results of AI research (at least relative to expectations) spurred a reallocation of resources to other field of inquiry. This so called “AI winter” lasted from the mid-1970s to roughly 2010, when changing circumstances and technological landscape brought with it reinvigorated interest in AI.

1 Meyer, 2017.

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A Note on Terminology: AI, Machine Learning and Analytics The moniker “AI” is often (mistakenly) used interchangeably to refer to a variety of concepts and terms related to advanced analytics. Broadly speaking, there are two major conceptual categories of AI: strong-AI and weak-AI, also sometimes referred to as broad or narrow AI. Strong AI refers to the fully sentient machine intelligence often portrayed in popular culture, like in the movie The Terminator or Her. Weak-AI represents decision-making models with a degree of intelligence and ability to make decisions, but only in very specific scenarios for which it was designed. Also referred to as “machine learning”, narrow AI is the only real form of AI that is currently in existence, with some doubting whether broad-AI is even conceptually possible to create. “Analytics” is often mistaken with weak-AI, and in a function of processing huge amounts of data to reach interesting, and sometimes eerily insightful conclusions, can appear similar in result to weak-AI. The major difference between analytics and weak-AI is the degree to which a human operator can be replaced in the analysis and redesign of the analytic system. In this work, the term “AI” is used to refer to these different processes interchangeably since the wider policy ecosystem tends to do the same.

In the late 1990s and early 2000s, a new phenomenon began with significant implications for AI: the digital revolution. There are a number of causes of the digital revolution but a crucial turning point occurred in the late 1990s as an increasing preference emerged for storing information in a digital format instead of an analog one. Combined with the increasing ubiquity of the internet, this had the effect of creating large and integrated datasets of information. Although the technical trends driving this began in the mid 2000a, by the 2010s, this astounding and exponentially increasing amount of data had roundly earned the moniker “Big Data”. “Big Data” would prove to be intimately related to the future of AI (See Figure 1).

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Figure 1: Total Global Data Production per year 2010-2020 (Projected)

In 1995, the year before digital storage become less expensive than analog storage, all of humanity produced roughly 1.5 exabytes of data per year. By 2018, this number was nearing 20,000 exabytes per year. To provide some context, 5 exabytes of data is sufficient to record all the words ever spoken by all human beings that ever lived. Source: IDC Digital Universe Study, 2012. Data is sometimes referred to as the oil of the 21st century because of its centrality to new productive processes like AI. The availability of previously unfathomable amounts of data needs to be sorted and analyzed to unlock its value. The effectiveness of AI is similarly improved by the increasing size of these datasets. As such, the data explosion has brought about a revived interest in AI. With the use of AI in turn creating more usable data, modern productive capacity can be viewed as a form of nested exponential curves, all driven by data. By the mid-2010s, governments worldwide started to become more keenly aware of the importance of AI, and consideration of AI began to feature more prominently in the world of policy. AI quickly developed from a small niche industry into something much larger as the market for AI applications continued to mature (See: Figure 2).

By the mid-2010s, governments worldwide started to become more keenly aware of the importance of AI, and consideration of

AI began to feature more prominently in the world of policy.

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Figure 2: Revenues from the artificial intelligence (AI) market worldwide from 2016 to 2025 (in million U.S. dollars)

Source: Statistica, 2018. A key difference between the contemporary interest in AI and past circumstances is that the current interest in AI is driven mostly by private capital seeking commercial application for AI technologies, not government and university-based research funding. This has made AI more of an everyday issue than it has been in the past. With the growing size and potential applications of the industry, so too have the implications of this growth for citizens and policy-makers start to be realized. This new “AI-summer” thus brings about new and unprecedented governance challenges. For one, Big Data and AI have largely advanced in fits and spurts that have often outpaced the social compact around things like privacy protection. This has left many citizens outraged with a growing governance gap where governments are proving unable to adequately address the challenges that come with AI. For another, the pace of change coming with AI may also very well be faster than the natural rate of creative destruction. This produces a situation where job destruction occurs more quickly than the new job creation, creating a glut in labour supply that manifests in unemployment, underemployment and precarious labour. The full implications of this structural change for the political-economy have yet to be realized, but in the immediate term it is causing significant in for policy areas related to employment and social security.2 There are also questions about AI’s contribution to new (and perhaps undesirable) types of wealth distribution, specifically the ever-

2 For more on this see Thirgood and Johal, 2016.

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increasing returns to capital and diminishing returns to labour.3 Similarly, this creates concerns about potential for AI to create an imbalance between economic and political authority. This is without mentioning citizen concerns about privacy, or the more philosophical concerns about the long-term implications of the technology for democracy and pluralism. As the pendulum swings backwards with the realization that adoption of AI comes with some socially undesirable effects, citizen demands for limiting the fullest technical potential of AI are increasingly commonplace. Many of these limitations are necessary for citizen and consumer protection, and can be viewed as an application of society’s values onto a system that was designed with few innate values of its own.4 Yet restrictions being placed on the use of AI and the data that fuels it, will also limit the technical capacity of that technology. This in turns comes with risks that imprudent policy interventions- interventions for which there is little real precedent- could prove unduly limiting and could even stifle progress. This demonstrates the clear need for careful, thoughtful and consistent policy surrounding AI. Good AI policy must balance citizen protections with targeted public investments and enabling regulations in order to ensure success in this domain, and perhaps even in the 21st century economy more broadly. AI Policy with Chinese Characteristics While several countries claim to seek global leadership in AI in one form or another, some claims are more credible than others. China in particular would seem to be the country which has taken the most serious and systematic approach to achieving global leadership in AI through state-led action. While other countries may have a higher innovative potential and more fully developed research clusters in AI, China benefits from a fortuitous alignment of state capacity with the requirements of AI industries at their current stage of technological maturity. At this stage in the technology’s development, of the benefit of AI in industry will come from successful roll-out of AI policies rather than the pure technical innovation that has driven the technology up to this point. Since AI is fed and improved by data, the availability of data and the standards which govern its use (like privacy law) is one of the key inputs for AI industries and an important determinant of competitive advantage. While many countries debate the implications of merging and integrating huge data sets, China has gone full speed ahead without giving it much trepidation. Indeed, China is emerging as a wild wild East of sorts when it comes to AI with few of the restrictions on AI being proposed in Western countries are given serious weight in China.5 China, as the world’s largest country, also contains more data and richer potential for its use than any other single 3 For more on the impact of automation see Gates, 2017 and for more on the changes in distribution of returns see Piketty, 2014 4 This is a large part of the emerging discipline often referred to as AI ethics, and indeed, there is much important work to be done in this space. 5 This point is sometimes debated, as China has released some policies and regulatory standards that would seem to propose even higher protective standards than are employed elsewhere. In the fine print however, many of these standards lack key enforcement mechanisms or tracks to implementation, making them much less stringent and mandatory than they might otherwise appear. For more see: Sacks, January 29th 2018.

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jurisdiction, and has adopted significantly less prohibitions on its use. This will help to permit the technology to reach its fullest potential in China, at least from a technical perspective. These basic fundamentals, combined with state-directed industrial and procurement policies in support of AI, ensures that China is going full-speed ahead in its pursuit of AI leadership. The Chinese government had announced as early as in 2014 that AI would feature heavily in its future planning and procurement for state security. AI has featured in a multitude of policy areas as part of China’s overarching AI strategy, but perhaps one of the first large-scale and highly notable initiatives for the application of AI was in its surveillance system. China is one of the most heavily surveilled countries on the planet with 176 million active surveillance cameras in use in 2017, a number which is expected to grow to 626 million by 2020.6 At the back-end, this creates an impossibly large and ever-growing amount of footage that must be monitored. This is the perfect type of task for AI, and the Chinese security apparatus has thus invested heavily in relevant AI technologies, such as software for facial recognition. This AI-backed monitoring software was leveraged into a comprehensive surveillance system that would integrate information about location and inter-personal associations with data about spending habits, credit, search history, resulting in a “score” reflecting one’s trustworthiness. This “Social Credit Score” was designed to address low levels of public trust by providing a measure for gauging the trustworthiness of strangers. This is a tool that could certainly prove useful in a country spotted with over 150 cities with a population above 1 million and thus, the potential for high levels of anonymity. Quite understandably, criticism has arisen of the Social Credit System for its potential applications in violating human rights and it potential use in support of authoritarianism. (See the text box below for more)

The Social Credit System: At the Cutting Edge, for Better or Worse The Social Credit system has achieved partial roll-out by 2018 and is expected to be employed across China by 2020.7 Despite the stated intention of using the Social Credit System for resolving issues of public trust, as of 2018, the Social Credit Score system has only been fully deployed in Tibet and Xinjiang- restless provinces known for bucking the authority of Beijing- where the system has been used to root-out political dissidents and clamp down on protests. The potential for the Social Credit System to be used to support authoritarianism is clear, but often distracts from the other implications and applications of such a system, which may well be of greater importance than yet another tool for dispersing dissent. Indeed, as one scholar of Chinese political affairs points out, the government of China is unapologetic in its authoritarian approach

6 Grenoble, December 12th, 2017. 7 Rollet, June 5th 2018.

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and has no lack of instruments for entrenching its control; the Social Credit Score system has emerged to serve a different purpose altogether.8 Assuming this to be true leaves room for more thoughtful consideration of the impact of such a system, or more importantly, of the guiding principles underlying this system which are common to other jurisdictions. Indeed, while the Social Credit Score system violates many Canadian norms of privacy and pluralism, creeping gamification and ranking of nearly everything, is a universal trend from which Canada is not exempt. The Social Credit System may indeed be only remarkable for having enthusiastically followed this gradual progression to its furthest extreme. This has the potential to provide policy-makers with a glimpse into the future to get out ahead of emerging trends. For instance, the Social Credit System is presently being used by individual Chinese citizens for fairly innocent, but nonetheless spooky purposes, such as vetting prospective friends or romantic partners.9 It is also used by the state for risk profiling and can prevent high-risk individuals from being able to travel. It takes no large stretch of the imagination to see that it could be used to inform what constitutes appropriate reading material, who might be hired for certain jobs, or who will be permitted in certain social establishments.

While the intentions behind the Social Credit Score system can be debated, the state security sector has indisputably been one of the earliest drivers of Chinese AI industries. There are several practical reasons for this, not the least of which being that government spending conducted under the guise of national security is exempted from World Trade Organization provisions against state subsidies of industries, thereby shielding China from countervailing trade action. This allows China to support AI industries in way that other countries cannot. For another, spending conducted for national security purposes is often excepted from quotidian legislation for safeguarding citizen rights to things like privacy, providing another boon to the technology’s development. As such, the security imperative has significantly impacted the direction of AI’s technical development in China. With the security sector as a major instrument through which competitive advantages are developed in China, some of Chinese technological leadership in AI has clear connections back to the security imperative. For instance, this has ultimately manifest in global leadership of Chinese firms in facial recognition software, a key ingredient for state surveillance programs and the social credit system. Since these early beginnings, other state portfolios have been steered towards nurturing the development of AI industries in China, and although it has been surpassed in some regards, the security sector continues to be a large player in Chinese AI industries.

8 Creemers, 2018. 9 Ma, April 29th 2018.

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By 2015, Chinese industrial policy was being marshalled towards AI as well, with China’s President Xi Jinping naming AI one of the central pillars of the “Made in China 2025” 5-year economic development plan. This plan focuses heavily on building technology-focused industries in China and on building the country’s long-term development trajectory on advances in the value-added technology sector. This was quickly followed the next year by a tripling of Chinese investment in US firms, the dramatic increase having been principally composed of increased acquisition of technology companies and intellectual property. In concert with these initiatives, the government of China has been investing heavily both in individual researchers and the Chinese research ecosystem for AI. This has included the investment of billions of dollars into the building of new institutes, campuses and research parks dedicated to research and development in AI. By 2017, Chinese backed venture capital in AI represented 48% of the global total, surpassing the share held by US venture capital.10 This suite of initiatives was also swiftly followed by the ratification of China’s Internet Cybersecurity Law in 2016 which sought to entrench cybersecurity standards, localize data in China and (critics argue) make it more difficult for foreign technologies companies to compete. Indeed, there are some clear limitations to competing on an even playing field in AI industries and in the technology sector more generally in China. Google, Twitter, and Facebook have effectively been barred from China, and Uber has withdrawn from the market voluntarily, giving space for Chinese equivalents Baidu, Weibo, WeChat and Didi to flourish and grow into companies of global significance. Chinese protections for intellectual property are also notoriously limited, and since those protections are key for foreign companies seeking to commit to R&D-intensive operations in China, again, the challenging conditions of the local environment leave room for Chinese equivalents to grow. While established players may cry foul, this strategy seems to be effective for China. In 2018, the largest corporate AI purchase in history occurred the acquisition of SenseTime, a Chinese AI company working on facial recognition, by Alibaba, another Chinese company.11 In addition to state policy instruments coming directly from the central government, the development of Chinese AI is marked by the fact that the Chinese political-economy lacks some of the distinctions and firewalls between private and public activities that characterize Western countries. For instance, it is worth noting also that most of the Chinese financial system is state-owned and state-controlled either in nominal or significant measure, as are many of China’s most

10 Lee, 2018. 11 Browne, April 9th, 2018

In addition to state policy instruments coming directly from the central

government, the development of Chinese AI is marked by the fact that the Chinese

political-economy lacks some of the distinctions and firewalls between private

and public activities that characterize Western countries.

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successful companies. Even when examining the governance of private commercial entities such as the powerhouse companies with stake in AI R&D, like Tencent and Alibaba, the lines between public and private are in fact blurred in these cases as well. For instance, these companies and many others like them feature board appointments from the Communist Party of China, a merging of public and private interests including through government edicts in favour of certain commercial behavior, and the intertwining of state and private initiatives activities due to state ownership of the financial system and state management of capital markets. Keys to Success Experts in the field have suggested that while other jurisdictions may lead in technological research and discovery, China is an incontestable leader in the implementation of technologies.12 This research would mirror that finding with regards to China’s AI strategy, noting also that China’s success in implementing related and complementary technologies has helped to compound its success with AI. For instance, China moved from an almost completely cashed-based economy in 2014, to an almost completely cash-less economy by 2018 (See Figure) driven by digital technologies and mobile transfers.13

Source: iResearch via That’s Magazine While the abrupt change from a cash system to one of digital payments remarkable in its own right, the move to digital payments also contributes to an exponential increase in the amount of data that 12 Lee, 2018. 13 As of 2018, over 80% of transactions in China were being conducted by mobile phones, China having leap-frogged the intermediate step of interact and visa transactions, jumping straight from cash to next-gen FinTech. China’s lead in mobile payment adoption is so substantial, that mobile payments in China now represents more than 50% of all global financial transactions by mobile phone. See: Yang, July 17th 2018.

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can feed into AI systems. Certainly this adds a significant increase to data production and what is ultimately being put to use in AI R&D, but also comes with significant increases in the prospects of putting AI to use in improving Chinese financial technologies. The types of complementary actions are being taken throughout the Chinese polity and demonstrate the clear benefit of long-term strategic thinking and institutional coordination. The Chinese drive for AI leadership is also noteworthy in its approach since this action is not confined to one government portfolio or another (such as industry, or scientific inquiry or otherwise) but rather represents a successfully executed whole-of-government initiative.14 It is quite a feat to achieve a horizontal integration of the AI leadership drive across all relevant departments, and indeed this accomplishment has proven a boon to the AI strategy in China overall. Some of the success in this horizontality however is clearly not replicable elsewhere. For one, China is a non-democratic entity that centralizes control in the Communist party, not the state administration, which permits Chinese initiatives to intersect across government departments more easily than in democratic jurisdictions elsewhere. This approach is of course not compatible with the governance structures used in most jurisdictions.

Sophisticated and interdisciplinary issue areas, such as AI industries, require effective horizontality and more

systematically encouraged cooperation between government departments.

Yet this should not be allowed to support the perpetuation of strong and ill-advised siloes between government departments in other jurisdictions. Some siloes should continue to exist and even be entrenched as a matter of principle, such as, for one example, those siloes that exist between departments which collect sensitive citizen data and intelligence gathering entities. In many (if not most) other cases, departmental silos perpetuate collective action challenges as a matter of routine rather than as a result of clear-eyed and intentional decision-making. Sophisticated and interdisciplinary issue areas, such as AI industries, require effective horizontality and more systematically encouraged cooperation between government departments. This may be especially so for AI, which stands to benefit greatly from integrated datasets that cross government departments. At present, these kinds of integrated datasets are very rare and seldom compatible with one another. Another advantage of China’s strategy has been its ability to effectively intersect the public/private sector boundary and marshal resources from both sectors into its AI strategy. Again, some tenets of this successful practice are transferable while other parts are not. China has a different view 14 Williams, April 16th 2018.

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towards governance which includes a comparative lack of emphasis on pluralism and distinctly less attachment to (often Western) conceptions pertaining to the hermetic separation of public and private activities. This makes for quite a powerful concentration of power in the Chinese state surrounding industrial policy which in turn engenders a strong capacity for industrial development once a particular course of action has been decided upon.15 Again, much of this is a function of the ruling Communist Party whose membership transects the leadership positions of both state and society. In addition to this system coming with plenty of disadvantages, this concentration of power is strictly non-compatible with many governance systems and principles elsewhere. The Chinese approach does however highlight the potential that comes with harmonious cooperation between public and private sectors when it comes to such a major undertaking as an artificial intelligence strategy. A solution that is compatible with Canadian values would be the continued use of multi-stakeholder processes in areas like AI policy in order to ensure private and public sectors are able to coordinate as much as is possible. Another palpable solution in this vein, and one that was employed with great success in Estonia, was to limit the technical barriers and policies which impede the flow of human resources between public and private sectors. This created a more competitive high technology sector and made it easier for government to resource its initiatives pertaining to technology. This practice was found to improve the empathy and goodwill that existed between sectors, a development that has ultimately permitted them to work better together.16 In many Western countries however, the opposite tack is being taken, with the technology and government sectors being increasingly estranged from one another. This trend is often attributed to the 2013 Snowden revelations, where a private technology contractor leaked classified government information to the public.17 As a consequence, restrictions on the activities of technology firms working for government have increased, as has mutual distrust between public and private sectors working with data and technology. This presents an obstacle to future initiatives supporting AI and the cultivation of leadership in AI industries. While it is somewhat simplistic to suggest that trust needs be rebuilt, ultimately the solution is just that. Efforts to forge new relationships between the state and technology sector will be crucial to improving the ability of both to be successful. Lessons Learned China’s ability to marshal so many state and economic resources into a coordinated and harmonized approach to the AI race is very much a product of a Chinese system of government. “China’s centralized and government-directed AI research, through its military as well as the quasi-private sector vehicles of Baidu, Alibaba, and Tencent, is...oriented to serve national

15 For more on Chinese industrial policy see the “Belt and Road Initiative” such as: Busza and Jin, May 31st 2017. 16 Robbins, 2018. 17 Bradshaw, 2018.

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interests of the central government, and can count upon a rich and government controlled pipeline domestic citizen data.”18 Copying such an endeavour could of course never be done in a Western liberal democracy for a wide range of reasons, not the least of which being long-standing norms in support of political pluralism that expressly interdict such a strong concentration of power. That concentration of power is ultimately a necessary prerequisite to the Chinese strategy. Nonetheless, some tenets of the Chinese strategy will likely provide important insights for decisionmakers nonetheless and thus merit close consideration.

The Chinese system of government is well-known for being heavily centralized, and its AI strategy has been no exception to this rule, with detailed information about citizens being centralized and easily accessible to decision-makers within the central government. It is worth noting however that this is (with a heavy dose of privacy protections) also a best-practice in data management that is being employed by other liberal-democratic jurisdictions, like Estonia. Although unpopular and somewhat taboo, there are many advantages to the centralization of citizen data, some of which have

clear spill-overs into the realm of AI. It may well be that an effective AI strategy requires a greater centralization of data ownership than is currently within the comfort levels of policy makers. If this is indeed the case, then effective national strategy for developing AI will require a dose of statecraft in addition to good policy-making. In spite of its habitually centralized political system, China has also proven capable of being omnivorous in its governance strategies pertaining to adoption of emerging technologies, implementing technologies on the basis of decentralized authority as well when this approach appears to be the most suitable use of the technology. For instance, China has been one of the most enthusiastic adopters of blockchain in its financial system, which is a form of highly distributed computing and transparent accounting system. When it comes to strategy for AI and other technologies, it may serve well to recall Deng Xiaopeng’s now famous adage, “I don’t care if it is a black cat or a white cat, so long as it catches mice.” Different technologies will have greater or lesser capabilities with different political systems, and although AI may lend well to the centralization that is common in the Chinese system, the attributes of these technologies should not be allowed impede their thoughtful use. This lesson exists in contrast to some of the political discourse taking place in liberal democratic countries, perhaps most notably the United States, where digital technologies are accused of being 18 Lombardi, March 23rd 2018.

It may well be that an effective AI strategy requires a greater

centralization of data ownership than is currently within the

comfort levels of policy makers. If this is indeed the case, then

effective national strategy for developing AI will require a dose of statecraft in addition to good

policy-making.

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“undemocratic” or “unethical”. From a strictly technical standpoint of course this is not possible; intelligent machines are the product of their users and have no values other than those with which they have been instilled by their designers. AI as a technology has little more capacity to be “unethical” than a hard-drive or a memory key. Nonetheless, fundamentally amoral technologies like AI have become politically charged in a public discourse that is increasingly polarized. The prevalence of this reactionary discourse from the level of policy represents a serious risk to fruitful and intelligent adoption of this new technology and presents a major barrier to the state’s capacity to develop and implement a coherent national strategy. This has the potential to be one of the most significant barriers to an effective AI strategy, not only due to the incompatibility of these views with good policy but also because they divert the political discourse on AI away from technology and industry, and into subjects of social policy. Of course, social policy is important on its own merits, but when policy swims outside of its lane as such, it has the impact of slowing policy development towards a strategy. Evaluating Global Leadership China benefits from substantial large-scale collaboration between state and private companies, and a generally higher willingness to experiment with new ideas and new products. The national security element of AI has also seen China develop what is often assessed to be a stronger national plan than that of the United States. Nonetheless, the United States is starting at a much higher point in the development of AI industries and there is little prospect of China being able to displace American leadership in AI in the immediate future. However, the rate of improvement in China’s AI sector is remarkable. From a policy and regulatory standpoint in particular, China has systematically cultivated itself into a desirable jurisdiction for conducting AI research and development. By comparison, American policy instruments have lagged in their efforts at continuous improvement. All of this to say that from purely industry standpoint, China leads in policy support for AI while the United States continues to lead in economic climate. While the US has been the long-term leader in this field, China is catching up quickly. In addition to strategy, China has a few innate advantages in AI development due to its large population, low data protection requirements, and convergence of business and state. American politics by contrast has become increasingly fractured making it a serious challenge for the US to launch a new vision for AI policy and to marshal existing resources. In addition to often ineffective attempts at directing US policy, an increasingly corrosive political discourse has politicised many technical requirements for developing effective AI. Discussions about the potential for AI to perpetuate institutional discrimination, gender-binaries and new forms of income redlining have taken centre-stage in the political discourse surrounding AI. While these are of course worthy subjects in their own right, the inability to achieve any sort of consensus on these issues as they relate to AI- even

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simply an agreement to disagree- ads significantly to the difficulty of wielding policy in a way that lends to technological progress with AI. This implications of China’s ambition in AI policy should not be overstated as some commentators have done by suggesting that this is evidence of an imminent Chinese-lead global cyber-dystopia. This is obviously not true, even though the relationship between the security sector and AI industries is for some a cause for concern. But in fact it is not uncommon for policy in emerging technology areas to be delivered through national security apparati for the simple reason that the defence sector often finds itself responsible for new technologies due to the lack of precedence or capacity in other departments. Indeed, inventions like the internet, nuclear energy, rocketry,

spacefare and the like, were all effectively placed under a state monopoly via the military for the early years of their existence. This is not particular to China. The United States made preliminary moves to support AI industries through the defence sector as well, specifically through DARPA, an agency of the US military, as has Russia similarly used state security organs to lead its development of cyber technologies. When it comes to Canada’s position in the global AI ecosystem, there is reason for tapered enthusiasm. Canada is a global hub for AI talent and development and Canadian researchers are very well respected. The government of Canada is also serious about providing thoughtful support for AI industries, announcing several rounds of substantial funding for excellence in AI. This occurs as the public administration

is seeking to modernize its operations and procure more digital and high-tech solutions, which can also be leveraged to support the growth of the industry. Even with all of these positive developments, Canada is very far from being in a position to claim the mantle of global leadership. Even as China, a country of 1.5 billion people, actively and aggressively seeks a global leadership position in AI, it remains far behind the US. For all its momentous efforts, China seems well poised to eventually overtake US leadership in AI, but to suggest that Canada is a serious contender for global leadership in AI overall is an exercise in political jousting, and not true policy analysis. In global AI policy, as in many policy realms, Canada is a “rule-taker” not a “rule-maker”, meaning that changes in the tectonics of global technology leadership will have significant implications for Canadian policy. More precisely this is to say that, Canada tends largely to work within standards and practices that have been developed elsewhere, arguing for changes at the margins to be sure, but nonetheless operating within the larger paradigm set by superpowers, rather than developing and proselytizing its own policy paradigm. In spite of Canada’s relative leadership position and ambitions, there is little indication that Canada will become a global “rule-maker” in AI, and will

When it comes to Canada’s

position in the global AI

ecosystem, there is reason

for tapered enthusiasm.

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likely continue to be a “rule-taker”.19 With China increasingly becoming a global player in the AI space and eventually a “rule-maker”, Canada will need to adjust, as well as to decide which battles to pick when advocating changes to this new AI paradigm. This will require a careful examination of what policy changes can reasonably be advocated and achieved. Recommendations for Policy Makers It is incumbent on Canada to discover and cultivate its real competitive advantage in AI. Much of existing strategy proposes generalizable Canadian leadership in AI, which is a recipe for a critical lack of focus. If China, the world's second-largest economy and perhaps most ambitious investor in AI, has made strategic decisions to pursue specific competitive advantages and not others, this clearly an important strategic consideration for Canada to consider is well. When considering as well that Canada has substantially fewer resources to dedicate to AI, it is imperative for decision-makers to ensure that these resources are well-targeted. A good first step would be a comprehensive review of these advantages which goes far beyond the moniker “AI” and gives ample consideration all the relevant inputs and ability to scale downstream applications. The Chinese case is a clear demonstration that among the pantheon of state institutions, there certainly remains room to improve coordination and increase program synergies. This raises the question of whether or not Canada can achieve better coordination of its state institutions, to which the answer is certainly “yes”. A good preliminary step would be for this horizontality and collaboration between departments central to Canada’s AI strategy to be explicitly reflected in mandate letter for ministers. At a deeper level, the government should commit to developing and implementing a national data sharing strategy that would lock government departments (at all levels) into compatible standards and portability. There will be limitations to this, to be sure, but progress in this space will help to engender a common purpose among departments and to make important data available to AI. Further to this end, the government of Canada should continue to aggressively pursue excellence in Open Data and ensure that as much data as possible is made Open by Default.20 This is in fact one of Canada’s competitive advantages in AI since the degree of openness with data that the government of Canada is pursuing, is likely to be wholly impossible for a system of government as secretive and controlling as that of China. While secrecy and control may support Chinese efforts at implementing new systems, they would hinder developments in support of genuine innovation or decentralized co-creation. In this sense, some of the inherent advantages that come with the Chinese political system may well have been fully exploited, while the

19 Some have speculated that Canada could become a global leader in “ethical AI” although what precisely constitutes “ethical AI” and how one might go about achieving leadership in this is not abundantly clear. 20 In 2018, the government of Canada was in fact ranked 1st globally for data openness, which while an important recognition of the good work being done in the Canadian public administration, it is not cause for allowing progress to stall. There remains much work to be done if this data is to make a meaningful contribution to progress in AI industries.

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advantages inherent to the Canadian political system have yet to be realized. This makes Canada’s efforts at developing a successful approach to Open Government all the more important. In the age of AI and big data, Open Government is not just about being transparent, it's about being economically competitive. Overall, there would seem to be a recognition in the United States that China has the capability to emerge as a leader in AI and that it has the potential to displace the US. China’s strategy for improving the technology sector has been described by the American Council on Foreign Relations identified as an “existential threat to US technological leadership”.21 This has clear geopolitical implications, which have the prospects of being quite negative in a political environment that is highly charged and competitive. Some even view the economic benefit of leading in AI development as being related to, or potentially combined with, military power in the 21st century. If true, or widely perceived to be true, this could lead to fundamental shifts in the global balance of power. These advances are provoking a defensive US response and in the Trump administration’s position on a trade war with China. Canada will likely be unable to walk the tightrope of escalating competition between these two powers. In 2018, Canada found itself in a difficult position when it arrested telecommunications provide Huawei’s Chief Financial Advisor, Meng Wanzhou, at the request of US authorities, facing a worsening diplomatic situation and tit-for-tat arrests of Canadian citizens.22 As global standards begin to emerge for governing AI and AI industries begin to engage in more direct competition, more delicate circumstances are likely to emerge. This is perhaps especially likely in the event Canada is continuing to successfully advance its AI industries, making it a more important site for great power competition. Conclusions China has put a huge amount of political and financial resources into an economic development strategy centred on technology, especially AI, and this merits close attention for lessons learned. There are risks to over-extending the applicability of the Chinese strategy, much of which stems from characteristics of Chinese governance which are non-transferable. Yet policy isomorphism and the sharing of best practices is key to continuous improvement and good governance. All of this to say that lessons from China’s AI strategy should be closely studied for their relevance and significance, and doubly so for their compatibilities with Canada’s political system. As Canada continue to develop and promote its own capacities with AI, it will need to continue paying close attention to best practices where they exist and keeping a close eye on emerging global standards.

21 Laskai, 2018. 22 Stober, December 17th, 2018.

As global standards begin to emerge for governing AI and AI industries begin to

engage in more direct competition,

more delicate circumstances are likely to emerge.

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China is a key site for both of these things and this is likely to continue over the long term, making it an important place to watch.

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