China’s Social Credit System: How the Three Actors of the ‘Surveillance Triad’ Contribute to the Construction of a Quantified Self

Research Master Media Studies
January 23, 2017

Introduction

In the Fall of 2015, the announcement of the implementation of a Social Credit System in China created a buzz in Western newspapers and (online) magazines. Although the Chinese government already released an official issuance about this upcoming system in the Summer of 2014, it gained noticeable attention when an English translation of this document by China specialist Rogier Creemers of Oxford University was published on his website China Copyright and Media. The Social Credit System (SCS) resembles financial credit systems which are used for determining someone’s creditworthiness: whether someone is likely to pay back a loan. In an interview with de Volkskrant, Rogier Creemers explains that the SCS would take this type of monitoring a step further. With the help of the latest internet technologies, the Chinese Communist Party seeks to move beyond fighting fraudulent (financial) practices. In addition, the SCS would also actively promote ‘socialist core values’ among citizens, such as patriotism, respecting the elderly, working hard and avoiding extravagant consumption. A bad social credit score could result in being ineligible for certain jobs or applying for housing credit (Obbema, Vlaskamp and Persson).

The Social Credit System received attention again after the season premiere of the popular techno-dystopian Black Mirror series’ third season in the Fall of 2016. In “Nosedive”, protagonist Lacie Pound (portrayed by Bryce Dallas Howard) lives in a world where both friends and strangers can rate your popularity (measured in stars) with the help of technology in smartphones and standard smart lenses that show everyone’s name and social rating (Figure 1). In order to be able to live in an exclusive state, the protagonist needs a 4.5 star rating out of 5. With her current rating stuck on 4.2, she needs to interact and gain positive ratings of other people with high social scores. This quantified social status, resembles for some commentators what China’s SCS might look like when fully implemented in 2020 (Nguyen).

Considering that the SCS has not been implemented yet, to envision what it might look remains in the domain of fiction at the moment of writing this paper. For that reason, no academic articles have yet been published about the SCS according to the knowledge of the author. However, the topic of surveillance of citizens through Internet technologies has received considerable attention in a range of academic fields. From a humanities perspective, one can ask what kind of online identities are being constructed through digital surveillance. In recent academic scrutiny, the term ‘quantified self’ has been coined, to describe such identities, referring to the use of quantitative measures (large scale data collection) that are involved in online identity construction.

On a metalevel, typically three main actors are involved in such online identity construction: governments that seek to surveil their citizens through data collection, citizens who use digital communication platforms (users-citizens), and corporations that manage those platforms and thus provide the possible gateway for governments to surveil. In what follows, this paper will shed light on the question how governments, users-citizens and corporations contribute – in the context of China and its forthcoming SCS – to the construction of a quantified self. This inquiry relates to the larger interdisciplinary debate about what happens to the notion of citizenship in the context of increasing data capture and surveillance of citizens by governments.

Figure 1 – Still from protagonist Lacie Pound (Bryce Dallas Howard) in Black Mirror’s “Nosedive”, showing her social rating.

The Surveillance Triad

As mentioned here above, this paper will address three main actors in the online identity construction of a quantified self: governments, users-citizens, and corporations. Of course, one could argue that such an enumeration is not thorough enough and other actors should be included. Unfortunately, that would not fit the scope of this paper. Especially ‘activists’, would constitute a logical fourth actor in the theorization of online identity construction in the context of China’s SCS. However, for the sake of clarity, activists are grouped with users-citizens in this paper in the discussion of the SCS here below. In what follows, the majority of sources theorize surveillance practices in the western context, mainly focusing on the United States specifically, after Edward Snowden’s revelations in the Summer of 2013 of mass scale data capture by the National Security Agency (NSA). This paper builds on these sources to create a theoretical model that is used to scrutinize China’s SCS as a case study. Through the use of this model, with its theoretical foundations derived from sources that target the western context, differences between the western and Chinese context with regards to online identity construction of a quantified self can be distilled.

The model to study the online identity construction of a quantified self proposed here is called The Surveillance Triad (Figure 2).

Figure 2 – The Surveillance Triad

In the Surveillance Triad, the three main actors in online identity creation – governments, corporations and users-citizens are shown. What is illustrated by the triad, is the relationships and complex interplay between the three actors. The users-citizens angle of the triad is deliberately positioned at the bottom, because as will become clear in this paper, that the agency of this actor is considerably smaller compared to governments and corporations. The area between the different actors is literally grey, because the domain of online identity construction as a result of surveillance is a grey area and a continuous field of contestation. In the remainder of this theoretical discussion, the three relationships – governments vis-à-vis corporations, corporations vis-à-vis users-citizens and governments vis-à-vis users-citizens that are portrayed in The Surveillance Triad, will be elaborated on. Of course, these relationships are not distinct entities but interconnected parts of the field of the online identity construction of a quantified self through surveillance practices.

Governments vis-à-vis corporations

On the 6th of June 2013, former N.S.A. employee Edward Snowden revealed an item in The Guardian, showing that the Foreign Intelligence Surveillance Court (FISC) had ordered telecommunications giant Verizon to hand over metadata from millions of telephone calls to the NSA. This revelation shows that governments, such as the American government, engage in astonishingly large scale data collection of populations (users-citizens) and how they do it (Lyon 2). In this case, through a court order that obligates a communication corporation to hand over metadata. The next day, also in The Guardian, the PRISM program was revealed, a program designed to give the NSA direct access to the servers of some of the biggest technology companies such as Apple, Facebook, Google, Microsoft, Skype, Yahoo and YouTube (ibid.). President Barack Obama responded to the revelations, by stating that ‘no content, just metadata’ were involved in the data capture by the NSA (Van Dijck 197). Metadata can somewhat ambiguously be described as ‘the data about data’, such as IP addresses, the identity of a contact, the location of a call or message for instance (Lyon 3).

According to José van Dijck, “metadata appear to have become a regular currency for citizens to pay for their communication services and security – a trade-off that has nestled into the comfort zone of most people” (198, italics in original). Van Dijck attributes this normalization of data collection to a growing paradigm in society, one of datafication. Datafication refers to the transformation of social action into online quantified data (ibid.). Every click on a link, every Facebook like on a page, every retweet, all of them are being transformed into data. This data is often sold to third-party advertisers who get the possibility to target consumers more specifically (Lyon 3). This is probably not a secret anymore for most Internet platform users, but the revelation that governments also gain access to users-citizens data and that corporations help them to this data was quite striking for a large amount of users-citizens for whom it was a wake-up call (Van Dijck 197).

The Snowden revelations show that corporations and the government of the United States form an excellent match where it comes to data surveillance of users-citizens. Internet giants who have access to enormous amounts of users-citizens data provide a possible gateway to surveil for governments. When these metadata are being captured and made accessible for governments, the social interactions of users-citizens become datafied, contributing to a form of quantified self. Even though it is ‘just metadata’ that is being collected, this form of data does map out who or what users-citizens are, often without their knowledge or aware consent (Lyon 3).

Corporations vis-à-vis users-citizens

In the previous section, that illustrated the relationship between governments and users-citizens, users-citizens were already part of the equation. However, in terms of the corporations-users-citizens relationship, the latter seem to have less problems with corporate data capture than government surveillance. As already mentioned here, data has become a ‘natural’ currency to pay for Internet platforms, not in the least because these platforms are ‘free’ to use and have to be paid for somehow.

This argument is backed up by a modest qualitative study of Kirsty Best, who finds that technology users remain relatively undisturbed by privacy issues, accepting for instance greater usability of Internet platforms in return (6). The majority of her respondents were ‘not bothered’ by surveillance, and if aware of the possible dangers of surveillance felt that they were too small to matter (Best 11- 12). What is noticeable is that the respondents perceive surveillance as something that happens on an individual level, instead of something that targets whole populations (Best 14). In addition, the respondents of Best’s study often thought of surveillance in terms of financial information or fraudulent practices with regards to financial transactions (15-16). Best’s study illustrates a double individual perception of surveillance practices. Individual where it comes to the ones surveilled, and individual with regards to the one engaging in surveillance. An identity thief or financial forger who uses data to its own advantage.

However, as noticed earlier, the Snowden revelations have made users-citizens increasingly aware of surveillance practices by governments through corporate Internet platforms and questioned this lack of privacy. In response, some Internet companies such as Google and Facebook filed court cases against “N.S.A.-bullying tactics” to regain consumer trust (Van Dijck 204).

Furthermore, in the beginning of 2016, Apple declined when asked by a federal judge to help unlocking an iPhone belonging to Syed Farook, who was responsible for a mass shooting in San Bernedino in December 2015 which left fourteen people dead (Kharpal). Apple declined because helping crack the iPhone would set a precedent and giving the FBI access to hundreds of millions of other smartphones.

The examples of corporations resisting government capture of users-citizens data arguably show a form of user agency in the context of online surveillance. For economic reasons, it became far less attractive for large Internet corporations to hand over users-citizens data to governments, because they could risk losing a consuming audience. After the Snowden revelations giant Internet corporations in the United States have moved from being perfect gateways to users-citizens data to apostles of privacy concerns and civil rights.

Users-citizens vis-à-vis governments

In the previous section it became clear that users-citizens often perceive surveillance as something that targets individuals. However, academic scrutiny in the wake of Michel Foucault focusses on populations in this respect. He argues that the discovery of the population brought about a shift in how subjects were controlled in societies in the west (Foucault 161). A population, as collection of biological entities has amongst other aspects a birth rate, a mortality rate, a life expectancy, all aspects that can be controlled and should be disciplined to use “this population as a machine for producing, producing riches, goods, producing other individuals” (ibid.). This is what Foucault terms bio-politics.

In order to successfully use bio-politics, a “whole series of techniques of observation, including statistics, obviously, but also all the great administrative, economic and political organisms, are charged with this regulation of the population” (ibid.). Exactly this focus on population statistics or users-citizens data in the digital age is what makes Foucault’s work on bio-politics relevant for surveillance studies and the relationship between governments and its subjects (users-citizens) with regards to the construction of online identities.

In an advancement of Foucault’s work in the context of online identity construction, John Cheney-Lippold nuances the notion of bio-politics by distinguishing between two types: hard bio-politics and soft-biopolitics (173). The former is concerned with the regulation of populations through the use of categorizations, while the latter regulates these categories themselves (Cheney-Lippold 174). In short: Cheney-Lippold urges us to focus not only on the political regulation of populations through the use of categories, but to zoom in closer on how those categories are formed. He argues that through online data capturing and algorithmic suggestion of Internet platforms algorithmic identities are being constructed (Cheney-Lippold 165). Every interaction online – whether a click on a link or a Facebook like – gives rise to new suggestions determined by algorithms that ‘learn’ from a user’s interaction. A prominent example is the ‘You might also like…’ (based on your previous purchases) section on a site such as Amazon. Quite harmless as that may seem, the information gateway that Internet giants provide potentially is not. Indeed, when taking into account the access of governments, the mass scale amount of data can influence the construction of identity online, which is called bio-politics. With his focus on soft-biopolitics, Cheney Lippold highlights the categorization of online identities. These categories that shape online identities become “more opaque and buried, away from our individual vantage points and removed from most forms of critical participation. They are increasingly finding mediation outside the realm of traditional political intervention and inside the black boxes of search engines and algorithmic inference systems” (Cheney-Lippold 176).

Cheney-Lippold suggests that users-citizens lose control over determining who or what they are online, because the categorization of identity construction happens outside their scope. In the next section this paper will argue that disciplining through bio-political measures increasingly happens through self-disciplining, by drawing on the concept of the quantified self.

The quantified self

In her book The Quantified Self Deborah Lupton provides a sociology of self-tracking, focusing on a number of means of monitoring and measuring elements of everyday life and embodiment, with the help of mobile and wearable devices (3). However, it can be interpreted more broadly as an ethos and apparatus of tracking yourself and self-optimizing. In contemporary western societies, with the accessibility to a large set of data about oneself, the care of the self has become an ethical project, “which requires a self-awareness based on critical and considered reflection and the acquisition of self-knowledge as part of achieving the ideal of the ‘good citizen’ – that is, a citizen who is responsible, capable and self-regulated in the pursuit of happiness, health, productivity and wellbeing” (Lupton 48). In the context of the availability of all this data about oneself, building on the self has become a project of its own. “Expectations from people that they engage in self-optimisation have led to such practices becoming more accepted instead of being viewed pejoratively, as ‘vain’. Such practices of selfhood are now frequently represented as expected from people and as part of their achieving their ‘best selves’ and behaving as responsible citizens, engaged in self-care” (Lupton 49). A couple of decades back, spending great amounts of time on one’s body shape would have been socially awkward, nowadays it seems that everybody is hanging around in the gym. Of course, this idea of self-optimisation does not limit itself to health, but a social media profile should also be seen as a form of quantified self, under constant surveillance of one’s social network.

In what follows, this paper will focus on how a quantified self is constructed in the Chinese context, with the implementation of a Social Credit System (SCS), by focusing on how the three main actors of The Surveillance Triad contribute to such a construction.

China’s Social Credit System

At face value, the Social Credit System that is announced by the Chinese government is a system that keeps tabs on users-citizens with a social credit score. With this social credit score, users-citizens are urged to behave in a way that conforms with socialist core values. In the translation of the official government issuance by China specialist Rogier Creemers the opening statement is as follows:

A social credit system is an important component part of the Socialist market economy system and the social governance system. It is founded on laws, regulations, standards and charters, it is based on a complete network covering the credit records of members of society and credit infrastructure, it is supported by the lawful application of credit information and a credit services system, its inherent requirements are establishing the idea of a sincerity culture, and carrying forward sincerity and traditional virtues, it uses encouragement to keep trust and constraints against breaking trust as incentive mechanisms, and its objective is raising the honest mentality and credit levels of the entire society.

As becomes clear in the opening statement, the SCS is an attempt to create a ‘complete network’ of China’s users-citizens’ credit records. To foster such a network enormous amounts of data would have to be gathered and automatically processed by algorithms to determine social credit scores. The fact that the Chinese government wants to keep track of its users-citizens is not new in itself, argues Creemers in a column on the website of Foreign Policy. Before the Internet-era, the ‘personal file’ (dang’an) system also was a form of public keeping of ‘performance and attitudes’ of citizens. What is new according to Creemers, is that the Chinese government is now convinced that with the help of surveilling technologies and Big Data such a system can actually be implemented. Even though the system is not (yet) in place, there is already room for studying how the main actors of The Surveillance Triad – governments, corporations and users-citizens – and the relationships between them contribute to the construction of a form of quantified self in the context of China’s SCS.

The Chinese government vis-à-vis Chinese technology corporations

As became clear in the illustration of the government-corporations relationship above, governments depend on corporations as gateways to users-citizens data. In the context of the United States, Verizon was ordered to make telephone calls data accessible to the government.

The Communist Party of China has started to endorse eight different technology corporations in setting up credit databases that compile a wide range of online, financial and legal information. One of the most popular is Sesame Credit, which is part of Alibaba, the e-commerce corporation that runs the world’s largest online shopping platform (Denyer). Through Sesame Credit, tens of millions of users with high scores have been able to rent cars and bicycles without leaving deposits. Also in the realm of dating, this Sesame Credit is used, by the dating website Baihe. The site encourages users to display their Sesame Credit scores in order to attract potential partners. At the end of October, 15% of users- citizens did so (ibid.). At the moment, Chinese technology corporations such as Alibaba have picked up the government’s endorsement to implement a social credit score to their service. In the Chinese context, it is not unthinkable that corporations in the future will be made to implement such systems in order to remain open for business.

But what happens to the construction of identity in a SCS where the Chinese government and technology corporations become increasingly intertwined? When invoking Cheney-Lippold’s idea of soft-biopolitical categorization in this case, to be a ‘good’ citizen is to be creditworthy. An increasing amount of activities that used to lie outside the realm of financial credit become part of the creditworthiness of users-citizens. Social data, possibly scrawled from social media in the future for instance. The social and the financial become increasingly linked with a SCS and other activities outside the scope of data capture, such as offline activities are not being measured. From this point of view, it is paradoxical to say the least that the Chinese government expects the SCS to promote socialist core values, while at the same time someone’s creditworthiness becomes the predominant asset of a user-citizen.

Chinese technology corporations vis-à-vis Chinese users-citizens

From the small qualitative study by Kirsty Best discussed earlier in this paper it became clear that western users-citizens tend to view data as regular currency to pay for the use of Internet platforms. Users-citizens often see themselves as ‘too small to matter’ and think of data capture in terms of financial fraud targeted at an individual level, instead of an automated process structured by algorithms. However, the Snowden revelations about mass scale governmental surveillance have been a wake-up call for users-citizens. Through the scandal it caused, American tech corporations needed to position themselves differently towards government intervention to regain consumer trust.

In China, to the knowledge of the author, such a scandal has not taken place with regards to China’s SCS, yet. The first government endorsed projects, such as Alibaba’s Sesame Credit are in place, but at the moment it is still unclear whether Chinese users-citizens will critically ask questions about the massive collection of their data. However, as the Baihe dating site usage of Sesame Credit shows, users- citizens already take it up to themselves to offer their data in return for a good credit rating and a higher chance on a suitable partner. Here it is not hard to see the soft-biopolitical categorization at work. With the availability of Sesame Credit on a site such as Baihe, users-citizens are not made to include such a credit ranking, but in a biopolitical way urged to take it up themselves. Why would you not show your Sesame Credit, what do you have to hide?

Chinese users-citizens vis-à-vis the Chinese government

As mentioned above, so far there are no known users-citizens outcries against corporate implementations of social credit in Chinese Internet platforms. At the beginning of this paper, it was argued that for the sake of clarity an important actor in the construction of online identities, activists, are grouped with users-citizens, even though there is a distinct difference in engagement levels between activists and the average user-citizen. It should also be noted that the possibility for activism in China is considerably lower in comparison with the western context, from which the Surveillance Triad is derived, because dissident voices are often quickly silenced.

With respect to the announcement of the SCS, activist and dissident Hu Jia, who won the Human Rights Prize of the European Parliament in 2008, has made his comments. “On the outside this system may seem like a way to promote trust and credit worthiness and is supposed to be progressive, … [b]ut in an authoritarian state, there is no limit to what they have access to, and there is no law to protect those that are harmed” (Clover). Hu Jia points out a key aspect of the relationship between the Chinese Communist Party and Chinese users-citizens: the underlying aims of the government’s implementation of the SCS are not yet clear to China’s users-citizens. But when such a system would be in place, the possibilities for disciplining subjects could of course be endless, particularly because China has an authoritarian government.

The soft-biopolitical categorizations of what it means to be a ‘good citizen’, also in the Chinese context increasingly take place outside the scope of users-citizens perception, just as Cheney-Lippold argued with regard to algorithmic black boxes in the western online sphere. When it comes to algorithmic calculations of social credit, it seems unlikely that users-citizens will know how their data is gathered. What kinds of behavior will contribute to a good or a bad rating, however, will probably be propagated in all openness, in order to enforce the desired behavior of Chinese users-citizens. Here the difference between hard and soft-biopolitics becomes clearer in the Chinese context. The hard-biopolitical categories are out in the open to maximize the effect of its disciplining character, while the soft-biopolitical categorizations or the algorithmic computations that determine how these categories are constructed happen in a datafied, black-boxed context.

Conclusion

At the beginning of this paper, it was asked how the three main actors in online identity construction – governments, corporations and users-citizens – contribute to the formation of what has been called a quantified self. By drawing on literature that focuses on western online phenomena, a theoretical model – The Surveillance Triad – was established and uses to scrutinize the forthcoming implementation of a Social Credit System in China. From the analysis of China’s SCS through this model it became clear that the model itself needs to be adapted for the Chinese context. In this context, the government has a stronger position in relation to corporations compared to the western context. Therefore, the Surveillance Triad could be modified as shown in Figure 3. Arguably, the model could also look like a pyramid with ‘Governments’ at the top, but such a model would fail to illustrate the stronger position of ‘Corporations’ vis-à-vis ‘Users-citizens’.

Figure 3 – The Surveillance Triad, adapted to fit the Chinese context

From the discussion of China’s SCS it became clear that this system fits the paradigm of datafication, where all social interactions are transformed into data that can be repurposed for a number of reasons. While the Chinese government mainly focusses on creditworthiness of users-citizens at the moment, what it takes to be creditworthy can increasingly be extended to activities outside of financial merit. Rating every citizen with a score is of course literally a quantifiable measure. Valuing users-citizens based upon it might seem ridiculous, but taking into account the size of China’s population and risks of corruption it is probably not very surprising that the Communist Party is trying to seize upon the opportunity of monitoring users-citizens they think Big Data is offering in the near future.

To come back to the larger debate about what happens to the notion of citizenship in a world that becomes increasingly datafied, the forthcoming SCS might not even change this notion in the Chinese context, because the distrust in the population that the issuance of the SCS implies is arguably always part of an authoritarian regime. However, the implementation of the SCS would remove the soft-biopolitical categories of what it means to be a ‘good’ citizen even further away from users-citizens vantage-points, into the algorithmic black boxes that determine one’s social credit score.

At the moment, 2020 is still a long way ahead and academic scrutiny upon the SCS might seem a precarious endeavor. However, critical perspectives from the field of Humanities such as this paper has given are much needed because it can be of value to the debate about what happens to citizenship in datafied societal situations. Qualitative studies into the perceptions of Chinese users-citizens, such as Best study about western users-citizens could be a fruitful starting point.

References

Best, Kirsty. “Living in the Control Society: Surveillance, Users and Digital Screen Technologies.” International Journal of Cultural Studies 13.1 (2010): 5-24.

Cheney-Lippold, John. “A New Algorithmic Identity: Soft Biopolitics and the Modulation of Control.” Theory, Culture & Society 28.6 (2011): 164-181.

Clover, Charles. “China: When Big Data Meets Big Brother.” Financial Times. 2016. The Financial Times LTD. December 20, 2016. https://www.ft.com/content/b5b13a5e-b847-11e5-b151- 8e15c9a029fb.

Creemers, Rogier. “Planning Outline for the Construction of a Social Credit System (2014-2020).” China Copyright and Media. Rogier Creemers. 2014. November 24, 2016. https://chinacopyrightandmedia.wordpress.com/2014/06/14/planning-outline-for-the- construction-of-a-social-credit-system-2014-2020/.

Creemers, Rogier, Peter Mattis, Samantha Hoffman, Pamela Kyle Crossley. “What Could China’s ‘Social Credit System’ Mean For Its Citizens?.” Foreign Policy. 2016. David Rothkopf. FP Group. December 19, 2016. http://foreignpolicy.com/2016/08/15/what-could-chinas-social-credit- system-mean-for-its-citizens/.

Denyer, Simon. “China Wants To Give All of Its Citizens a Score – and Their Rating Could Affect Every Area of Their Lives.” Independent. 2016. The Independent. December 19, 2016. http://www.independent.co.uk/news/world/asia/china-surveillance-big-data-score-censorship- a7375221.html.

Foucault, Michel. “The Meshes of Power.” Space, Knowledge and Power: Foucault and Geography. Eds. Jeremy W. Crampton and Stuart Elden. Aldershot UK, Burlington VT: Ashgate, 2007. 153-162.

Kharpal, Arjun. “Apple vs FBI: All you need to know.” CNBC. 2016. CNBC LLC. December 19, 2016. http://www.cnbc.com/2016/03/29/apple-vs-fbi-all-you-need-to-know.html.

Lyon, David. “Surveillance, Snowden, and Big Data: Capacities, Consequences, Critique.” Big Data & Society 1.2 (2014): 1-13.

Obbema, Fokke, Marijn Vlaskamp and Michael Persson. “China Rates Its Own Citizens – Including Online Behaviour.” De Volkskrant. Laurens Verhagen. 2015. De Persgroep. December 12, 2016.
http://www.volkskrant.nl/buitenland/china-rates-its-own-citizens-including-online- behaviour~a3979668/.

Nguyen, Clinton. “China Might Use Data to Create a Score For Each Citizen Based on How Trustworthy They Are.” Business Insider Nederland. 2016. December 12, 2016.
https://www.businessinsider.nl/china-social-credit-score-like-black-mirror-2016 10/?international=true&r=US.

Van Dijck, José. “Datafication, Dataism and Dataveillance: Big Data between Scientific Paradigm and Ideology.” Surveillance & Society 12.2 (2014): 197-208.

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