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  4. Vol 7, Issue 3, 2025

Governing Big Data Swindling: Experiences from China

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DOI: 10.23977/jsoce.2025.070324 | Downloads: 0 | Views: 81

Author(s)

Jialin Li 1

Affiliation(s)

1 People's Daily Online Anhui Channel, Hefei, 230000, China

Corresponding Author

Jialin Li

ABSTRACT

Big Data Swindling—algorithmic price discrimination exploiting user data without consent—represents a growing threat to consumer rights. This study examines China's governance responses to the phenomenon through a multi-stakeholder lens. Findings reveal a governance framework anchored in policy regulation, corporate self-discipline, social oversight, and consumer empowerment, coalescing into a "government-led, multi-participatory" model. By leveraging state leadership while engaging platforms, civil society, and users, China has advanced collaborative efforts to curb exploitative data practices. Despite the borderless nature of digital governance, China’s experience offers valuable insights for global strategies in regulating algorithmic consumer harm.

KEYWORDS

Algorithm Discrimination; Algorithmic Governance; Big Data Swindling; Multi-Stakeholder

CITE THIS PAPER

Jialin Li, Governing Big Data Swindling: Experiences from China. Journal of Sociology and Ethnology (2025) Vol. 7: 177-182. DOI: http://dx.doi.org/10.23977/jsoce.2025.070324.

REFERENCES

[1] Burrell J, Fourcade M. The society of algorithms[J]. Annual review of sociology, 2021, 47(1): 213-237. DOI: 10.1146/annurev-soc-090820-020800.
[2] Zhu J. Theoretical proof and path optimization of anti-monopoly regulation of “big data-enabled price discrimination against existing customers”[J]. Journal of Northwest Minzu University (Philosophy and Social Sciences), 2021(05): 112-121. DOI:10.14084/j.cnki.cn62-1185/c.20210910.004.
[3] Darmody A, Zwick D. Manipulate to empower: Hyper-relevance and the contradictions of marketing in the age of surveillance capitalism[J]. Big Data & Society, 2020, 7(1): 2053951720904112. DOI:10.1177/2053951720904112.
[4] Blass J. Algorithmic advertising discrimination[J]. Nw. UL Rev., 2019, 114: 415.
[5] Shin D, Park Y J. Role of fairness, accountability, and transparency in algorithmic affordance[J]. Computers in Human Behavior, 2019, 98: 277-284. DOI:10.1016/j.chb.2019.04.019.
[6] Xu J. Opening the 'black box'of algorithms: regulation of algorithms in China[J]. Communication Research and Practice, 2024, 10(3): 288-296. DOI:10.1080/22041451.2024.2346415.
[7] Hu L. The principle and legal regulation for" big data swindle acquaintances"[C]. Atlantis Press, 2019: 417-4192019: 417-419. DOI:10.2991/jahp-19.2019.87.
[8] Ding W, Chen L. Regulating the use of big data: Justifications, perspectives and the Chinese way forward[J]. Computer Law & Security Review, 2022, 46: 105729. DOI:10.1016/j.clsr.2022.105729.
[9] Freeman R E. Strategic management: A stakeholder approach[M]. Cambridge university press, 2010.
[10] Parmar B L, Freeman R E, Harrison J S, 等. Stakeholder theory: The state of the art[J]. Academy of Management Annals, 2010, 4(1): 403-445. DOI:10.5465/19416520.2010.495581.
[11] Gasser U, Budish R, West S. Multistakeholder as governance groups: Observations from case studies[J]. Berkman Center Research Publication, 2015(2015-1).
[12] Cui D, Wu F. Moral goodness and social orderliness: An analysis of the official media discourse about Internet governance in China[J]. Telecommunications Policy, 2016, 40(2-3): 265-276. DOI:10.1016/j.telpol.2015.11.010.
[13] Gritsenko D, Wood M. Algorithmic governance: A modes of governance approach[J]. Regulation & Governance, 2022, 16(1): 45-62. DOI:10.1111/rego.12367.
[14] Hong Y, Goodnight G T. How to think about cyber sovereignty: The case of China[M]//China’s globalizing internet. Routledge, 2022: 7-25.
[15] Gunningham N, Rees J. Industry self-regulation: an institutional perspective[J]. Law & policy, 1997, 19(4): 363-414. DOI:10.1111/1467-9930.t01-1-00033.
[16] Xiao H. Developing responsible platform algorithm[J]. Journal of Xi’an Jiaotong University (Social Sciences), 2022, 42(01): 120-130. DOI:10.15896/j.xjtuskxb.202201013.
[17] Bonsón E, Bednárová M, Perea D. Disclosures about algorithmic decision making in the corporate reports of Western European companies[J]. International Journal of Accounting Information Systems, 2023, 48: 100596. DOI:10.1016/j.accinf.2022.100596.
[18] Radu R, Zingales N, Calandro E. Crowdsourcing ideas as an emerging form of multistakeholder participation in internet governance[J]. Policy & Internet, 2015, 7(3): 362-382. DOI:10.1002/poi3.99.
[19] Ahmad N, Milic N, Ibahrine M. Considerations about algorithmic literacy[J]. IT Professional, 2022, 24(4): 70-73. DOI:10.1109/MITP.2022.3193045.
[20] Silva D E, Chen C, Zhu Y. Facets of algorithmic literacy: Information, experience, and individual factors predict attitudes toward algorithmic systems [J]. New Media & Society, 2024, 26(5): 2992-3017. DOI: 10.1177/14614448221098042.
[21] Wang C. The regulation of big-data driven price discrimination: An analysis based on new institutional economics and game theory [J]. Jinan Journal (Philosophy & Social Sciences), 2021, 43(06): 52-64. DOI:10.3969/j.issn.1000-5072.2021.06.006.

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