Beyond Consent: A PPP-Based Governance Framework for the Legal Regulation of Facial Recognition in Public Spaces
DOI: https://doi.org/10.62517/jel.202614123
Author(s)
Ziye Wang
Affiliation(s)
School of Foreign Languages, Shanghai Jiao Tong University, Shanghai, China
Abstract
The ubiquitous Facial Recognition Technology (FRT) in public infrastructure has brought on the structural collapse of the “notice and consent” paradigm. In structural environments, consent has become illusory due to asymmetric power and functional expansion. In order to address this systemic lack of legitimacy, this study argues that it is insufficient to gradually improve frameworks based on individual rights, and further a regulatory paradigm shift grounded in Public-Private Partnership (PPP) is put forward. By reconceptualizing biometric data as a quasi-public asset, the author hypothesizes a dual mechanism integrating risk allocation and revenue sharing, to provide a potentially more viable alternative to the challenged consent-based models. Under this mechanism, technological risks can be internalized through contractual liability, mandatory algorithmic insurance, and residual state responsibility. Equally important, data-derived value can be partially recaptured through a data premium tax, and be reinvested via a public interest data trust. Through a comparative analysis of China, the UK, and the U.S., the framework proposed here suggests a modular and transferable approach, which is capable to embed accountability and distributive justice into FRT governance.
Keywords
Facial Recognition Technology; Public-Private Partnership; Biometric Data; Quasi-Public Asset; Algorithmic Governance
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