JOURNAL ARTICLE

Privacy and personal information protection in China's all-seeing state.

  • Published In: International Journal of Law & Information Technology, 2023, v. 31, n. 4. P. 349 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Ong, Rebecca 3 of 3

Abstract

This article examines the widespread deployment of artificial intelligence (AI)-enabled facial recognition technology (FRT) in China's public and private sectors, focusing on the regulatory, societal, and ethical challenges it raises, particularly regarding privacy and personal information (PI) protection. It outlines China's evolving legal framework—including the Civil Code (CC), Cybersecurity Law (CSL), and Personal Information Protection Law (PIPL)—highlighting progress made and existing gaps such as unclear definitions of biometric data, lack of mandatory prior authorization for FRT deployment, and limited individual legal recourse for privacy violations. The article also discusses cultural factors influencing Chinese attitudes toward privacy, noting a general willingness to trade privacy for security and convenience, alongside emerging public concerns exemplified by several high-profile cases. It concludes by recommending greater transparency, multi-disciplinary expert involvement, and the adoption of an adaptive governance framework to balance technological innovation with fundamental rights protection.

Additional Information

  • Source:International Journal of Law & Information Technology. 2023/12, Vol. 31, Issue 4, p349
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2023
  • ISSN:09670769
  • DOI:10.1093/ijlit/eaae003
  • Accession Number:176004684
  • Copyright Statement:Copyright of International Journal of Law & Information Technology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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