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

DOI

10.2991/978-94-6239-672-2_46How to use a DOI?

Keywords

Generative AI; Risk-based regulation; Proportionality principle; Content moderation; Disinformation; Cybersecurity; AI governance

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.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yuanquan Meng
PY  - 2026
DA  - 2026/05/12
TI  - Risk-Based and Proportionate Regulation of Generative AI: A Study on Content Moderation, Disinformation, and Cybersecurity
BT  - Proceedings of the 2026 3rd International Conference on Applied Economics, Management Science and Social Development (AEMSS 2026)
PB  - Atlantis Press
SP  - 482
EP  - 492
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-672-2_46
DO  - 10.2991/978-94-6239-672-2_46
ID  - Meng2026
ER  -