Social Credit in China: Looking Beyond the “Black Mirror” Nightmare

The Chinese government’s Social Credit program has received much attention from Western media and academics, but misrepresentations have led to confusion over what it truly entails. Such mischaracterizations unhelpfully distract from the dangers and impacts of the realities of Social Credit. On March 31, 2021, Christiaan Van Veen and I hosted the sixth event in the Transformer States conversation series, which focuses on the human rights implications of the emerging digital state. We interviewed Dr. Chenchen Zhang, Assistant Professor at Queen’s University Belfast, to explore the much-discussed but little-understood Social Credit program in China.

Though the Chinese government’s Social Credit program has received significant attention from Western media and rights organizations, much of this discussion has often misrepresented the program. Social Credit is imagined as a comprehensive, nation-wide system in which every action is monitored and a single score is assigned to each individual, much like a Black Mirror episode. This is in fact quite far from reality. But this image has become entrenched in the West, as discussions and some academic debate has focused on abstracted portrayals of what Social Credit could be. In addition, the widely-discussed voluntary, private systems run by corporations, such as Alipay’s Sesame Credit or Tencent’s WeChat score, are often mistakenly conflated with the government’s Social Credit program.

Jeremy Daum has argued that these widespread misrepresentations of Social Credit serve to distract from examining “the true causes for concern” within the systems actually in place. They also distract from similar technological developments occurring in the West, which seem acceptable by comparison. An accurate understanding is required to acknowledge the human rights concerns that this program raises.

The crucial starting point here is that the government’s Social Credit system is a heterogeneous assemblage of fragmented and decentralized systems. Central government, specific government agencies, public transport networks, municipal governments, and others are experimenting with diverse initiatives with different aims. Indeed, xinyong, the term which is translated as “credit” in Social Credit, encompasses notions of financial creditworthiness, regulatory compliance, and moral trustworthiness, therefore covering programs with different visions and narratives. A common thread across these systems is a reliance on information-sharing and lists to encourage or discourage certain behaviors, including blacklists to “shame” wrongdoers and “redlists” publicizing those with a good record.

One national-level program called the Joint Rewards and Sanctions mechanism shares information across government agencies about companies which have violated regulations. Once a company is included on one agency’s blacklist for having, for example, failed to pay migrant workers’ wages, other agencies may also sanction that company and refuse to grant it a license or contract. But blacklisting mechanisms also affect individuals: the People’s Court of China maintains a list of shixin (dishonest) people who default on judgments. Individuals on this list are prevented from accessing “non-essential consumption” (including travel by plane or high-speed train) and their names are published, adding an element of public shaming. Other local or sector-specific “credit” programs aim at disciplining individual behavior: anyone caught smoking on the high-speed train is placed on the railway system’s list of shixin persons and subjected to a 6-month ban from taking the train. Localized “citizen scoring” schemes are also being piloted in a dozen cities. Currently, these resemble “club membership” schemes with minor benefits and have low sign-up rates; some have been very controversial. In 2019, in response to controversies, the National Development and Reform Commission issued guidelines stating that citizen scores must only be used for incentivizing behavior and not as sanctions or to limit access to basic public services. Presently, each of the systems described here are separate from one another.

But even where generalizations and mischaracterizations of Social Credit are dispelled, many aspects nonetheless raise significant concerns. Such systems will, of course, worsen issues surrounding privacy, chilling effects, discrimination, and disproportionate punishment. These have been explored at length elsewhere, but this conversation with Chenchen raised additional important issues.

First, a stated objective behind the use of blacklists and shaming is the need to encourage compliance with existing laws and regulations, since non-compliance undermines market order. This is not a unique approach: the US Department of Labor names and shames corporations that violate labor laws, and the World Bank has a similar mechanism. But the laws which are enforced through Social Credit exist in and constitute an extremely repressive context, and these mechanisms are applied to individuals. An individual can be arrested for protesting labor conditions or for speaking about certain issues on social media, and systems like the People’s Court blacklist amplify the consequences of these repressive laws. Mechanisms which “merely” seek to increase legal compliance are deeply problematic in this context.

Second, as with so many of the digital government initiatives discussed in the Transformer States series, Social Credit schemes exhibit technological solutionism which invisibilizes the causes of the problems they seek to address. Non-payment of migrant workers’ wages, for example, is a legitimate issue which must be tackled. But in turning to digital solutions such as an app which “scores” firms based on their record of wage payments, a depoliticized technological fix is promised to solve systemic problems. In the process, it obscures the structural reasons behind migrant workers’ difficulties in accessing their wages, including a differentiated citizenship regime that denies them equal access to social provisions.

Separately, there are disparities in how individuals in different parts of the country are affected by Social Credit. Around the world, governments’ new digital systems are consistently trialed on the poorest or most vulnerable groups: for example, smartcard technology for quarantining benefit income in Australia was first introduced within indigenous communities. Similarly, experimentation with Social Credit systems is unequally targeted, especially on a geographical basis. There is a hierarchy of cities in China with provincial-level cities like Beijing at the top, followed by prefectural-level cities, county-level cities, then towns and villages. A pattern is emerging whereby smaller or “lower-ranked” cities have adopted more comprehensive and aggressive citizen scoring schemes. While Shanghai has local legislation that defines the boundaries of its Social Credit scheme, less-known cities seeking to improve their “branding” are subjecting residents to more arbitrary and concerning practices.

Of course, the biggest concern surrounding Social Credit relates to how it may develop in the future. While this is currently a fragmented landscape of disparate schemes, the worry is that these may be consolidated. Chenchen stated that a centralized, nationwide “citizen scoring” system remains unlikely and would not enjoy support from the public or the Central Bank which oversees the Social Credit program. But it is not out of the question that privately-run schemes such as Sesame Credit might eventually be linked to the government’s Social Credit system. Though the system is not (yet) as comprehensive and coordinated as has been portrayed, its logics and methodologies of sharing ever-more information across siloes to shape behaviors may well push in this direction, in China and elsewhere.

April 20, 2021. Victoria Adelmant, Director of the Digital Welfare State & Human Rights Project at the Center for Human Rights and Global Justice at NYU School of Law.