# Artificial Intelligence, Surveillance, and Big Data111This working paper is
expected to appear in: Diginomics Research Perspectives: The Role of
Digitalization in Business and Society (Springer Nature, editor: Lars Hornuf).

David Karpa
University of Bremen
<EMAIL_ADDRESS>Torben Klarl
University of Bremen /
SPEA, Indiana University, Bloomington<EMAIL_ADDRESS>Michael Rochlitz
University of Bremen /
ICSID, Higher School of Economics, Moscow
<EMAIL_ADDRESS>
(August 28, 2024)

###### Abstract

The most important resource to improve technologies in the field of artificial
intelligence is data. Two types of policies are crucial in this respect:
privacy and data-sharing regulations, and the use of surveillance technologies
for policing. Both types of policies vary substantially across countries and
political regimes. In this paper, we examine how authoritarian and democratic
political institutions can influence the quality of research in artificial
intelligence, and the availability of large-scale datasets to improve and
train deep learning algorithms. We focus mainly on the Chinese case, and find
that – ceteris paribus – authoritarian political institutions continue to have
a negative effect on innovation. They can, however, have a positive effect on
research in deep learning, via the availability of large-scale datasets that
have been obtained through government surveillance. We propose a research
agenda to study which of the two effects might dominate in a race for
leadership in artificial intelligence between countries with different
political institutions, such as the United States and China.

Keywords: artificial intelligence, political institutions, big data,
surveillance, innovation, China

JEL: O25, O31, O38, P16, P51

##  1 Introduction

During the late 2000s and early 2010s, a number of publications on recursive
learning in multi-layered neuronal networks led to a breakthrough in the field
of artificial intelligence (Hinton and Salakhutdinov, 2006; Hinton et al.,
2006; Krizhevsky, 2009; Krizhevsky et al., 2012). The studies laid the
foundation for a new approach to machine learning: recursively self-improving
algorithms are structured in layers to create programmable neural networks,
which are then used to sort through large amounts of data in a process called
“deep learning”. Other than with traditional machine learning, deep learning
algorithms do no longer require outside training. Instead, they are able to
autonomously reach a goal set at the beginning, with the speed of learning
depending on the amount of raw data available.

After a couple of years, machine-learning techniques based on deep learning
have led to a number of major breakthroughs in various fields such as image,
speech and facial recognition, medicine, particle physics, neuroscience and
language translation (LeCun et al., 2015). The larger public became aware of
the new technology when a team from Deep Mind, an AI firm now acquired by
Google, managed to beat Lee Sedol, one of the highest ranking professional Go
players, in a five-game match in March 2016 (Silver et al., 2016). While for
the game of chess IMB’s Deep Blue had already managed to beat world champion
Garry Kasparov in 1997, Go is an order of magnitudes more difficult to solve
than chess.222While Deep Blue beat Garry Kasparov in 1997 by using brute force
computing, this approach does not work for Go, a game with 250 possible moves
each turn, and a typical game depth of 150 moves, resulting in about
$250^{150}$, or $10^{360}$ possible moves – more than the number of atoms in
the observable universe. Winning the game against one of the world’s leading
players thus demonstrated the potential of the new technology (Silver et al.,
2017; Bory, 2019).

Deep Mind’s win against Lee Sedol had also another important implication. Go
was invented in China, is mostly played in China, Japan and Korea, and is
still considered by many people in East Asia as a quintessential Asian game
(Moskowitz, 2013). That a Western company managed to win the game with the
help of a computer program against a leading Asian player led to a spike in
interest in artificial intelligence research in China, triggering a large-
scale government program to promote and support the already fast-developing
sector.333Lee, (2018, page 3) compares the effect of Deep Mind’s win against
Lee Sedol to America’s Sputnik moment: “Overnight, China plunged into an
artificial intelligence fever. The buzz didn’t quite rival America’s reaction
to Sputnik, but it lit a fire under the Chinese technology community that has
been burning ever since”. 5 years later, China has become – together with the
United States – one of the two leading nations in the field of AI research,
with both countries engaged in a tight race for leadership.

Figure 1 shows that while China has already overtaken the United States in
terms of the overall quantity of AI-related research publications, it still
lags behind with respect to the number of high quality publications in applied
AI research, where China today is at about the same level as Germany or the
UK.444As a proxy for research quality in artificial intelligence, we use the
Nature Index for the year 2020. The index counts publications in applied
artificial intelligence published in Nature and its sub-journals, such as
Science, the Proceedings of the National Academy of Sciences (PNAS), and
others. The list can be accessed under https://www.natureindex.com/faq, and
the source of our data can be found here:
https://www.natureindex.com/supplements/nature-index-2020-ai/tables/countries.
As a proxy for the overall quantity of publications in artificial
intelligence, we use the Nature Index Dimensions database, which counts all
publications in AI from a specific country between 2015 and 2019:
https://www.natureindex.com/supplements/nature-
index-2020-ai/tables/dimensions-countries. A similar picture – higher quantity
but still lower quality, on average – also exists with respect to patents, for
example in the case of the internet of things (Kshetri, 2017), and when
comparing research institutions. Here American and European universities are
still ahead when looking at high-quality output, whereas Chinese institutions
have recently overtaken all other countries in the world with respect to the
overall number of AI-related publications per university (Savage, (2020), see
also tables 1 and 2 in section 4).

Figure 1: Publications in selected journals (x-axis); publications total
(y-axis)

In this paper, we try to understand if China has the potential to overtake the
United States also in terms of research quality. To answer this question, we
focus on one fundamental difference between both countries – the difference
between authoritarian and democratic institutions. While the United States are
a competitive democracy, China is an authoritarian single-party state. Our
objective is to study how this difference in political institutions can affect
the quality of research and innovation in the field of artificial
intelligence. Apart from permitting us to better understand the AI race
between the United States and China, this will also allow us to gain a better
understanding of the role political institutions play with respect to research
and innovation.

Conventionally, authoritarian political institutions are seen as having a
mostly negative effect on innovation (see for example Huang and Xu, 1999;
Stokes, 2000; Josephson, 2005; Tebaldi and Elmslie, 2008; Knutsen, 2015; Silve
and Plekhanov, 2018; Tang and Tang, 2018; Rosenberg and Tarasenko, 2020). In
authoritarian political systems, the story goes, censorship, surveillance and
repression, while necessary for the survival of the regime, limit the free
flow of information, and make it – on average – more difficult for scientists
to freely engage in research, thus limiting their creativity and
inventiveness.555A certain amount of censorship, surveillance and government
control can of course also be found in democratic political systems. We
therefore assume a continuum of repressiveness, from complete freedom to
complete state control, with the negative effects on creativity increasing
with growing government control.

The central argument of our paper is that with respect to innovation in the
field of artificial intelligence, this conventional wisdom might no longer
hold, with the negative effects of authoritarian institutions potentially
being offset by the opportunities offered through big data. As pointed out
above, the most important input for deep learning algorithms to self-improve
are large amounts of data (Hey, 2009; Halevy et al., 2009; Domingos, 2015; Sun
et al., 2017; Beraja et al., 2021). With respect to the availability of large
data sets, modern authoritarian states with sophisticated surveillance systems
might have a significant advantage over democratic systems, where – on average
– policies on government surveillance and data sharing are stricter than in
autocracies (Kshetri, 2014; Chen and Cheung, 2017; Strittmatter, 2020; Zeng,
2020). Less strict data sharing laws can permit authoritarian governments to
allocate data gathered through surveillance as an input-subsidy to domestic IT
firms, an industrial policy the Chinese government is already increasingly
pursuing (Lee, 2018; Beraja et al., 2021). A side-effect is that in this way,
authoritarian states can use the data they have gathered through surveillance
to further improve their AI-based surveillance capabilities, thus initiating a
positive feedback loop. As a result of this process, China has already become
a leader in the field of surveillance technologies, and has started to export
them to other authoritarian countries (Feldstein, 2019).666See also “The
Panopticon is Already Here”, The Atlantic, September 2020,
https://www.theatlantic.com/magazine/archive/2020/09/china-ai-
surveillance/614197/.

Our hypothesis is that whether China will manage to catch up to, and
potentially overtake the United States to become world leader with respect to
cutting-edge innovation in AI will be determined by the trade-off between the
negative effects of censorship, surveillance and control on innovation and
creativity, and the positive effects of having access to better and more data
than countries that do not rely to the same extent on digital surveillance. As
we argue in section 3, for the time being – and for the foreseeable future –
China will remain at a disadvantage when it comes to conventional innovation,
as its authoritarian institutions are hindering the free flow of information,
as well as intrinsic motivation, creativity, and the possibility for
scientists to freely determine their research agenda. On the other hand, China
has already today an advantage with respect to using big data as an input in
AI research, as we document in section 4. We argue that who will win the race
in artificial intelligence will ultimately depend on what type of research
will be more important in the field during the next couple of years. If new
and innovative ideas lead to another breakthrough after the deep learning
revolution, the US might keep the lead in the foreseeable future. If however
large datasets to train and improve existing algorithms turn out to be most
important during the next decade, China might well become the overall leader
in the field.

This paper is structured as follows. Section 2 introduces a simple theoretical
framework, which will be used in the subsequent sections to guide our
argument. The framework consists of three actors – the authoritarian regime,
the public, and high-tech firms, a number of strategies used by the
authoritarian regime to ensure its political survival (repression, censorship,
surveillance and public spending), and a number of outcomes we are interested
in, most importantly the amount of data available to research institutions,
and the quality of research and innovation in AI. Section 3 discusses the
negative effects of censorship and surveillance on research and creativity,
thus reiterating and illustrating the conventional argument that on average,
authoritarian political institutions have a negative effect on innovation.
Section 4 then introduces our argument, by showing how large amounts of data
gained through government surveillance can, potentially, have a positive
effect on innovation in AI. Finally, section 5 summarizes our argument and
concludes.

##  2 Theoretical framework

An extensive literature has examined the strategies used by authoritarian
governments to remain in power, with Egorov and Sonin, (2020) providing a
recent overview. According to Svolik, (2009), authoritarian regimes during the
last 70 years have faced two principal challenges – revolution from below, and
palace coups from within. Traditionally, most authoritarian regimes have
reacted with either repression or cooptation to these challenges. While
repression to get rid of regime opponents has taken the form of arrests,
purges, deportation or worse (Moore, 1998; Gregory et al., 2011; Blaydes,
2018; Montagnes and Wolton, 2019; Buckley et al., 2021), public spending has
been used to either convince the general public that it is better off under
the status quo than under an alternative regime (Acemoglu and Robinson, 2006),
or to co-opt a more narrowly defined elite, in order to prevent a coup (Bueno
de Mesquita et al., 2003; Gallagher and Hanson, 2015).

More recently, the growing importance of the internet and more sophisticated
censorship, surveillance and propaganda techniques have led to the emergence
of a new type of dictatorship. In these so-called “informational autocracies”,
the manipulation and targeted use of information has become the preferred
strategy of the government, while repression is no longer as important as
before (Guriev and Treisman, 2019, 2020). Examples of modern informational
autocracies include such diverse countries as Russia under Vladimir Putin,
Hungary under Victor Orban, Turkey under Recep Tayyip Erdoğan, as well as
contemporary China.777As China does not have elections and is still using
repression, it is sometimes not seen as a classic “informational autocracy” in
the sense of Guriev and Treisman, (2019, 2020). However, China has – more than
any other country – perfected the strategic censorship of the internet (King
et al., 2013, 2014; Roberts, 2018), as well as modern authoritarian
surveillance technologies (Kostka, 2019; Kostka and Antoine, 2020;
Strittmatter, 2020). Following Ringen, (2016) and Minzner, (2018), we maintain
that it can therefore be argued that China has taken the idea of an
“informational autocracy” to the next level, by combining sophisticated
surveillance and censorship techniques with targeted repression. In this new
type of informational autocracy, increasingly sophisticated surveillance
technologies are used to keep the population in check, but can also permit the
state to conduct a new kind of industrial policy, by using data collected
through surveillance as an input for research and innovation (Beraja et al.,
2021; Karpa et al., 2021).

Figure 2 illustrates our theoretical framework, by presenting the different
strategies used by authoritarian regimes in simplified form. Red arrows
represent a negative relationship, whereas green arrows illustrate a positive
effect. As can be seen, repression and censorship make it more difficult for
the public to organize through collective action.888Whereas repression has a
directly negative effect on the ability of the public to meet and protest,
censorship limits the ability of the public to communicate, to access
independent information, and to subsequently coordinate collective action.
Surveillance plays a similar role, by rendering censorship (Roberts, 2020) as
well as repression and cooptation (Xu, 2021) more precise and cost-
effective.999The red arrow from surveillance to public spending is motivated
by Pan, (2020) and Xu, (2021), who show that with better surveillance, lower
amounts of public spending have to be used to achieve the same reduction in
the propensity to revolt. As a result, all four strategies reduce the
propensity of the public to revolt, and strengthen the political control of
the authoritarian state.

Figure 2: Theoretical Framework

Repression, censoring and surveillance, however, can also have direct negative
effects on the ability of an economy to innovate. While the negative effects
of repression on innovation are well documented,101010See for example
Waldinger, (2010, 2012) and Medawar and Pyke, (2012) on how the expulsion of
scientists from Nazi Germany in the 1930s affected the quality of research in
Germany. we focus in particular on strategies used by informational
autocracies, i.e. on how censorship and surveillance might hinder research and
innovation. Section 3 will discuss these negative effects in detail.

In section 4, we then turn to the effect of big data, and the potential
positive interaction between authoritarian surveillance and innovation in AI.
The green arrow from surveillance to big data, and from there to innovation in
artificial intelligence illustrates how data gathered by the authoritarian
state can be used as an input to accelerate research in the field. The green
arrows from innovation to censoring and surveillance, in turn, symbolize the
positive feedback effect of innovation in AI helping to improve censoring and
surveillance technologies, which then can be used to generate even more and
better data. After outlining both the negative and positive effects of
authoritarian control on innovation in sections 3 and 4, we discuss in section
5 under what circumstances the negative or positive effects might be more
prominent.

##  3 Authoritarian Control and Innovation: Negative Effects

###  3.1 Censorship

Censorship has been used as a control strategy by authoritarian regimes since
ancient times. In this section, we first outline why and how censorship is
used in modern autocracies, before discussing how it affects innovation.

As outlined in figure 2, censorship can reduce the ability of the public to
self-organize and revolt, both by making coordination more difficult, and by
reducing the amount of information critical of the government. Authoritarian
governments, however, face a trade-off with respect to how much censorship to
use. In particular in large authoritarian states, some amount of uncensored
information from institutions independent of the government might be
necessary. Otherwise, the central government might become completely dependent
on information provided by regional bureaucrats (Pan and Chen, 2018) or the
security services (Soldatov and Rochlitz, 2018), who can use this dependence
to advance their own agendas.111111A detailed discussion of this trade-off for
the case of Russia and China can be found in Libman and Rochlitz, (2019),
chapter 4. As we will outline in more detail below, censorship also always
imposes an economic cost, by rendering information more expensive.

Authoritarian governments have reacted with different strategies to this
dilemma, which are classified by Roberts, (2018) according to the mechanism
through which they affect the user, namely fear, friction, and flooding. Fear-
based censorship operates similar to classic repression, with users being
deterred from accessing or distributing certain types of information by
threats of costly punishment. Many authoritarian regimes have adopted laws and
regulations that set clear boundaries regarding information usage, with
journalists and content creators having to fear the possibility of arrest or
loosing their jobs. Pan and Siegel, (2020) show that fear-based strategies can
work, by illustrating how repression of well-known online activists in Saudi
Arabia in the form of imprisonment or torture makes them less likely to
continue their criticism of the government, after their release from custody.

If fear-based censorship is too obvious, however, it can produce adverse
effects. When the arrests of online activists became public knowledge in Saudi
Arabia, Google search requests for their names increased significantly (an
effect often referred to as the Streisand effect121212The Streisand effect
occurs when the censoring of information increases its value or makes it more
attractive (Hobbs and Roberts, 2018). The name of the effect goes back to
Barbra Streisand, who when trying to remove pictures of her home from the
internet attracted even more attention to them.), while anti-government
sentiment and mobilization among the followers of the arrested activists also
increased (Pan and Siegel, 2020). In addition to the Streisand effect, Hobbs
and Roberts, (2018) also identify a gateway effect of censorship. When
Instagram was blocked in China during the Hong Kong protests in 2014, users
started to employ virtual private networks (VPNs), and through this then
became familiar with additional politically sensitive content on other
platforms that were blocked in China (Hobbs and Roberts, 2018).

In order to avoid this kind of backlash, a more sophisticated form of
censorship is to introduce some element of friction, which is often less easy
to notice than outright repression. Instead of restricting information
explicitly, selected content is made time or resource intensive to access,
rendering it more costly than other information (Roberts, 2018). Examples of
friction include the use of firewalls that block specific websites (Qiang,
2019), or the manipulation of results from search engines (Ruan et al., 2017).
Here as well, China has been a pioneer, and is more advanced today than most
other authoritarian countries. As shown by King et al., (2013, 2014), the
Chinese state is not censoring all forms of government criticism from the
Chinese internet. Instead, targeted censorship is used to get rid of posts
that have the potential to result in collective action against the government.
In this way, the Chinese state solves the authoritarian censorship dilemma
described above, by permitting the internet to continue its role as a
monitoring and information tool for the central government, while preventing
it from playing the role of a “liberation technology”, i.e. a tool that could
be used by citizens to coordinate and organize public protests.

As illustrated by the green arrow from innovation to censorship in figure 2,
these kind of censorship technologies are fast evolving. While the censorship
described by King et al., (2013, 2014) was still mostly a manual effort by
thousands of Communist party and police workers, deep learning algorithms have
since started to assist and sometimes already replace manual censorship,
making censorship cheaper, more sophisticated, and less easy to recognize and
evade (Dixon et al., 2016; Roberts, 2018; Griffiths, 2019; Tiffert, 2019;
Strittmatter, 2020; Tai and Fu, 2020; Gao et al., 2021).

Finally, flooding is a technique that in its results closely resembles the
effect of introducing friction, i.e. access to certain types of information
becomes more costly. It differs however in the way that it is implemented,
with the cost of accessing independent information being increased by the
widespread distribution of alternative information congruent with the agenda
of the state. Flooding is particularly prevalent on social media, where
government-affiliated actors systematically create information and distribute
it. King et al., (2017) document how in the mid-2010s, the Chinese government
was responsible for the fabrication of approximately 448 million social media
posts per year. These posts were not used to argue with skeptics of the
Communist party or the government, nor were they used to influence discussions
of controversial issues, but rather to distract the public, change the
subject, and altogether divert attention from discussions about politics or
sensitive issues. In Russia, the government has been following a similar
strategy, with troll factories flooding social media with pro-government
messages, making it more difficult for users to identify information critical
of the government within the vast mass of available content (Karpan, 2019;
Linvill and Warren, 2020). As a result, while in the late 2000s social media
was still described as a “liberation technology” and considered by many as
constituting a danger to authoritarian regimes (Diamond, 2010), modern
informational autocracies have since reacted and seem now to be able to use
social media as an additional tool to foster regime stability (Gunitsky,
2015).

Censorship and Innovation While fear, friction and flooding permit the
authoritarian state to reduce the risk of collective action and public unrest,
they also come at a cost. In this paper, we will focus in particular on how
these censorship technologies influence the ability of an economy to generate
new ideas and innovate.

A classical example that already existed in traditional autocracies is the use
of repression to get rid of scientists critical of the regime, or – inversely
– the promotion of academics who refuse to criticize the government. In an
excellent study of the Chinese university system, Perry, (2020) shows that
this still has a significant effect on scientific productivity in contemporary
China, as the allocation of resources to individual researchers and projects
is often based on political instead of scientific criteria. Similar problems
were faced by the Soviet Union (Graham, 1987, 1993), and most importantly Nazi
Germany, which suffered from an enormous brain drain as a result of the
repressions of the 1930s (Waldinger, 2010, 2012).

Less obvious and more sophisticated censorship techniques can also have a
negative effect on innovation, however. One of the biggest problems faced by
foreign companies in China is the way in which censorship and the great
firewall are slowing down internet connections, especially to servers based
abroad.131313In a series of interviews with foreign investors carried out in
Shanghai in May 2016 by one of the authors of this study, nearly all investors
complained that internet censorship and slow connections are rendering their
activities significantly more difficult. See also “China Internet Restrictions
Hurting Business, Western Companies Say”, Wall Street Journal, 12.02.2015,
https://www.wsj.com/articles/BL-CJB-25952. Similar problems are faced by
scientists and researchers. While the domestic internet in China is easily
accessible and fast, scientists who want to have unlimited access to the
global web to participate in international scientific debates face significant
technical hurdles and costs, as connections to the global net are slow and
often many times more expensive than connections to the domestic
internet.141414According to an article published in The Atlantic, in 2013
Peking University charged $1.50 a month for unlimited domestic Internet use,
but $14.50 for unlimited access to the World Wide Web (“How Internet
Censorship Is Curbing Innovation in China”, The Atlantic, 22.04.2013,
https://www.theatlantic.com/china/archive/2013/04/how-internet-censorship-is-
curbing-innovation-in-china/275188/).

In addition to slow internet connections, problems with the accessibility of
data also make research more difficult for scientists in China and other
authoritarian countries. One problem is that data provided by the government
migth be strategically manipulated, if these data are also used as input for
the performance evaluation of government officials (Kalgin, 2016; Wallace,
2016). Xiang, (2019) maintains that in particular with respect to research in
the field of artificial intelligence, data quality for China-based researchers
remains heterogeneous. While with respect to surveillance and control
technologies Chinese researchers enjoy a definite advantage as compared to
their peers in the West, as the security services have strong incentives to
collaborate with IT firms with respect to the development of new surveillance
infrastructure and the sharing of data (Xiang, 2019, page 45), this is not
necessarily the case in other fields. With respect to research in autonomous
driving, speech recognition or robotics, for example, the quality and amount
of available data are mixed at best, and often lower than in the US or Europe,
as local and regional governments have no incentives to make data available,
but on the contrary fear that providing data as an input for scientific
research can get them into trouble (Xiang, 2019, page 32).

A further issue is that especially in the social sciences, Western publishers
are facing increasing pressure by the authorities in Beijing to render
articles critical of the Chinese government invisible in their academic
journals. The articles are then not only not available for download, but
Chinese researchers will never learn that they actually exist, if they use
only the domestic version of the Chinese internet to search for information
(Wong and Kwong, 2019). Similar problems exist with respect to the access to
archives, to which researchers in China are only admitted if the historical
information fits current political narratives (Roberts, 2018). These
developments are not limited to China, with for example the Russian State Duma
continuously issuing new laws that make collaboration and the exchange of
information between Russian and foreign researchers more
difficult,151515“Russian academics decry law change that threatens scientific
outreach”, Nature, 12.02.2021,
https://www.nature.com/articles/d41586-021-00385-5. a problem also faced by
scientists in Hungary (Enyedi, 2018) or Turkey (Aydin, 2021).

A common characteristic of all these censorship technologies is that they
impose taxes on information, by making accessing or sharing information more
costly. Although the primary aim of censorship is to limit anti-government
activities and collective action, one externality is thus that research is
becoming more costly. As shown in a seminal paper by Murray et al., (2016),
who study a natural experiment in genetics research, a reduction in openness
and available information can result in less innovation and a reduced
diversity of research output. We argue that these results can also be
translated to the effect of censorship in authoritarian states, which –
through imposing taxes on information – often has a significant negative
effect on scientific research and innovation. In a study of China’s higher
education sector, Schulte, (2019) makes precisely this point, by arguing that
novel combinations – or innovations – need a diverse and open academic
environment to emerge. Such an environment does only exist to a limited extent
in China, where the attempt by the state to impose authoritarian stability may
be in conflict with the objective of becoming an innovative knowledge economy
(Cao et al., 2009; Schulte, 2019).

###  3.2 Surveillance

While imposing taxes on information is one important channel through which
authoritarian institutions can negatively affect innovation, another channel
is surveillance. Here the effect is however less straightforward. On the one
hand, surveillance can increase performance, when monitoring is inducing
researchers to exert more effort. As we discuss in more detail in section 4,
surveillance can also generate data, which – under specific circumstances –
can be used as input to accelerate research, especially in the field of deep
learning.

However, surveillance can also have a negative effect on creativity. This
trade-off is important for the AI race between China and the US (see section
1), with the outcome of the race depending on the still open question which
type of research will ultimately be more important in artificial intelligence.
While some observers argue that big breakthroughs like the deep learning
revolution are rare, and that for the foreseeable future it is the amount of
available data and the resources invested in research that will determine who
will win the race (Spitz, 2017; Sun et al., 2017; Lee, 2018), it is also
conceivable that another breakthrough will change once more the fundamentals
in the field, leading to a new paradigm. In section 4, we argue that China has
an advantage with respect to the amount of data and the ability to induce
effort for average-quality researchers. In this section, we argue that with
respect to creativity, however, China’s authoritarian institutions impose a
tax that is similar in its negative impact to the tax on information described
in section 3.1.

To illustrate what we mean by creativity, think of a maze that is
characterized by having one entrance but many exits, and thus many different
potential paths to take (Amabile, 1996). When trying to find an exit, some
solutions can be more elegant, or more creative, than others. In the face of
extrinsic reward structures or surveillance, the narrowest and quickest path
will be chosen to get out of the maze, and “all behavior is narrowly directed
toward attaining that goal” (Hennessey, 2003, page 262). In order to find a
creative solution, however, immersion with the maze itself, experimentation
with alternative paths, and focusing attention towards seemingly incidental
aspects become important (Hennessey, 2003). Without intrinsic motivation to do
the task at hand, and with an external target goal in mind, individuals will
most likely rush towards the end of the maze. As Reiss and Sushinsky, (1975)
have shown, evaluation competes with task enjoyment, and attention diffuses
away from the task to the fact of being evaluated. Or as Hennessey, (2003,
page 262) puts it: “when individuals are distracted by their excitement about
a soon-to-be delivered prize or their anxiety surrounding an impending
evaluation, their intrinsic motivation and enjoyment of the task at hand are
undermined and they rush through their work as quickly as possible.”
Surveillance can thus lead to faster results, but at the price of less
creative solutions, and a lower probability that a new fundamental
breakthrough is discovered.

Ecosystems of Surveillance The existence of so-called “ecosystems of
surveillance” is one aspect in which authoritarian and democratic political
systems do fundamentally differ. Although surveillance does also exist in
democracies,161616In Western democracies, digital surveillance technologies
are being used for example to deliver targeted campaign adds during election
campaigns in what Tufekci, (2014) calls “computational politics”, or when
personal data is used for commercial purposes in what has become known as
“surveillance capitalism” (Zuboff, 2019). researchers in authoritarian systems
face, on average, more surveillance both in their daily lives, and in their
activity as researchers (Schulte, 2019; Jiang, 2020; Strittmatter, 2020;
Perry, 2020; Xu, 2021). The importance of surveillance in authoritarian states
can be explained by the informational dilemma of the authoritarian government.
As a result of censorship, media control and the absence or manipulation of
elections, the regime does not know the true sentiments of its citizens
(Edmond, 2013; Egorov and Sonin, 2020; Xu, 2021). As a consequence, the
efficient allocation of resources to co-opt regime opponents remains
impossible, as the regime is uncertain which actors require co-optation, and
which actors might be better controlled through repression. Such targeted co-
optation or repression is however necessary, as large-scale mass repression is
only rarely used in contemporary dictatorships (Guriev and Treisman, 2019; Xu,
2021), partially because of the disadvantages of international backlash in a
globalized economy, but also because visible repression can signal that the
regime is weak (Guriev and Treisman, 2020).

New computational methods and the ability to infer new information from
existing data with the application of AI technologies have now led to a
dramatic increase in digital surveillance capabilities (Edmond, 2013; Tufekci,
2014; Feldstein, 2019), which are increasingly being used by authoritarian
regimes. One such technology are social credit systems. While the best known
system is currently being introduced in China, the technology is also exported
by China to other authoritarian states (Feldstein, 2019). The stated objective
of China’s social credit systems is to assess the “trustworthiness” of
individuals by taking into account personal, financial, and behavioral data,
in order to foster social control and pre-empt social instabilities (Kostka
and Antoine, 2020).

In combination with the largest video surveillance network in the world, and
the collection of biometric information on individuals such as DNA samples and
fingerprints, the Chinese social credit system is in the process of becoming a
comprehensive database on hundreds of millions of individuals (Liang et al.,
2018; Qiang, 2019). The system is unprecedented in its scale and scope, with
the scale at which data are collected and used being made possible by the lack
of a legal system protecting personal data (Chen and Cheung, 2017). Since
March 2020, the development of the system has received an additional boost
through the compulsory introduction of tracing apps during the Covid 19
pandemic (Knight and Creemers, 2021).171717See also “China uses cover of Covid
to expand Big Brother surveillance and coercion”, The Times, 25.04.2021,
https://www.thetimes.co.uk/article/china-uses-cover-of-covid-to-expand-big-
brother-surveillance-and-coercion-ndpz3klmw. At least until today, the system
also seems to benefit from high levels of social approval, with various
studies finding that Chinese citizens are indifferent to, or even in favor of
this kind of digital surveillance (Kostka, 2019; Kostka and Antoine, 2020; Su
et al., 2021; Xu et al., 2021).

As we will show in section 4, this extensive reservoir of personalized data
can play a substantial positive role for the further development of AI
technologies, should the Chinese government decide to make it systematically
available to AI firms in the country. The Chinese social credit system is
however also putting into place a massive surveillance infrastructure,
providing the Chinese state with the technical ability to tightly control
every aspect of the life of its citizens (Strittmatter, 2020; Werbach, 2022).
It is in the interest of the Chinese government to make this surveillance
infrastructure as visible and present as possible in everyday life. While
censorship becomes more effective the less visible it is (see section 3.1),
surveillance relies on its ubiquitous nature to be effective (Feldstein,
2019). Behavioral modifications, or “chilling effects” are created even in the
absence of physical violence, due to the omnipresence of surveillance and the
potential subsequent assessment of behavior (Feldstein, 2019; Kostka, 2019;
Kostka and Antoine, 2020). In line with Foucault, (1995), where the panopticon
disciplines inmates of a prison through a state of constant potential
observation, social credit systems can induce self-monitoring and adjustment
of behavior in the interest of the government (Kostka, 2019; Aho and Duffield,
2020).

As of yet, the social and societal implications of such a large-scale
surveillance project remain, however, unclear. In the next section, we will
argue that the omnipresence of surveillance can negatively affect creativity,
and through this, high-quality innovation.

Surveillance, Creativity and Innovation Creativity, that is the generation of
novel and useful outcomes and ideas (Amabile and Pratt, 2016), is an important
input for research and innovation (Anderson et al., 2014; Litchfield et al.,
2015; Acar et al., 2019). At the macro level, it is sometimes described as a
basic economic input shaping technological change (Mokyr, 1992; Attanasi et
al., 2020). Some scholars have even argued that creativity is more than just
an input to innovation, with the boundary between both concepts remaining
fluid (Anderson et al., 2014). Generally, however, creativity can be
understood as the generation of new ideas, while innovation is their
implementation (Acar et al., 2019).

Creative performance can suffer from a number of external constraints, such as
tight deadlines and time pressure (Acar et al., 2019; Hennessey and Amabile,
2010), evaluation and surveillance (Amabile et al., 1990), and other, related
factors (Acar et al., 2019). According to the componential theory of
creativity,181818For an overview of other theoretical perspectives on
creativity and innovation, see Anderson et al., (2014). in which domain-
relevant skills, creativity-relevant skills, and intrinsic task-motivation are
defined as the principal components of creative performance (Amabile, 1983,
1988; Amabile and Pratt, 2016), the main external influence on creativity is
channeled through intrinsic task-motivation.191919A related focus is proposed
by Cerasoli et al., (2014), who review 40 years of research on external
incentives and intrinsic motivation. While we focus on external constraints
and intrinsic motivation, Cerasoli et al., (2014, page 983) are in line with
our argumentation when they show that intrinsic motivation is a strong
predictor of performance, but suffers from “crowding out” when external
incentives are too directly tied to performance outcomes. To understand how
authoritarian surveillance affects creativity, one thus needs to understand
how it affects intrinsic motivation.

To better understand the psychological processes of intrinsic motivation, a
useful framework is provided by self-determination theory, or SDT (Deci and
Ryan, 1985; Ryan and Deci, 2019). According to SDT, humans have three innate
needs that determine motivation: the need for interpersonal relatedness, the
need for competence, and the need for autonomy (Ryan and Deci,
2000).202020Beyond intrinsic motivation, these basic psychological needs
appear to be essential for facilitating positive social development, personal
well-being, and the avoidance of psychopathology (Ryan and Deci, 2000, 2019).
While interpersonal relatedness refers to being affiliated with a group or
team, competence is the feeling of mastering a task, and autonomy refers to
having control over a situation. In the following, we discuss how in
particular the needs of competence and autonomy, and through them intrinsic
motivation, are affected by government surveillance.

According to SDT, intrinsic motivation can either be supported or undermined
by the context and external circumstances of a specific task (Ryan and Deci,
2000). In this respect, perceived levels of competence and autonomy play a
particularly important role (Deci, 1975; Deci and Ryan, 1985). Events such as
positive feedback, awards or communications which foster feelings of
competence can enhance intrinsic motivation. In a creativity-related
experiment with university students, Deci, (1975) for example finds that
positive performance feedback led to higher levels of intrinsic motivation,
whereas negative feedback decreased feelings of competence, and through this
intrinsic motivation and creativity.

According to Ryan and Deci, (2000, page 70), however, a sense of competence
alone will not enhance intrinsic motivation, “unless accompanied by a sense of
autonomy or […] by an internal perceived locus of control”.212121Other studies
relying on different theoretical frameworks identify a similar importance of
autonomy as catalyst to enable creative performance, see e.g. Li et al.,
(2018) In other words, in addition to a feeling of being competent, one also
has to perceive the ability to independently determine one’s actions, in order
for intrinsic motivation to play a role. In the literature, this is often
paraphrased as an internal, instead of an external locus of control.222222An
internal locus of control means that a person is autonomously able to control
her or his actions, while an external locus of control signifies that the
person’s actions are determined by an outside actor or institution.

In order to perceive the locus of control internally as opposed to externally,
the functional significance of an event is highly relevant. According to Deci
and Ryan, (1985), the way a person perceives a specific context can have two
functional aspects – either a controlling one, or an informational one. These
two aspects can be of different salience, i.e. one of them can dominate the
awareness of a person, and thus be more salient. A context is perceived as
controlling in nature if a person experiences pressure from outside to reach a
specific target or goal, resulting in an external locus of control (Koestner
et al., 1984). On the other hand, so-called “effectance-relevant” feedback,
i.e. feedback that is perceived as providing helpful information for the
achievement of a particular task, without limiting the autonomous functioning
of a person, is resulting in the locus of control being perceived as internal
(Koestner et al., 1984; Deci and Ryan, 1985). Here the effect on intrinsic
motivation is positive.

Experiments testing this theory have shown that imposing limits with the
intention to control negatively affect intrinsic motivation and creativity, as
opposed to imposing informational limits that provide feedback but are not
coercive in nature (Koestner et al., 1984)232323Here the difference between
controlling and informational limits refers to how an external constraint of
behavior is framed. When the activities of subjects were met with “shoulds and
musts”, their intrinsic motivation and creative performance were reduced.
Conveying the same behavioral constraint with compassion and without external
pressure had no negative effect on intrinsic motivation.. Similar effects were
found by Pittman et al., (1980), who look at verbal rewards that also reveal
an intention to control, or Lepper and Greene, (1975), Pittman et al., (1980)
and Plant and Ryan, (1985), who study the effects of video surveillance. Enzle
and Anderson, (1993) look at different forms of surveillance, and find that
surveillance with the intention to evaluate,242424A negative effect of
evaluative surveillance has also been identified by Froming et al., (1982),
who look at intrinsic motivation, and Amabile et al., (1990), who study
creativity. or to check that rules are being followed had a negative effect on
motivation. Surveillance where these intentions were not openly stated or
visible, but where the subjects suspected that either evaluation or distrust
was motivating the surveillance, had an even stronger negative effect on
intrinsic motivation. If, on the other hand, subjects were convinced that
surveillance was in place not to control but to assist them in their tasks,
the effect on intrinsic motivation was positive.

These results are highly relevant for our study. As Enzle and Anderson, (1993,
page 261) put it, “it is not surveillance per se that is important, but the
belief that the surveillant intends to exercise social control.” For now, it
seems that even though digital surveillance is becoming increasingly
omnipresent in China, it is not yet seen in a negative light by most Chinese
citizens (Kostka, 2019; Kostka and Antoine, 2020; Xu et al., 2021) – possibly
because gamification is diverting attention from the control aspects of
digital surveillance (Ramadan, 2018). If these sentiments remain stable or
will change in the future remains an open question. It is possible that once
the intention of the Chinese state to use surveillance for social control
becomes more visible, the negative effects on intrinsic motivation described
above become more salient as well.

Surveillance and Innovation in Chinese Academia In Chinese research
institutions and universities, this already seems to be the case. The Chinese
education system is characterized by high levels of performance-oriented
surveillance and control. Competition to pass the national college entry exam
is extremely high, and can often have a traumatizing effect on students who
participate (Howlett, 2021). As students and academics are seen as a political
“risk group”, political surveillance is especially intense in Chinese
universities (Yan, 2014; Shao, 2020), with the use of digital surveillance and
social credit systems being combined with personal monitoring by senior
researchers and students (Perry, 2020). Materialistic incentives and career
opportunities are offered with the specific intention to exercise political
control (Perry, 2020, page 14), while video surveillance in classrooms and the
ability of students to report professors in real-time via apps is used to
monitor teaching activities. Reports can lead to warnings, salary reductions
or dismissal (Perry, 2020, page 11).

This system of intense surveillance is used to tightly control potential
political activities of researchers and students in Chinese academia, while
also being used to incentivize researchers to follow research directions
indicated by the state. As a result, academic and scientific interests are
subordinated to social stability and the interests of the Communist Party
(Liu, 2017), with most university professors strictly following the party line
in their activities and research (Hao and Guo, 2016). This does not mean,
however, that the system is not able to generate high levels of scientific
output. On the contrary, during the last 10 years the overall number of
scientific publications has increased significantly in China.252525“China
declared world’s largest producer of scientific articles”, Nature, 18.01.2018,
https://www.nature.com/articles/d41586-018-00927-4.

Accordingly, our hypothesis – based on the discussion above – is that ceteris
paribus, the Chinese university and research system will for some time
continue to perform worse in terms of research quality than a university
system in a competitive democracy, as a result of the negative effects of
surveillance on intrinsic motivation and creativity. In terms of the overall
quantity of produced research, tight surveillance migth however offer an
advantage, as surveillance allows to incentivize researchers to exert effort.

For now, this hypothesis has not been tested empirically in any comprehensive
way.262626During the last 30 years, the Chinese bureaucratic system has proven
that it can very efficiently incentivize state officials to exert effort to
reach pre-determined performance criteria, such as economic growth (Li and
Zhou, 2005; Rochlitz et al., 2015; Jia et al., 2015; Yao and Zhang, 2015;
Libman and Rochlitz, 2019). Schedlinsky et al., (2020) however show
experimentally that even for relatively simple tasks, surveillance can reduce
the motivation and hence the effort exerted. Hence, more research is needed to
better understand how surveillance can affect performance with respect to
complex and less complex tasks in authoritarian environments, and if – for
example - a difference exists between performance in academic and scientific
environments, and performance in bureaucratic settings. It could, however,
provide an explanation why China is still lagging behind with respect to
research quality in the field of artificial intelligence, while it has already
overtaken all other countries in the world with respect to overall research
output in the field (as illustrated by Figure 1 in section 1). Table 1
provides additional evidence on this point, by showing how the top 20 research
institutions in the world with respect to research quality in AI (as measured
by the Nature Index of research output in applied artificial intelligence) are
still mainly located in the United States. When looking at the overall number
of publications in AI, however, 14 out of the world’s top 20 institutions are
already located in China (Table 2).

Table 1: Top 20 research institutions in artificial intelligence (Nature
Index)

Rank | Institution | Location | Share 2015–2019
---|---|---|---
1 | Harvard University | United States | 331,08
2 | Stanford University | United States | 257,9
3 | Massachusetts Institute of Technology (MIT) | United States | 209,04
4 | Max Planck Society | Germany | 167,98
5 | University of Oxford | United Kingdom | 132,34
6 | University of Cambridge | United Kingdom | 130,68
7 | Chinese Academy of Sciences (CAS) | China | 130
8 | University College London (UCL) | United Kingdom | 129,7
9 | Columbia University | United States | 127,56
10 | National Institutes of Health (NIH) | United States | 122,69
11 | New York University (NYU) | United States | 117,4
12 | University of Washington | United States | 110,68
13 | Princeton University | United States | 107,92
14 | University of California, Berkeley | United States | 107,54
15 | University of California, San Diego | United States | 100,63
16 | University of Pennsylvania | United States | 98,54
17 | Johns Hopkins University | United States | 85,8
18 | Yale University | United States | 83,95
19 | California Institute of Technology (Caltech) | United States | 83,59
20 | University of Toronto | Canada | 83,27
Note: Data come from the Nature Index on top-level publications in the field
of applied artificial intelligence, see:
https://www.natureindex.com/supplements/nature-index-2020-ai/tables/countries.

Table 2: Top 20 research institutions in artificial intelligence (Dimensions
Data)

Rank | Institution | Location | Publications in AI
---|---|---|---
|  |  | (2015 - 2019)
1 | Tsinghua University | China | 11,867
2 | University of Chinese Academy of Sciences | China | 8,835
3 | Shanghai Jiao Tong University | China | 8,796
4 | Beihang University | China | 8,605
5 | Zhejiang University | China | 8,337
6 | Harbin Institute of Technology | China | 8,315
7 | Nanyang Technological University | Singapore | 6,985
8 | University of Electronic Science and Technology of China | China | 6,954
9 | Stanford University | United States | 6,609
10 | Massachusetts Institute of Technology | United States | 6,314
11 | Southeast University | China | 6,18
12 | University of Tokyo | Japan | 6,058
13 | Huazhong University of Science and Technology | China | 6,048
14 | University College London | United Kingdom | 5,988
15 | Wuhan University | China | 5,797
16 | Beijing Institute of Technology | China | 5,641
17 | Peking University | China | 5,612
18 | Harvard University | United States | 5,553
19 | University of Michigan | United States | 5,464
20 | Xi’an Jiaotong University | China | 5,364
Note: Data come from the Nature Index Dimensions Database that counts all
publications in the field of
artificial intelligence between 2015 and 2019, see
https://www.natureindex.com/supplements/nature-
index-2020-ai/tables/dimensions-countries.

##  4 Authoritarian Surveillance and Big Data

Our overall hypothesis in this paper is that while the United States still
have an advantage with respect to research quality and creativity, China has
an advantage with respect to the number and motivation of average-quality
researchers and internet entrepreneurs, as well as with respect to the
quantity of data available for research. Accordingly, who can win the AI race
will ultimately depend on what will be more important in future research in AI
– big data or new ideas. In this section, we will first give an outline of the
symbiotic relationship between the Chinese state and private companies with
respect to the gathering of data, before looking at how this data can be used
to promote innovation.

###  4.1 Cooperation Between the State and the Private Sector

While sections 3.1 and 3.2 look from a political perspective at the landscape
of surveillance in China, this section takes the different stakeholders into
account, and shows how their cooperation permits the collection of large
amounts of data on Chinese citizens. In China, a handful of big tech companies
shape the digital environment, with Baidu, Alibaba and Tencent being the three
biggest players (Jia et al., 2018). They offer a vast array of services,
including online payment, e-commerce, social media, medical services, cloud
services, as well as all kinds of additional convenience apps, which permit
them to gather large amounts of data on their customers (Arenal et al., 2020).
They have also developed their own social credit systems, which combine and
bundle data gathered across different domains.

From the side of the government, smart cities with surveillance technologies
in the form of CCTV and a range of other sensors are being developed in
metropolitan areas, with the number of surveillance cameras installed in China
having increased almost exponentially in recent years.272727According to a
recent report by China File, central and local Chinese governments spent $ 2.1
billion between 2016 and 2020 to buy surveillance cameras, “State of
Surveillance: Government Documents Reveal New Evidence on China’s Efforts to
Monitor Its People”, China File, 30.10.2020, https://www.chinafile.com/state-
surveillance-china. The Chinese state also places a high priority on
technologies like the internet of things that are relevant for smart cities,
as they facilitate economic growth but also render censorship and surveillance
more effective (Kshetri, 2017).

Similar trends are observable for the AI sector as a whole, where the state
takes a leading role in financing AI development, buying AI solutions and
determining strategies for future development (Ding, 2018; Arenal et al.,
2020; Righi et al., 2020). Through this kind of symbiosis, data are shared
between public and private domains, for example in the financial sector
through Alibaba’s social credit system “Zhima score”. Besides transaction data
from more than half a billion users in Alipay, Zhima score has also access to
external government data derived from interactions between individuals and
public services (Arenal et al., 2020). The use of facial recognition software
to identify deception in potential borrowers is another example from the
financial sector where the state-owned Bank of China and Tencent cooperate
(Kshetri, 2020). Other projects include the sharing of legal data on criminals
between the authorities and private companies that provide digital service
solutions such as video identification systems or remote trials on social-
media (Arenal et al., 2020). In an attempt to automate legal decision making,
courts purchase AI-powered solutions from tech companies for gathering data on
court decisions (Stern et al., 2021). This data is subsequently made public in
markets where companies and courts trade raw and repacked data, for research
and commercial purposes. Additional examples include the use of facial
recognition technologies to identify jaywalkers, or smart glasses for the
police, with the technology being developed by private companies that also
benefit from shared data to further improve their systems (Arenal et al.,
2020).

The sharing of large amounts of data between the state and the private sector
is made possible by data privacy regulations that are much weaker than in
Europe or the US (Wu et al., 2011; Kshetri, 2014). In particular with respect
to data sharing, Chinese companies are allowed to operate with very few
regulations (Jia and Kenney, 2016). This is being facilitated by consumers
being less concerned about data privacy in China than in many other countries
(Kshetri, 2017).

Although the Chinese government has recently started a crack-down on the
country’s big tech companies, this does not mean that the symbiotic
relationship between the private sector and the state is about to end. Rather,
it can be understood as a signal by the Communist Party to the country’s
leading tech companies, to show who ultimately remains in charge.282828“What
China Expects from Businesses: Total Surrender”, The New York Times,
19.07.2021, https://www.nytimes.com/2021/07/19/technology/what-china-expects-
from-businesses-total-surrender.html.

###  4.2 Big Data and Innovation in AI

According to Lee, (2018, page 14), at the current stage of AI research
“successful AI algorithms need three things: big data, computing power, and
the work of strong – but not necessarily elite – AI algorithm engineers.
Bringing the power of deep learning to bear on new problems requires all
three, but in this age of implementation, data is the core. That’s because
once computing power and engineering talent reach a certain threshold, the
quantity of data becomes decisive […].”292929See also Halevy et al., (2009)
and Sun et al., (2017) on the role of big data in deep learning, especially
with respect to vision tasks and facial recognition.

Beraja et al., (2021) show that data from the symbiotic relationship between
private firms and the Chinese state is already today having a measurable
positive effect on the number of commercial AI innovations in China, with the
positive effect being larger than the potential crowding-out effect resulting
from state intervention. The provision of government data to fuel private-
sector innovation can thus be seen as an industrial policy tool used by the
Chinese state, with the data gathered through surveillance being used as
subsidy.

For the moment, the positive effects of this symbiotic relationship are mostly
concentrated in the field of surveillance technologies (Xiang, 2019; Beraja et
al., 2021), where China has already today gained world leadership, and is
exporting its technological solutions to other countries (Feldstein, 2019). In
other sectors, China is not quite there yet, with various institutional and
technological factors hindering the efficient sharing of data between
government institutions and private companies (Xiang, 2019). In the health
sector, for example, millions of personalized dossiers already exist, but
implementation and data use are still hampered by institutional deficiencies
(Zhang et al., 2020). Similar problems exist with respect to automated court
decisions, where insufficient analytical capabilities hinder the effective
implementation of the system (Stern et al., 2021). Even facial recognition is
still suffering from implementation problems, with the efficiency of AI-
automated CCTV applications remaining limited by various hard- and software
problems (Peterson, 2020).

Nevertheless, already today China is a leader in combining datasets from
different sources and making them available for research institutions and
private companies, at a much larger scale than other countries. Once these
massive datasets are available, their use as input for deep learning
algorithms can have significant transformative potential for various sectors
(Qiu et al., 2016). If one considers China’s population size, it extensive and
fast evolving surveillance infrastructure, as well as the fact that the
country only seriously embarked on using deep learning technologies a couple
of years ago, the potential for future development is vast. If it can outweigh
the negative effects of surveillance and control on creativity and innovation
remains to be seen, but is definitely a possibility.

##  5 Conclusion

In this paper, we examine the implication of a new technology – deep learning
– on the effect political institutions have on innovation. Traditionally,
authoritarian political institutions were seen as having a mostly detrimental
effect on innovation, as they block the free flow of information, and hinder
researchers to creatively explore new ideas. In section 3, we show that these
negative effects still exist today. In authoritarian or semi-authoritarian
countries such as China, Russia, Turkey or Hungary, the free flow of
information is disrupted by various forms of censorship, and researchers face
substantial pressure and control from the side of the state. We discuss a
large theoretical and empirical literature from the field of psychology – in
particular works related to self-determination theory (Deci, 1975; Deci and
Ryan, 1985, 1987, 1990) – to show how the attempts of a government to exercise
social control can negatively affect intrinsic motivation and creativity. In
particular in view of the recent initiative by the Chinese state to introduce
a large-scale system of digitized social surveillance, these studies have
again become highly relevant. They show that although China and other
authoritarian states often have ambitious strategies to foster innovation and
build knowledge economies, their authoritarian institutions can act as a
significant obstacle and stumbling block in this regard.

We then introduce a novel hypothesis, by arguing that with respect to research
in artificial intelligence and in particular deep learning, the negative
effects of censorship and surveillance might be attenuated – or even
outweighed – by the positive effects of having large amounts of data
available. As we outline in section 4, due to extensive government
surveillance, lax data privacy rules and the bundling and sharing of data
between state and private actors, in some sectors such as for example facial
recognition, research institutions in China have already today access to much
larger and better datasets than their competitors in democratic countries. As
data are the most important input in this type of research (Halevy et al.,
2009; Sun et al., 2017), the potential positive effects on future innovation
might be substantial.

For now, empirical research on these questions remains scarce. Beraja et al.,
(2021) are one of the few studies that examine in a rigorous empirical setting
how the sharing of data between private and government institutions affects
innovation in AI in China, with the authors finding a significant and positive
effect for the sector of surveillance technologies. To understand how deep
learning technologies as well different institutional approaches with respect
to data-sharing and surveillance might affect the race for leadership in
artificial intelligence, we would however need much more empirical and
theoretical research. Here two lines of research are of particular importance
– how political institutions affect intrinsic motivation, creativity and
innovation in contemporary academic institutions, and how the role played by
big data is driving innovation in artificial intelligence. We hope that this
study can provide a first building block for a new research agenda in this
direction.

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