Research on AI-enabled collaborative governance mechanism for content security

In order to bridge the gap between technological optimization and institutional design in Internet content security governance, an integrated framework was constructed, incorporating deep learning-based review technology and multi-stakeholder collaboration. A methodology leading to a three-layer dynamic coupling governance model covering technology, process, and institution with an extended Stackelberg game framework was developed for formal modeling of the strategic interactions among regulators, platforms, Artificial Intelligence (AI) systems, and users. In this connection, an adaptive cross-modal confidence propagation algorithm was presented to improve the accuracy in reviewing multimodal content, together with a Thompson sampling-based dynamic threshold optimization mechanism. On comprehensive test sets, the accuracy of the dynamic collaboration mechanism was 94.6%, and game equilibrium attainment was 95.8%. Compared with pure manual review, costs were reduced by 76%, and efficiency was increased by 8.7 times. Meanwhile, the cross-modal confidence propagation algorithm showed an accuracy increase of 8.4% in high-uncertainty situations. Cross-scenario generalization capabilities have also been tested and verified on social media, short video, online education, and e-commerce platforms. The proposed collaborative governance mechanism can effectively balance accuracy, efficiency, and cost in content moderation and provide a theoretical basis for AI-enabled governance research.