Risk-Based and Proportionate Regulation of Generative AI: A Study on Content Moderation, Disinformation, and Cybersecurity

Yuanquan Meng ()
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Yuanquan Meng: Capital University of Economics and Business

A chapter in Proceedings of the 2026 3rd International Conference on Applied Economics, Management Science and Social Development (AEMSS 2026), 2026, pp 482-492 from Springer

Abstract: Abstract The governance challenges of generative AI are increasing in content moderation, disinformation, and cybersecurity fields. This study develops a dual-dimensional analytical framework combining risk tiering and proportionality assessment to assess the effectiveness of regulations from four major jurisdictions (EU, US, China, the UK). Through comparative analysis using the proportionality assessment tool (suitability, necessity, and proportionality stricto sensu), the study reveals a descending order of regulatory effectiveness from the cybersecurity domain to disinformation. This study reveals that proportionality effectiveness is limited by the quantifiability of the risk and the complexity of value tension. The cybersecurity domain shows the highest proportionality effectiveness because of the well-defined technical indicators and fewer conflicts of rights. In contrast, the disinformation domain is challenged by the unclear risk boundaries and the complexity of freedom of expression. Current frameworks exhibit notable progress in risk classification, particularly the EU AI Act's four-tier system, yet structural deficiencies persist in systematic proportionality application. This study provides a novel proportionality assessment tool for evaluating the effectiveness of the regulations and proposes domain-specific strategies to improve proportionality effectiveness.

Keywords: Generative AI; Risk-based regulation; Proportionality principle; Content moderation; Disinformation; Cybersecurity; AI governance (search for similar items in EconPapers)
Date: 2026
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DOI: 10.2991/978-94-6239-672-2_46

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