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Artificial Intelligence, Surveillance, and Big Data

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Diginomics Research Perspectives

Part of the book series: Advanced Studies in Diginomics and Digitalization ((ASDD))

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. This chapter examines 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 case of China, 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 USA and China.

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Notes

  1. 1.

    While 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 250150, or 10360 possible moves—more than the number of atoms in the observable universe.

  2. 2.

    Lee (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 did not quite rival America’s reaction to Sputnik, but it lit a fire under the Chinese technology community that has been burning ever since”.

  3. 3.

    As 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.

  4. 4.

    A 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.

  5. 5.

    See also “The Panopticon is Already Here”, The Atlantic, September 2020, https://www.theatlantic.com/magazine/archive/2020/09/china-ai-surveillance/614197/.

  6. 6.

    As China does not have elections and is still using repression, it is sometimes not seen as a classic “informational autocracy” as invoked by 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.

  7. 7.

    Whereas 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.

  8. 8.

    The 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.

  9. 9.

    See 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.

  10. 10.

    A detailed discussion of this trade-off for the case of Russia and China can be found in Libman and Rochlitz (2019), chapter 4.

  11. 11.

    The Streisand effect occurs when an effort to conceal information increases its value or makes it more attractive (Hobbs and Roberts 2018). The effect is named for Barbra Streisand, who when trying to remove pictures of her home from the internet attracted even more attention to them.

  12. 12.

    In 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.

  13. 13.

    According 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/).

  14. 14.

    “Russian academics decry law change that threatens scientific outreach”, Nature, 12.02.2021, https://www.nature.com/articles/d41586-021-00385-5.

  15. 15.

    In Western democracies, digital surveillance technologies are being used for example to deliver targeted campaign ads 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).

  16. 16.

    See 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.

  17. 17.

    For an overview of other theoretical perspectives on creativity and innovation, see Anderson et al. (2014).

  18. 18.

    A 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.

  19. 19.

    Beyond 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).

  20. 20.

    Other 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).

  21. 21.

    An 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.

  22. 22.

    Here the difference between controlling and informational limits lies in 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.

  23. 23.

    A 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.

  24. 24.

    “China declared world’s largest producer of scientific articles”, Nature, 18.01.2018, https://www.nature.com/articles/d41586-018-00927-4.

  25. 25.

    During 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 (Jia et al. 2015; Li and Zhou 2005; Libman and Rochlitz 2019; Rochlitz et al. 2015; Yao and Zhang 2015). 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.

  26. 26.

    According to a recent report by China File, central and local Chinese governments spent US$ 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.

  27. 27.

    “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.

  28. 28.

    See 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.

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  1. Faculty of Business Studies and Economics, University of Bremen, Bremen, Germany

    David Karpa, Torben Klarl & Michael Rochlitz

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    Lars Hornuf

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Karpa, D., Klarl, T., Rochlitz, M. (2022). Artificial Intelligence, Surveillance, and Big Data. In: Hornuf, L. (eds) Diginomics Research Perspectives. Advanced Studies in Diginomics and Digitalization. Springer, Cham. https://doi.org/10.1007/978-3-031-04063-4_8

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