Constructing a Data‐Driven Society: China's Social Credit System as a State Surveillance Infrastructure
Abstract
Big data technologies have been adopted by both the public and private sectors to develop and expand surveillance capacities. This article traces the institutional processes and political‐economic interests of the public and private stakeholders involved in the construction of China's Social Credit System (SCS), which is currently on track for full deployment on 1.4 billion citizens by 2020. The SCS aims to centralize data platforms into a big data–enabled surveillance infrastructure to manage, monitor, and predict the trustworthiness of citizens, firms, organizations, and governments in China. A punishment/reward system based on credit scores will determine whether citizens and organizations are able to access things like education, markets, and tax deductions. While the SCS is widely described by the Western news media as a means of “big brother” or political control, we find that it is a complicated system that focuses primarily on financial and commercial activities rather than political ones. This article presents a framework for understanding state surveillance infrastructures by exploring how various government agencies are cooperating to establish this centralized data infrastructure with the aim of scoring credit, and discussing the distinct but interconnected processes of data collection, data aggregation, and data analytics.
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China aspires to create a national-level social credit system to incentivize pro-government speech and action.
“China, for instance, aspires to create a national-level social credit system to incentivize pro-government speech and action.”
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- Authors
- Liang, F., Das, V., Kostyuk, N., & Hussain, M. M.
- Venue
- Policy & Internet
- Publisher
- Wiley
- Published
- 2018-08-02
- Volume
- 10
- Issue
- 4
- Pages
- 415-453
- ISSN
- 1944-2866 (ISSN-L)
- OpenAlex
- W2887516688
- Cited by
- 617
- References
- 24
- Citations by year
- 2026: 44 · 2025: 49 · 2024: 42 · 2023: 174 · 2022: 101 · 2021: 97 · 2020: 71 · 2019: 32 · 2018: 7
- FWCI
- 103.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Policy & Internet (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › COVID-19 Digital Contact Tracing
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- social credit system, big data, credit scoring, data platform, data collection, data aggregation, data analytics, trustworthiness, government agencies, China, data-driven society
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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