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Procedural fairness, accountability, and legitimacy in the AI-enabled state: evidence from China

Year: 2026
Type: article
Study design: Observational Study
Abstract: The integration of artificial intelligence (AI) into administrative, judicial, and regulatory processes is reshaping how legal rules are implemented and experienced in contemporary China. The study examines whether established legitimacy mechanisms continue to structure acceptance of AI-mediated legal duties. Drawing on procedural justice theory and responsibility-gap debates, the study develops a legitimacy-centered model in which perceived procedural fairness, perceived AI accountability, and trust in state institutions influence public and professional stakeholders’ acceptance of AI-embedded legal duties through perceived legitimacy of the AI-enabled state. AI literacy is incorporated as a moderating factor. Using a mixed-methods design that combines survey data ( N = 642) and semi-structured interviews ( n = 41) across the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and the Greater Bay Area, the study employs partial least squares structural equation modeling (PLS-SEM) to test direct, indirect, and moderated relationships. Results indicate that procedural fairness and AI accountability significantly enhance perceived legitimacy, which in turn strongly predicts acceptance of AI-mediated legal obligations such as algorithmically enforced traffic regulation and administrative decision-making. Trust in state institutions operates primarily through legitimacy rather than independently. Qualitative findings reveal conditional support: respondents acknowledge efficiency and consistency benefits but express concerns regarding opacity, data misuse, and reduced human oversight. The study demonstrates that algorithmic governance remains normatively dependent on legitimacy judgments and provides empirically grounded insight into AI-enabled authority within China’s centralized governance framework.
Authors: Guifeng Fan, Weihua Lyu
Language: English
Subfield: Safety Research
Open Access status: gold
Estimated APC paid: $1,890
OpenAlex ID: W7208827386
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