Social Credit and the Quantification of Everyday Life: Sesame Credit’s Mediation of Power Relations in China’s Credit Culture

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Social Credit and the Quantification of Everyday Life

Sesame Credit’s Mediation of Power Relations

in China’s Credit Culture

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University of Amsterdam RMA Thesis Cultural Analysis 24 February 2019 Student Mick Vierbergen Student number: 10662235 mick@vierbergen.net Supervisor

Prof. dr. ir. Jeroen de Kloet b.j.dekloet@uva.nl

Second reader Dr. Daan Wesselman d.v.wesselman@uva.nl

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Introduction

In early 2015, China’s central bank, the People’s Bank of China, requested eight leading tech companies in the Chinese market to develop credit scoring systems, including Alipay’s parent company Ant Financial and WeChat’s Tencent. This incentive was intended to restore trust in market transactions between consumers and businesses, and, on a grander scale, in China’s economy as a whole. As the economy has until recently primarily been cash-based, the credit scoring initiatives were solutions to the shortage of personal credit records in China (Creemers 23). Credit scores would presumably allow lower waged citizens, who lack a credit history, to apply for small loans (S. Ahmed). This way, the government attempted to ‘catch up’ with credit economies like that of the United States.

The commercial credit systems were initially licenced as ‘pilots’, next to local government-run pilots, for a nation-wide credit system that is to be constructed by 2020. This system encompasses a broader meaning of ‘credit’; in short, the national Social Credit System (社会信用系统 / shèhuì xìnyòng xìtǒng; hereafter ‘SCS’) not only aims to monitor

and manage financial credibility of citizens and companies, but also moral credibility

(Ohlberg, Ahmed and Lang 6). The Social Credit System evaluates individuals and institutions – commercial as well as governmental – on their economic and social behaviour based on massive data collection (Meissner 2). These ratings are the basis of a reward/punishment system that fosters ‘good’ behaviour and penalises ‘bad’ behaviour. As such, the SCS is a tool that allows the Chinese government to excite fine-tuned economic and social control.

In June 2017, however, the People’s Bank of China (PBoC) did not extend the official licences to any of the commercial credit systems to serve as a pilot under the government project of the SCS. This was partly due to differences in interest (Ohlberg, Ahmed and Lang 12) and an apparent lack of quality of the credit systems in the eyes of the central bank. Nevertheless, there are still close ties between the government and the eight companies. After refusing to extend the licences, the PBoC established a united credit scoring bureau with the eight companies, called Baihang Credit (百行征信 / bǎixíng zhēng xìn). With this,

the Chinese government can keep a close watch on the commercial scoring systems and remains relatively in control over the initiatives (Creemers 25).

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The most notable and successful of these commercial pilots – and the object of analysis in this thesis – is Sesame Credit (芝麻信用 / zhīma xìnyòng; also called Zhima

Credit), developed by Alibaba’s subsidiary Ant Financial. Sesame Credit is a consumer credit scoring service that indicates the financial credibility of users, much like FICO scores in the US. It is an opt-in service within the mobile payment application Alipay Wallet. As traditional credit information is scarce, Sesame Credit uses data from the payment platform and

Alibaba affiliated businesses – such as the e-commerce websites and Alipay-connected restaurants, third-party companies, and even certain government bureaus – to compute a personal score between 350 and 950 (S. Ahmed). Although the algorithmic calculation is blackboxed, the application identifies five categories of data that are used to compute the score: credit history (e.g. from borrowed loans); contract fulfilment capacity (e.g. electricity and gas bills); personal characteristics (age, education, occupation, place of residence, etc.); behaviour and preference (e.g. usage of the Alipay app and shopping behaviour on Alibaba-owned commercial websites such as Taobao); and interpersonal relations (based on

contacts in the Alipay app). Users with high Sesame scores are rewarded with privileges such as cheap loans, deposit waivers on bike or car rentals, and priority lanes at airports, and even expedited visa applications for certain countries (Ohlberg, Ahmed and Lang 12).

After the People’s Bank of China did not grant Sesame Credit a licence, it functions – at least for the time being – as an autonomous commercial credit system, relatively

disconnected from the Social Credit System. Several scholars have pointed out, however, that the relation between commercial actors such as Ant Financial and the government is still ambiguous, and the development of this relationship requires close attention as the construction of the SCS unfolds (Ohlberg, Ahmed and Lang 13; Creemers 27). Even at this moment, while the two credit systems are allegedly separate from each other, close ties between Sesame Credit and the government remain. Local governments, for example, use Sesame scores to waiver deposits on public services such as healthcare and social housing (Creemers 24), and conversely, Sesame Credit denies people blacklisted by the Supreme People’s Court from purchasing luxury goods at Alibaba owned e-commerce websites Taobao and Tmall (S. Ahmed). Furthermore, as state interventions on companies and internet platforms are not uncommon in China, it remains unclear what the role of Sesame Credit will be in relation to the SCS or other government regulations and how its user base will be affected.

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Journalistic and academic context

While these developments have sparked nightmarish imaginations in English language media, Chinese media seem to be less openly critical. One report of the Mercator Institute for China Studies (MERICS) – a think tank focused on policy-oriented research – provides an insightful examination of the Chinese media coverage of the Social Credit System in the first half of 2017. They found that "[n]either official nor private media fundamentally question the need for the Social Credit System” (Ohlberg, Ahmed and Lang 7). Chinese media

generally approach the topic of the Social Credit System quite even-handedly, affirming the state’s opinions that it will cure societal issues, such as food security and consumer rights. Critical voices mainly focus on the technical issues of the Social Credit System, such as infrastructure and data quality, and privacy issues of corporate credit systems. Some articles raise questions surrounding the hackability and ‘objectivity’ of commercial credit scores; one mentions growing "data black markets" where hackers raise Sesame scores (Ohlberg, Ahmed and Lang 8). While Chinese media often frame the state as a trustworthy authority, there is a general tendency of distrust towards commercial enterprises such as Ant Financial that have access to vast amounts of personal data. Censorship and self-censorship might be one reason why Chinese media have been reluctant in raising critical questions towards the State. Another reason might be that commercial credit systems are currently way more common than the state-run pilots, and thus receive more critical attention.

In line with the passive attitude of journalistic media, the topic has not raised much critical debate on Chinese social media either. Ohlberg, Ahmed and Lang note that social media coverage mainly consists of reposting of journalistic articles (5). Manya Koetse, a Chinese social media expert from the social trend-watching website What’s on Weibo, agrees that the Social Credit System currently is not a hot topic on the internet. On the contrary, she writes that when Sesame Credit is mentioned, it “is mostly linked to fun extras and the Chinese sharing economy.” (Koetse) To whatever extend censorship plays a role here too, it remains evident that the Social Credit System is not a topic much-discussed under Chinese citizens.

Conversely, English-language journalism generally has a more dystopian view on the construction of the Social Credit System. Two comparisons pop up regularly in popular

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media: George Orwell’s 1984 and the episode ‘Nosedive’ of the sci-fi series Black Mirror.1

Although concerns about increasing surveillance and centralisation of power are pressing indeed, these dystopian comparisons often simplify the real-world situation and provide a one-sided view. Moreover, factual information is often lacking or incorrect. Quotes by Alibaba executives are decontextualised and exaggerated through echo-chamber effects2_,

and many articles confuse Sesame Credit with the Social Credit System. They often frame Sesame Credit as a potential forerunner of a ‘national citizen rating mechanism’ (it is unclear if the SCS will even use a numeric scale for evaluation), while it should rather be viewed as part of one of the Social Credit System’s multiple goals: to foster the

development credit economy.3

Academic literature has responded to the construction of the Social Credit System and media coverage primarily by providing an as-accurate-as-possible view on the current state of affairs. There is still little academic coverage on the topic of social credit, and there is a need for more research from various disciplines. One of the main challenges of this thesis is therefore that there is little academic literature and it is hard to tell fact from fiction. Some of the primary academic sources of this thesis were published, or yet to be published, during the time of writing.4

Rogier Creemers, one of the leading law scholars researching the SCS, translated government policy documents and published a detailed description of the current situation in May 2018. Shazeda Ahmed is an expert on commercial and state pilot projects and

1_{See for example: “Big Data Meets Big Brother as China Moves to Rate its Citizens” (Botsman); “Sesame} Credit, Fintech and Social Credit Scores in China” (Borak); “China: When Big Data Meets Big Brother” (Clover); “Open Sesame? China’s Social Credit Revolution Hits A Roadblock” (Perkins); “Black Mirror Is Coming True in China, Where Your 'Rating' Affects Your Home, Transport and Social Circle” (Vincent).

2_{One statement is of Ant Fiancial’s technology director, Li Yingyun: “Someone who plays video games for 10} hours a day, for example, would be considered an idle person, and someone who frequently buys diapers would be considered as probably a parent, who on balance is more likely to have a sense of responsibility.” The statement was originally published by the Chinese journal Caixin in 2015, but has been copied in

numerous English-language articles. See for example: “Big Data Meets Big Brother as China Moves to Rate its Citizens” (Botsman); “How China Wants to Rate Its Citizens” (Fan); “China’s “Social Credit System” Will Rate How Valuable You Are as a Human” (Galeon); “China 'Social Credit': Beijing Sets Up Huge System” (Hatton). 3_{See for example: “Big Data Meets Big Brother as China Moves to Rate its Citizens” (Botsman); “China Wants} to Give All of its Citizens a Score – And Those Who Fall Short Will Be denied Basic Privileges” (Denyer).

4_{For my research, I attended the 16}th_{Chinese Internet Research Conference – themed ‘Modes of Connection',} in Leiden (22-23 May 2018) – which dedicated one panel to the Social Credit System. The presentations focused on advances in pilot projects and their role in the construction of the SCS. Audience questions addressed issues of clarification of rumours from the media. The presentations at the conference and the subsequent one-day workshop on the SCS show that research on social credit systems is still in an early stage that mainly approaches the SCS and its pilots descriptively.

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Sesame Credit-related issues. She published an article at The Citizen Lab in 2017 on security considerations of Sesame Credit and, together with Mareike Ohlberg and Bertram Lang, the MERICS report on media coverage and pilot projects discussed above. Genia Kotska has recently published a quantitative study on public acceptance and user perceptions of social credit pilots. Other essential publications are Mirjam Meissner’s 2017 MERICS report on the implications of the SCS for businesses and Packin, Lev-Aretz’s article on the SCS and the right to be ‘unnetworked’, and Xin Dai’s descriptive analysis of the Social Credit System Project in China’s emerging reputation state (draft paper).

The academic corpus on the Social Credit System and affiliated pilots so far has primarily approached the issues from a legal and economic perspective, as much of the research is policy-oriented. With the notable exception of Kotska’s research, user perspectives are underrepresented. Besides Hao Wang’s (unpublished) research on disciplinary techniques of credit systems through algorithmic transparency, a critical

approach to social credit has lacked in this corpus. Humanities-grounded Chinese media and internet studies has so far neglected social credit-related issues. While state influence on social media platforms is well-discussed in this field – focusing on issues of censorship and contention (Poell, de Kloet and Zeng; Yang, Power), civility (Yang, Emotions; De Seta), and ‘networked authoritarianism’ (MacKinnon) – critical research on the relation between credit platforms and the Chinese government has been lacking. Jia and Winseck have, however, argued that “to better understand the Chinese internet, we must grasp not just the tight relationship between the state and business but the emergent three-way ties between the state, internet companies, and finance capital.” (Jia and Winseck 32) Instead of a political perspective on the Chinese Internet, the field must also take the view of political economy.

This thesis also relates to platform studies and critical data studies, as it quite closely follows José van Dijcks analytical model to examine platforms through the lens of political economy and Actor-Network-Theory (ANT) described in her book The Culture of

Connectivity. Her methodology approaches online platforms both as socioeconomic

structures – focusing on ownership, governance, and business model – and as sociotechnical constructs – by analysing technology, users and content. Although Van Dijck uses her

method to study how social network sites and platforms for user-generated content mediate sociality, I employ her model to analyse the power structures of the trading and marketing platform Sesame Credit, and, consequently, also its effects on sociality.

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Moreover, platform studies, and particularly Van Dijcks book The Culture of

Connectivity, has focussed mainly on ‘Western’ platforms (see also Srnicek’s Platform Capitalism and Van Dijck, Poell and De Waal’s new book The Platform Society) and could

benefit from case studies of platforms in Chinese contexts. Conversely, academic and non-academic discourse should regard the social credit systems in China in the global context of increasing platformisation and datafication, and growing reputation economies.

This thesis positions itself at the intersection of these academic debates – adding a critical perspective to the law and economy-dominated discussion of Chinese social credit and introducing the topic to Chinese internet and media studies – by analysing how Sesame Credit mediates power relations as a technological platform. Building on the policy-based research of social credit studies, it takes a ‘top-down’ approach of what José van Dijck calls “platform strategies” and contrasts this with a bottom-up perspective of “user tactics” (20). I use qualitative in-depth interviews with users to complement Kotska’s quantitative

analysis. The main question of this thesis is “How does Sesame Credit mediate power relations between Alibaba and its users?” This research question is split up into three sub-questions: “How is Sesame Credit positioned in a network of institutional power structures of Alibaba and the Chinese government?”; “How are Alibaba’s norms inscribed in Sesame Credit as a technological platform?”; and “How does disciplinary power operate through the everyday user practices of Sesame Credit and what are the user’s motivations of use?”

Theory and methodology

Methodologically speaking, this thesis follows the approach of ‘cultural analysis’ (Bal). Cultural analysis typically aims to ‘conduct a meeting’ between an object and a concept and uses close reading, derived from textual analysis, to analyse cultural objects. Cultural

analysis aims to take on a critical perspective on cultural phenomena and adopts a self-reflexive stance. As cultural analysis aims for a dialogue between object and concept, the theory will be introduced – and thoroughly discussed in relation to the objects – in the chapters whenever the analysis asks for it. Hence, this introduction does not provide an in-depth overview of the theoretical framework to avoid ‘applying’ theory ‘on’ the object.

It seems fit, however, to note in advance that this thesis adopts a Foucauldian conception of power to answer the above-mentioned research questions. For Foucault, power is not a quality that an entity can possess but is relational. This allows us to see how

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Sesame Credit is positioned in a network of power relations; between Alibaba, the Chinese government, users, and merchants connected to the platform. Actor Network Theory offers a productive view on Sesame Credit’s practice of mediation of these power relations, as it recognises non-human forms of agency: the technological actors within the credit system and the platform as a whole.

The methodology of cultural analysis does not come without complications. First of all, Sesame Credit is not a clearly demarcated cultural object like a text or an artwork, which makes it difficult to close read. And, as might be clear by now, academic literature is still struggling to define its characteristics and boundaries. Practically speaking, this means that this thesis frames Sesame Credit in different ways – as a technology of power, a

technological platform, and an everyday consumer credit system – and close reads relevant objects, such as policy documents, the Sesame Credit application and its interface, and user experiences and motivations. Besides this, Sesame Credit is defined by another cultural object, that is the Social Credit System. As the Chinese government initiated the

development of Sesame Credit as a pilot under the project of the SCS, an analysis of Sesame Credit cannot exclude the examination of its relation to the national Social Credit System.

Furthermore, many parts of the Sesame Credit are ‘blackboxed’, i.e. only the inputs and outputs are visible while the inner workings remain opaque. Both on a technical level and the level of business strategies, these mechanics are hidden. This problem is

unavoidable when analysing a financial platform such as Sesame Credit, as trade secrets prevent businesses to reveal these inner workings. To circumvent this problem for close reading, I resort to reverse engineering techniques – looking at inputs and outputs to uncover parts of these inner structures – and rely on existing academic literature to analyse technical aspects from the ‘outside’.

To examine how users engage with Sesame Credit in everyday life, I made a research trip to Shanghai, where I stayed for four months. In this time, I used Alipay and Sesame Credit on a daily basis and spoke to multiple Chinese users of the credit system. I set up a personal Sesame Credit account for this research, which allowed me to study how my daily actions influence the score (and to try reverse-engineering the algorithmic decision making). I also performed six in-depth semi-structured interviews (Bernard 212) with Chinese users within my social circle. Of the six informants, three were male and three female, aged

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between 21 and 27. Most came from and lived in Shanghai or other urban areas in China. The interviews were performed in English, as my Chinese is insufficient for conversations.

The interviews were set in an informal setting, at their home, in a café, or over the phone, and structured like informal conversations. The interviews are audio recorded5_and

transcribed afterwards. For ethical reasons the participants were noted in advance about the purposes of the research and were aware that the conversation was being recorded.6

They were also informed that the interview was on a voluntary basis and that they were not obliged to answer any questions they did not want to answer, as some questions ask for their opinion on politically sensitive topics. I also replaced their names with common given names in China to ensure their anonymity.

The ethnographic study on users and usage of Sesame Credit is limited in scope, due to restrictions of time and resources. Its aim is not to provide a full ethnography of social credit in everyday life. Rather, it functions as a pilot study that identifies the most general uses and motivations. As such, it forms a base for further research on the topic. In line with this, and because of the little academic coverage about Chinese social credit, this thesis as a whole is a pilot study that explores the power relations of social credit systems. Hence, it focuses on three diverse aspects: a political economy of Sesame Credit, the technological structure of the platform, and an audience ethnography.

Chapter structure

The first chapter aims to answer the question “How is Sesame Credit positioned in a network of institutional power structures of Alibaba and the Chinese government?” This chapter builds on the policy-based research of social credit studies and presents an

overview of the current state of affairs of the national Social Credit System. It then frames the object of Sesame Credit within this context. I close read one policy document that describes the government’s plans most clearly and has been central to social credit studies. Then, I discuss Sesame Credit within the ‘ecosystem of connective media’ (Van Dijck, Culture) and the ‘business ecosystem’ (Moore) of Alibaba along the lines three concepts from Manuel Castells’ theory of political economy: ownership, business model, and

5_{One interview is partly video recorded to follow the user’s walkthrough, as was proposed by the participant} himself.

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governance. Building on Foucauldian theory, I argue here that Sesame Credit is a

‘technology of power’. The chapter ends with a discussion on how Sesame Credit relates to Chinese media and Internet studies.

The second chapter analyses Sesame Credit through a close reading of the smartphone application and its interface. The research question is “How are Alibaba’s norms inscribed in Sesame Credit as a technological platform?” Here, I use Actor-Network-Theory to account for non-human actors that mediate power relations. I use Light, Burgess and Duguay’s ‘walkthrough method’, to first give an outline of how the system works. In their method, the researcher walks the reader through an application, focusing on the functions and features and semiotic structures. Following José van Dijck, the analysis uses four intersecting concepts: data, algorithm, interface and content. This chapter aims to explain the “implicit participation”, or the usage inscribed in the technology itself (Van Dijck, Culture 33).

The third chapter is themed around the user of Sesame Credit and aims to answer the question “How does disciplinary power operate through the everyday user practices of Sesame Credit and what are the user’s motivations for use?” It focuses on the intersection between implicit and “explicit use” of the application (Van Dijck, Culture 33). Explicit use refers to the actual use of technological platforms in situ. This chapter approaches explicit users as ethnographic subjects, and their everyday usage practices and motivations for use are the objects of analysis. I analyse this through a close reading of in-depth interviews. During the interviews, I asked the participants to walk me through their everyday usage of Sesame Credit, following the guidelines of Light, Burgess and Duguay’s ‘walkthrough method’. The chapter first offers a reflection on my position as a researcher before going into the user practices and their motivations for use.

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Chapter 1: Alibaba and the Chinese government

1.1 Introduction

The question “How is Sesame Credit positioned in a network of institutional power

structures of Alibaba and the Chinese government” is central in this chapter. To answer this, I first elaborate on the Chinese government’s plans to construct a national Social Credit System (SCS), under which China’s central bank initially licenced Sesame Credit as a commercial pilot. The next section focuses on the function of Sesame Credit in Alibaba’s ecosystem by analysing the structures of ownership, business model, and governance. Following a Foucauldian theoretical framework, it argues that Sesame Credit is a technology of power that mediates (financial, social, behavioural (etc.) norms to its user-base that support Alibaba’s business model. Lastly, I look at the relation between Sesame Credit and what I term an ideology of ‘credit culture’ that the Chinese government aims to establish, basing my analysis on literature from Chinese media and Internet studies.

1.2 China’s 2020 National Social Credit System

This section gives an overview of the current status of implementation of the national Social Credit System. It first discusses an essential policy document released in 2014 that gives an outline of what the Social Credit System will be and its how it is to be constructed.

Hereafter, I focus on the mechanisms that are already in place.

The Construction of a Social Credit System

In June 2014, the State Council released the “Planning Outline for the Construction of a Social Credit System (2014-2020)” (“社会信用体系建设规划纲要(2014-2020年)" / "shèhuì

xìnyòng tǐxì jiànshè guīhuà gāngyào (2014-2020 nián)") This document is the first of a series

of government policy publications and is still the most comprehensive overview of what the SCS will look like. It has been a cornerstone in policy-oriented research in social credit studies.

Rogier Creemers, who made a translation of the policy document, comments that the document “put forward a timetable until 2020 for the realization of five major

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investigation and oversight, fostering a flourishing market built on credit services, and completing incentive and punishment mechanisms” (12). The prime objective of the SCS is the promotion of sincerity on four different levels of society: it aims to make government affairs more ‘sincere’, to increase commercial sincerity, social sincerity, and enhance credibility in juridical affairs (China, State Council). The “Planning Outline” also emphasises the SCS’s dual goal of social management and economic regulation. The creation of the Social Credit System is paralleled with an ideological transformation; it goes hand-in-hand with the promotion of ‘Core Socialist Values’ (社会主义核心价值观 / shèhuì zhǔyì héxīn

jiàzhí guān). On the other side, it will be an important mechanism that strengthens the

Socialist Market Economy (社会主义市场经济 / shèhuì zhǔyì shìchǎng jīngjì).

The Socialist Core Value System (社会主义核心价值体系 / shèhuì zhǔyì héxīn jiàzhí

tǐxì) is a set of twelve moral principles – of which integrity is one (诚信 / chéngxìn;

etymologically close to ‘credit’/’sincerity’) – that is introduced by the State in 2012 to fight the ‘moral decay’ of the past decades of increasing individualization. While these twelve values are being promoted in education, via propaganda and the strategic use of different media (China, State Council, Planning Outline, Part III.1), the Social Credit System will give another impulse in the moral schooling of Chinese citizens. In particular, the SCS aims to establish a ‘sincerity culture’7₍_诚信文化_{/ chéngxìn wénhuà) and create a “thick}

atmosphere in the entire society that keeping trust is glorious and breaking trust is

disgraceful and ensure that sincerity and trustworthiness become conscious norms of action among all the people.” (sic) (China, State Council, Planning Outline, Part I.3)

On the other hand, the Social Credit System is an essential component of the

Socialist Market Economy. The Socialist Market Economy was introduced in Deng Xiaoping’s economic reformations of the late 1970s and merges a planned Socialist economy with market elements. It is part of his idea of ‘material civilisation’, which he proposed alongside the concept of ‘Socialist spiritual civilisation’ (Yang, Emotions 1949). The SCS allows the State to effectively regulate the behaviour of market participants and reach industrial and technological targets (Meissner 4). As the evaluations of customers and companies will drastically influence their market positions, the SCS can infuse regulations in market

7_{Rogier Creemers has translated this as ‘sincerity’ but bear in mind that it is the same word as the Socialist} Core Value of ‘integrity’.

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exchange. In other words, planning will be implemented in the market economy itself and State influence will be visible on the level of ‘liberal’ trade as credit scores become part of competition.

The “Planning Outline” states that governmental sincerity is the core of the creation of the Social Credit System (part II, section 1). Without a trustworthy network of

administration and honest enforcement of regulations, the implementation of the SCS on other levels will not be possible. In line with this, the SCS also aims to improve judicial credibility to assure an honest prosecutorial basis for punishment systems. The government also has an exemplary role in society; raising honesty, accuracy, efficiency in government affairs, and even transparency of policies and regulations, will be a model of sincere behaviour for the rest of society.

In the commercial sector, the Social Credit System will evaluate the economic and social behaviour of companies. The State Council hopes to resolve societal problems such as issues with food security, tax fraud, violation of consumer rights, and environmental

pollution (China, State Council, Planning Outline, Part II.2). Meissner’s 2017 MERICS report shows a comprehensive diagram that shows what data is used to compile credit scores and what possible consequences are (see Image 1). Input data includes information on company representatives, annual reports of corporations, and compliance with government

regulations (for example internet regulations, safe work environments, tax payment, environmental impact, and intellectual property). The scores have an effect on subsidies and investment opportunities of the companies, and even travel possibilities and career opportunities of company representatives, among other things. Ultimately, the system will also incorporate real-time and remote monitoring and automated score computation (Meissner 4). In e-commerce, for example, real-time data could provide information on customer satisfaction, delivery, product quality, etc. In the transportation sector, vehicles will be tracked remotely, and in polluting industries emissions will be monitored in real-time. Meissner notes that, instead of a centralised rating organisation that compiles a single score per company, “the government plans suggest a rather diversified and decentralized market for social credit ratings” with multiple (commercial and governmental) score providers (5).

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For the improvement of ‘social sincerity,’ the Social Credit System is implemented in areas of healthcare, social security, and education. The "Planning Outline" also dedicates one paragraph to the construction of a credit system that evaluates ‘natural persons’. It states that the Social Credit System will collect credit records on the economic and social lives of individuals, alongside the ratings that relate to their professional function. Moreover, the document also mentions that a system will be constructed to evaluate the online behaviour of so-called ‘netizens’ (网民 / wǎngmín). Interestingly enough, the document does not

mention quantitative scoring as a method of evaluation (Creemers 13). Although the document implies that the SCS will increase surveillance on citizens and evaluate their behaviour, it is unclear if it will rate citizens with an actual ‘citizen score’.

Although the mention of a credit system for natural persons and netizens is brief in this first document, further publications specify some of the consequences for individual citizens. Blacklisted people are for example restricted to hold high positions in companies or government bodies. Other imposed restrictions are on train or air travel, hotels and

restaurants, conspicuous consumption travel (such as organised holidays to foreign

countries and other holiday areas), high-fee schools for children of the subject, and building and renovating housing (China, State Council, Opinions).

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In short, while the Chinese government presents the Social Credits System as a "cure-all" for a whole range of societal problems (Ohlberg, Ahmed and Lang 5), it will also be a massive technology-based system for economic and social control (Creemers 3). The SCS has two main functions: to promote financial credit and to track and manipulate citizens’

social and moral behaviour. The two are, however, not completely separated. The core of

the system will be the collection of data on individuals and organisations from a multiplicity of sources, possibly also commercial ones, that will be shared among local and central government bodies. On the basis of these data and evaluations, citizens and institutions will be evaluated and penalised or rewarded accordingly.

Current status of the implementation

Although the construction of the Social Credit System is still at an early stage, certain fundamental components have already been set in place (Ohlberg, Ahmed and Lang 2). So far, the primary concern has been the construction of a data sharing infrastructure and the establishment of the Joint Punishment System. Besides this, local governments and

commercial actors have established pilots to experiment with the practical application of social credit systems.

One significant factor of the digitisation of social management has been the

introduction of the 2003 Identity Card Law (Creemers 20). The 18-digit identity cards render citizens digitally identifiable and allow for efficient data collection. It has become a universal system for digital identification and is used by different government bodies and private enterprises. Together with the regulations on real-name authentication requirements in online environments, such as social media and other account-based systems, and in mobile phone registration, the introduction of the ID code made it possible to connect multiple data points and store this information efficiently (Creemers 21).

For the collection and sharing of data for the Social Credit System, the government established the ‘National Credit Information Sharing Platform’ (全国信用信息共享平台 /

quánguó xìnyòng xìnxī gòngxiǎng píngtái) in October 2015 (Meissner 6). This has been the

central platform that receives data from multiple ministries and other government bodies, such as the Peoples Bank of China and the National Development and Reform Commission (NDRC) that both lead the implication of the SCS. The Information Sharing Platform is the

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back-end data provider for the information platform ‘Credit China’ (信用中国网 / xìnyòng

zhōngguó wǎng). This website, built in collaboration with Baidu, provides information about

the Social Credit System itself and makes public credit-related information about companies and individuals (Creemers 21). The National Enterprise Credit Information Publicity System

(国家企业信用信息公示系统 / guójiā qì yè xìnyòng xìnxī gōngshì xìtǒng) is another

platform that publicises information – particularly on companies – that is backed by the Information Sharing Platform. These practices of publicly ‘naming and shaming’ are part of a punishment system that is connected to the Social Credit System. Although these databases are not yet up to their full potential, they form the basis of a data sharing infrastructure that could in the future be the core of the SCS (Ohlberg, Ahmed and Lang 11).

The Joint Punishment System (联合惩治体系 / liánhé chéngzhì tǐxì) is another

element that has already been set in place. Jointly established by 45 collaborating state bodies, it is a blacklisting system that currently primarily lists citizens that resisted court orders and companies that do not conform to the law or regulations (Ohlberg, Ahmed and Lang 10). Punishments that follow for blacklisted individuals and companies include restrictions in economic opportunities, constraints in holding high-positions in certain organisations, and limits in conspicuous consumption (Creemers 15). The latter

encompasses restricted access to first-class air travel and high-speed trains, luxury hotels and restaurants, holidays to foreign countries and fee-paying schools for the entrant’s children. As these blacklists are shared with local governments and even private enterprises, the restrictions are imposed everywhere. Alibaba’s Taobao and Tmall, for example, deny blacklisted entrants to make luxury purchases (. Some local governments have already incorporated the blacklist system as a punishment mechanism for minor offences. The city of Ningbo, for example, has blacklisted fare-dodging individuals and Shenzhen installed facial-recognition technologies at zebras and reports repeatedly caught individuals (Creemers 17-18).

In several cities and districts, local governments are experimenting with social credit information systems. The NDRC and PBoC have appointed 43 municipalities to construct pilots for the national SCS. Many of these trials have developed their own rating system and are testing punishment mechanisms for blacklisted citizens and companies. Rongsheng, one of 12 selected ‘model cities’, has introduced a scoring-based system in which individuals are

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rated on a scale that goes up to 1000 points (Ohlberg, Ahmed and Lang 12; Creemers 19). The scores are then categorised into six categories (AAA to D) with appurtenant

consequences. Shanghai has released a smartphone application called ‘Honest Shanghai’ (诚信上海 / chéngxìn shànghǎi), in which users can register with their national ID number and

facial recognition (Ohlberg, Ahmed and Lang 12; Creemers 18). The application then computes a score based on government documents and rates the citizen with one of three categories: very good, good, and bad. The app also shows the ratings of local companies on a map marked with green, yellow, and red smileys.

The People’s Bank of China also encouraged eight tech companies in 2015 to develop commercial pilots for the national SCS and to stimulate the credit economy and increase financial inclusion. Citizens that traditionally lack credit records could benefit from such credit systems as they gain financial opportunities from these commercial credit systems. Among these pilots are Tencent, WeChat’s parent company, Baidu, and Alibaba’s subsidiary Ant Financial that developed Sesame Credit. The central bank initially gave the companies six months to develop a commercial credit-reporting system (Creemers 22).

According to the PBoC, however, these commercial trials were not a success; in 2017 the central bank refrained from granting official Social Credit System pilot licences to any of the commercial enterprises. The main reason was a conflict of interests: the priorities of the companies lay at developing a credit system that supported their own core business, such as

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e-commerce or insurance (Creemers 24). The vision of the People’s Bank, however, was to establish a centralised credit reporting system. Due to the direct market competition of the companies, they would not share their proprietary data, creating a fragmented credit economy (Ohlberg, Ahmed and Lang 12). This also led to complains on behalf of the central bank about the ability to indicate accurate financial credibility of the systems. Although the eight companies included some of the largest data-owners in China, their data generation is restricted to their relative userbases and business areas (Creemers 24-25). Other criticisms were that the companies did not protect user privacy (Ohlberg, Ahmed and Lang 12) and that due to their commercial interests, the companies could not inhabit an independent third party-position (Reuters Staff).

In an effort to solve these problems, the National Internet Finance Association (NIFA) founded a credit scoring bureau called Baihang together with the eight companies. The NIFA is a government organisation originally initiated by, and still under the administrative

control of, the People’s Bank of China (Creemers 25). The government body owns the majority of equity stakes with a percentage of 36%, while the rest of the shares are equally distributed under the eight companies. As a semi-commercial credit scoring bureau, Baihang complements the central bank’s Credit Reference Centre, which is its main governmental credit authority. The credit union received a three-year licence from the People’s Bank of China, which allows the central bank to keep a close watch on the commercial credit system pilots (Creemers 25). It is however uncertain how this partnership is going to unfold in the coming years. If the cooperation lasts, the government might be able to unify the data from the eight companies to support the national SCS. The cooperation might however also break due to the competing commercial interests of the private companies.

The future relationship between the national Social Credit System and Sesame Credit thus remains ambiguous, but it is certainly an important development to follow as the implementation of the SCS advances. One major factor in this relationship will be the commercial interests of Ant Financial. These are currently for the most part in line with the goals of the government, but this could change if the Baihang collaboration fails. The next section elaborates on these interests of Ant Financial and its superior Alibaba.

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1.3 Alibaba’s business ecosystem

As the previous section has shown, Sesame Credit is connected to the political context of the Social Credit System. But although the Chinese government had originally initiated the development of commercial social credit pilots, Sesame Credit remains a consumer credit system owned by the private enterprises Alibaba and Ant.

This section analyses the commercial and organisational (infra)structures of Sesame Credit within Alibaba’s ‘business ecosystem’ (Moore), and in the larger ‘ecosystem’ of connective media in China (Van Dijck, Culture). To do so, I use three concepts from Manuel Castells’ theory of political economy: ownership, business model, and governance.

“Proponents of political economy”, remarks José van Dijck, “[…] regard platforms and digital networks as manifestations of power relationships between institutional producers and individual consumers.” (27) It is precisely the commercial and institutional power relations that this section aims to address.

Ownership: Alibaba’s e-business ecosystem

Ownership is perhaps the most crucial aspect in defining institutional positions of power. In this light, it no surprise that the government is cautious in granting credit scoring licences to any one of the eight companies. In the Baihang joint credit bureau, the PBoC, via the NIFA, retains its position of power as it has the highest equity stakes in the enterprise. In this way, the central bank also maintains its control over the commercial initiatives.

In Sesame Credit, the most significant stakeholders are Ant Financial and, by extension, the Alibaba Group. Ant Financial started as the payment platform Alipay to manage online payment on the Alibaba.com e-commerce website. In 2015 Alipay changed its name to Ant Financial as it began offering multiple financial services besides the payment platform. Ant Financial now also incorporates Ant Fortune, a wealth managing service; MYbank, a private online bank for micro-enterprises; and the credit scoring system Zhima Credit (Ant Financial). As such, Ant Financial grew out to be the financial arm of the Alibaba Group. Although Ant Financial largely operates as an autonomous enterprise, Alibaba has a 33% equity stake in the company (Alibaba Group and Ant Financial).

In fact, Alibaba itself, which started as a business-to-business (B2B) trading website in 1999, has grown into one of the largest Internet-based conglomerates in China. Over the last two decades, the corporation has expanded its commercial imperium through takeovers

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and partnerships. It founded the popular business-to-consumer (B2C) e-commerce website Taobao.com, partnered up with Yahoo! and acquired the Yahoo! China web portal, and founded the cloud computing company Alibaba Cloud (Huang, Hu and Lu 29). Today, the Alibaba Group leads an expanding interconnected network of companies that operate interdependently for mutual benefit. Jia and Winseck also note, that the Chinese

government also has a direct influence in Alibaba as Alibaba has one government official in its board of directors (46). As the board decides on the management of the company, government goals can be implemented in the business strategies of Alibaba.

Based on James F. Moore’s conception of the ‘business ecosystem’, Huang et al. argue that Alibaba has evolved into an ‘e-business ecosystem’. They define the term as “an organic ecosystem that is made of enterprises and organizations with close relations, using the internet as a platform to make competition and communication through virtual alliance, sharing resources, and making full use of their advantages beyond geographic limits” (27). By clustering multiple companies together and covering multiple sections of the market, Alibaba manages to compete with other e-business ecosystems such as eBay, which withdrew from the Chinese market in 2006, and Tencent.

Much of the internal structure of Sesame Credit depends on the relationship with other platform companies. Ownership, bonds, and competition play important roles in how it works as a system of production (Van Dijck, Culture 36). On the back-end of the credit scoring system, this means that user data that Sesame Credit uses to compute scores are derived from Alibaba-owned or related enterprises. The score that is produced is thus based on behaviour on e-commerce websites, interaction on social media, and credit history (etc.).

On the other side, it also means that Sesame Credit is applicable to many other companies and their services. Hight Sesame scores can for example waiver deposits on the bike-rental platform Ofo (Yu). There are even other platforms that incorporate the Sesame score into their service. The P2P services platform Daowei (到位 / dàowèi) allows only users

with a score higher than 650 to offer services on the platform while requesting a service requires a minimum score of 600 (Schoenmakers). Baihe (百合 / bǎihé), an online dating

platform, also encouraged users to show their Sesame score (Creemers 23).

In short, looking at ownership shows that the Alibaba Group, as a ‘leading species’ (Moore) in the e-business ecosystem occupies a central position of power. Still, the

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companies coevolve with each other in a complex, relatively decentralised, manner driven by mutual interests. The distribution of data and the applicability of Sesame Credit is built on this institutional infrastructure. Sesame Credit, as a system of production, relies on, and supports, other companies in the Alibaba e-business ecosystem.

Business model: ‘Meet, work, and live at Alibaba’

Alibaba’s vision is to create an online space for consumers and businesses to ‘meet, work, and live at Alibaba’ (Alibaba Group). It provides a platform for users, consumers, merchants, and businesses to interact socially and commercially. Small businesses can use the digital infrastructure of the Alibaba Group to connect with other companies in the ecosystem and gain a larger userbase. One example of this is Alibaba’s digitisation of traditional retail under the name of ‘New Retail’8_{. Small retail businesses can easily join the ecosystem and tap into}

its infrastructure by conducting part of their business via Alibaba-connected platforms, such as sales via Taobao and payments via Alipay. Also due to Jack Ma’s guru-like status in the e-business economy, many companies gladly follow his ideas for future developments. Thirdly, the Alibaba Group website explains that the company strives to make Alibaba products and services central to the everyday lives of their customers.

It is clear from this vision that Alibaba aims to bind companies and consumers to their ecosystem and to increase user experience. These two components, gaining ecosystem members and enhancing user experience, go hand-in-hand as they amplify network effects. For example, the user experience improves as more businesses are connected to the Alipay platform. Conversely, the more people use Alipay; the more businesses join the platform. As such, platform companies have a natural tendency towards monopolisation (Srnicek 45).

Similarly, Sesame Credit also enhances the network effects of the Alibaba ecosystem. On the one hand, it encourages users to interact with Alibaba-affiliated platforms such as Taobao or Tmall, as this increases Sesame scores and grants them

privileges. In fact, many of these rewards also encourage users to purchase more goods and services that are related to Alibaba. This way, Sesame Credit creates behavioural feedback loops that steer users towards staying within the ecosystem. This increased user traffic

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attracts businesses to connect to Sesame and offer their products and services via the credit platform and join Alibaba’s ecosystem.

Jie Guo and Harry Bouwman take an ecosystem view on the Alipay’s mobile payment platform (of which Sesame Credit is part) and focus on the dependency of actors in the ecosystem. They show that merchants highly depend on the m-payment platform as it provides, among other things, an increased customer base, cheap transaction management, and an IT infrastructure (69; table III). Many merchants and businesses do not have the resources to develop their own mobile payment infrastructure or cannot compete with payment platforms giants such as Alipay. As such, for many single merchants, it is better to join the platform than to compete against it. Conversely, Alipay also depends on merchants as they provide their products. This dependency is, however, less fundamental as Alipay’s platform relies on a multitude of suppliers.

Within the Alipay ecosystem, Sesame Credit enhances trust in the commercial relationship between consumers and companies in the Alibaba ecosystem and, as such, further amplifies network effects. Indeed, Guo and Bouwman note that risk management is another element of merchant dependency on Alipay (69; table III). As a financial credit indicator, Sesame Credit reflects the customer’s ability to pay back borrowed money or products/services paid on credit. And, not unimportantly, it also reflects their loyalty to the Alibaba ecosystem.

This way, Sesame Credit is a mechanism that binds consumers and companies to the Alibaba e-commerce ecosystem and strengthens their relationship. By providing a credit and payment infrastructure between consumers and companies, Sesame Credit and Alipay position themselves strategically between two interacting parties. This makes both

companies and consumers dependent on their financial credit infrastructure. Furthermore, it allows platforms to extract data from this interaction and for the improvement of their own services – such as a more accurate Sesame score – and other services in the ecosystem of Alibaba.

Governance: Sesame Credit as a technology of power

José van Dijck notes that “[t]o analyze the governance structure […], one needs to

understand how, through what mechanisms, communication and data traffic are managed.” (Culture 38; emphasis in original) She points to contractual mechanisms such as the

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end-user license agreements (EULAs) or terms of service (ToS). Indeed, Sesame Credit, and by extend Alipay, use these contractual mechanisms to secure the rights and obligations of platform users (i.e. companies and consumers). This way, Sesame Credit gets legal access to personal data on which it bases the credit score.

Technical mechanisms like algorithms and protocols, which will be the topic of the next chapter, play important roles in automated forms of governance. It is through these mechanisms that important technical decisions are made, such as how certain data influences the score and to what privileges the user has access to. Van Dijck rightly points out that structures of governance touch upon social norms (e.g. privacy, property, and proper behaviour etc.) and often contest and (re)structure these social norms (38). Sesame Credit’s central mechanism of governance is, however, the score. Through the score, Sesame Credit regulates its user-base by granting or denying individual users access to certain privileges. By doing so, the credit scoring mechanism fosters behavioural patterns that are in line with Alibaba’s business model, i.e. increasing consumption and promoting the use of connected platforms and services.

As the credit scoring system decides over everyday possibilities, such as renting a bicycle, and financial opportunities, such as borrowing money, it is a mechanism that subjects users to forms of discipline. For Foucault, discipline targets the behaviour of

individual bodies (Meshes 160). Jeffrey T. Nealon notes that “discipline works on individuals precisely through the more efficient means of targeting their potential actions, their

capacities: literally what they can – and can’t – do” (31). The score in Sesame Credit is a deciding mechanism in what actions the individual can and cannot undertake.

Discipline does, however, not work solely through the restriction of certain actions. It is also a productive power that promotes actions and desires (Nealon 24). Through granting access to certain services, Sesame Credit promotes the use of Alibaba-connected services. It also frames services and privileges inaccessible for users with lower scores as objects of desire, encouraging users to increase their score (for example by purchasing products on Alibaba-connected e-commerce websites).

Sesame Credit is a panoptic environment that monitors the user’s online and consumer behaviour, social connections and personal characteristics. Through the score, credit scoring is a mechanism that provides evaluative feedback on the user. It

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individual users, the score is thus an indicator of how they should behave commercially, socially, or otherwise. Through its evaluative score, Sesame Credit thus also promote practices of self-discipline and self-surveillance as it urges users to adjust their behaviour to their score.

Although for Foucault discipline is practised in enclosed institutional environments, like schools, factories, prison, and the army, Sesame Credit brings these disciplinary

techniques into the everyday lives of users (History 262; Meshes 159-160). This way, the slogan ‘Live at Alibaba’ takes a quite literal form.

In short, Sesame Credit is a technology of power in Alibaba’s business ecosystem: a mechanism that “determine[s] the conduct of individuals and submit them to certain ends or domination” (Foucault, Technologies 18). As a technology of power, Sesame Credit produces a certain normativity: norms of consumption, sociality, behaviour, etc. These norms, fuelled by Alibaba’s business goals, are implemented in the everyday lives of Sesame Credit users.

1.4 China’s credit culture

The previous subchapters have shown that Sesame Credit functions as a private credit rating system, relatively distinct from the emerging national Social Credit System. However, as Baihang shows, close ties remain between Sesame Credit and the People’s Bank of China. Returning to the question “How is Sesame Credit positioned in a network of institutional power structures of Alibaba and the Chinese government”, this subchapter further explores Sesame Credit as a platform on the Chinese internet.

Although there are no definite answers yet on the collaboration between Internet companies and the Chinese government concerning social credit, there already is a close relation between the two. Social media websites like Sina Weibo and WeChat carry out the State’s censorship regulations, and, as discussed above, Alibaba restricts blacklisted civilians from buying luxury goods. The government’s power over the Internet is further exemplified by the nation-wide block of thousands of foreign websites (Poell, de Kloet and Zeng 3), also known as ‘The Great Firewall of China’. The internet in China is thus a combination of State, companies and internet users.

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Rebecca MacKinnon coined the term ‘networked authoritarianism’ to describe the Chinese government’s control over the Internet. She argues that this control is manifested in four different ways, corresponding to four deliberative spaces in Chinese cyberspace identified by Min Jian: central propaganda spaces, government-regulated commercial spaces, emergent civic spaces, and international deliberative spaces. The government’s influence on Alipay and Sesame Credit can be understood in relation to the second category: the platforms are “owned and operated by private companies but subject to government regulation” (36).

She notes, however, that networked authoritarianism has two sides: “single ruling party remains in control while a wide range of conversations about the country’s problems nonetheless occurs on websites and social-networking services.” (33) As such, citizens enjoy a greater ‘sense of freedom’ as Internet platforms allow them to talk about political issues, while the government still controls. Xueqing Li, Francis L. F. Lee and Ying Li also point to the dual impact of social media platforms; on the one hand, social media promotes political awareness and civic culture, while on the other hand, it enlarges support for the political system. This double view on China’s Internet control resonates through Chinese Internet and media studies, many authors arguing that although the Internet is heavily surveilled and controlled by the State, there still is room for humour (Poell, de Kloet and Zeng) and

contention (Yang, Power).

Guobin Yang argues that the Chinese government recently turned towards proactive and preventive methods of governing the Internet that produces a ‘positive energy’ online (1946). At the core of this is the idea of ‘wénmíng’ (文明), which can mean both ‘civilization’

and ‘civility’ and is one of the twelve Socialist Core Values. Yang explains that “[a]s

civilization, wenming operates as an ideological discourse that legitimates the governance

and administration of society. As civility, it functions as a strategic technology and tool for governance and self-governance, including the governance of the Internet.” (1946; emhasis in original) Wénmíng thus operates as a soft power that is fostered by the State but also performed by non-State actors like platform companies and Internet users.

It is in this light that we can understand how the Chinese government promotes

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called a credit culture.9_{I use ‘credit’ here in the Chinese sense, that includes, besides}

financial credibility, also moral values like sincerity, trustworthiness and integrity and is etymologically close to the Socialist Core Value of integrity (诚信 / chéngxìn). This way,

China’s credit culture is similar to Yang’s first function of wénmíng: it is “an ideological discourse that legitimates the governance and administration of society” (1946).

At the centre of this ideological discourse is the belief that these values can be calculated objectively. Van Dijck calls this an ideology of ‘dataism’:

dataism shows characteristics of a widespread belief in the objective quantification and potential tracking of all kinds of human behaviour and sociality through online media technologies. Besides, dataism also involves trust in the (institutional) agents that collect, interpret, and share (meta)data culled from social media, internet platforms, and other communication technologies. (Datafication 198; emphasis in original)

In the name of increased societal ‘credibility’, the Chinese government is able to increase surveillance practices and personal restrictions. In light of Foucault’s biopower, we can see how credit is thus used to increase the living standards – hence biopower – of the

(normative part of the) population, while at the same time targeting individuals. Indeed, credit is also used as a "technology and tool for governance and

self-governance" as the previous section shows (Yang, Emotions 1946). Sesame Credit is one of these technologies of power that disciplines individual users by evaluating their behaviour and rewarding or punishing them according to their score.

Although Sesame Credit mainly mediates Alibaba’s business goals, it is also

positioned in the credit culture that the Chinese government aims to establish. It is part of the ideological apparatus that assumes that societal values such as credibility, sincerity, and trustworthiness can be measured and calculated into a numerical scale of ‘credit’. As such, Sesame Credit combines hard forms of power – through the discipline of users – with soft power; it promotes the ideology of a credit culture.

9_{I derived the notion of ‘credit culture' from the Chinese government's call to promote a ‘sincerity culture' in} the "Planning Outline" as the words for ‘credit' and ‘sincerity' are strongly connected in the Chinese language.

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Chapter 2: Sesame Credit as a Technological Platform

2.1 Introduction

After establishing that Sesame Credit is a technology of power that produces norms that are fuelled by Alibaba’s business model, this chapter asks the question “How are Alibaba’s norms inscribed in Sesame Credit as a technological platform?” By conceptually framing the credit scoring system as a technological platform, this chapter adopts a double-sided view of Sesame Credit as a space that facilitates interaction and mediates this interaction through the network of technological actors.

As a platform, Sesame Credit is positioned between multiple actors and facilitates their interaction: Alibaba and Ant Financial, users, third-party merchants, and, although in a less direct way, the Chinese government. Tarleton Gillespie notes several different

meanings of the notion of platform (Politics 349-350): from its origin in architecture, the notion of platform implies a figurative ‘raised surface’ which multiple parties can claim. As such, although platforms seem like neutral and open spaces, they empower whoever stands on it. This way, a platform also bears a political meaning: it is the stage from which one can make a political statement.

The meaning of the term platform also has a computational dimension: it denotes the technological "infrastructure that supports the design and use of particular applications" (Gillespie, Politics 349). Nick Srnicek also notes that the positioning of platforms between different actors, and by facilitating the digital infrastructure that allows for their interaction, platforms have the privileged access to record these interactions (44). It is in these multiple meanings of the term that this chapter views Sesame Credit as a technological platform that mediates, monitors, and modifies social interaction.

I use Latour’s Actor-Network-Theory (ANT) to analyse the technological actors that operate within Sesame Credit. The main reason for this is that ANT shows how power relations are mediated, instead of merely accepting them as social facts. In his book

Reassembling the Social: An Introduction to Actor-Network-Theory, Latour criticises

sociologists who rely on ‘social forces’, such as ‘power’, as an explanation for states of affairs: “power, like society, is the final result of a process and not a reservoir, a stock, or a capital that will automatically provide an explanation” (Latour 64). Rather, ANT aims to

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explain how social ties, and with that power imbalances, are constituted and composed in the first place. This way, this chapter interrogates and nuances the broad power imbalances sketched out in Chapter 1.

As is often celebrated, ANT recognises the agency of non-human entities. By accepting them as “participants in the course of action”, ANT allows a perspective on how Sesame Credit, and its technological components, shapes the interaction between Alibaba, users, merchants, and the government (Latour 71; emphasis in original). This opens up the discussion on the seeming neutrality of platforms hinted at earlier; although Sesame Credit might look like an intermediary that facilitates interaction, it is a mediator that transforms, translates, distorts and modifies it (39).

With ANT’s recognition of non-human agency, an ethical problem emerges: as humans are not the only accountable actants, responsibility and accountability diffuse over the network of actors. Arjun Appadurai addresses this problem of new materialism and suggests to focus on what he calls ‘mediants’, rather than on actants. He grounds his idea in Deleuzian assemblage theory (Deleuze and Guattari) and the concept of dividuality – the idea that human subjects are divided into, and composed of, a multiplicity of monads, molecules (etc.) that are temporarily and non-hierarchically in association with each other (Deleuze 5).10_{A mediant, he explains, is a “dynamic assemblage of the human dividual that}

is available to blend with and catalyze other nonhuman mediants (and actants) to produce effective and durable patterns of assemblage” (232).

What is at stake here is the mediation of normativity through the material

assemblage of human and non-human dividuals. For example, we can identify as mediant the assemblage of Sesame Credit’s algorithms, the parts of the human programmer devoted to programming, the computer and screen the programmer works on, etc. This way,

Appadurai’s theory allows accepting the agency non-human actants, while putting particular emphasis on the (dividual) human agency in the assemblage. This chapter focuses on

several human actors. What we must take from Appadurai’s theory is that these non-human actors are mediating (Alibaba’s) normativity through their association with non-human actors, such as programmers, designers, CEO’s. They are not autonomous actors in and of themselves but come to do what they do (partly) through human intentionality.

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The third reason why Ant is suited to analyse Sesame Credit as a technological platform is that as ANT focuses on relations between actors, it provides a solid theoretical basis to map out the technological infrastructure of Sesame Credit. José van Dijck

differentiates between five technological components that make up the infrastructure of a platform: data, algorithm, protocol, interface, and default. What makes these components actors, and accordingly constitutes the infrastructure, is the momentary connections

between them and actors outside the technology (e.g. content and users). It is precisely the relational quality of these technological actors that makes them function as a digital

infrastructure.

Protocols are responsible for the distribution of data as they “are formal descriptions of digital message formats complemented by rules for regulating those messages in or between computing systems.” (Van Dijck, Culture 31) Protocols govern the flows of data between Sesame Credit and other systems and thus play an essential role in the

computation of the score.11

Defaults are “settings automatically assigned to a software application to channel user behavior in a certain way” (Van Dijck, Culture 32). While for many platforms default settings are important mechanisms for the management of user behaviour, Sesame Credit mainly does this through its content (the score and advertisements) and interface. There was, however, one controversy at the start of 2018 about a default setting. An article of the South China Morning Post (owned by Alibaba) reports that the ‘Alipay Annual User Footprint Report’, which allows users to look up how often they have used Alipay in the previous year and for what purposes, “had a box that was checked by default, consenting to the ‘Zhima Credit Service Agreement’” (Zen). According to the article, Alibaba apologised for the ‘mistake’ and removed the opt-in feature from the User Footprint Report immediately.

Apart from this example, however, default settings do not play a significant role in Sesame Credit’s construction of norms. And although protocols are significant actors in the technological infrastructure of Sesame Credit, they are not the primary sites through which disciplinary power operates. As such, I focus my analysis on the other three technological actors: data, algorithm and interface.

Social Credit and the Quantification of Everyday Life: Sesame Credit’s Mediation of Power Relations in China’s Credit Culture