Generative Artificial Intelligence Governance Action Framework: Content Analysis Based on AIGC Incident Report Texts

Abstract

[Purpose/Significance] The breakthrough of Generative Artificial Intelligence（Generative AI）has led to the explosive growth of Artificial Intelligence Generated Content （AIGC）, which inevitably cause people to be negatively affected by information overload, information noise, information security, and other related issues, making social information governance face new challenges. This paper aims to analyze and discuss the characteristic attributes of AIGC incidents, so as to provide a reference for Generative AI governance in China. [Design/Methodology] Based on AI Incident Database（AIID）and taking AIGC-related incident reports as samples for content analysis, the types, causes, damage objects and countermeasures of existing AIGC incidents were discussed. [Findings/Conclusion] The diversity of the objects affected by AIGC incidents, the wide distribution of the scope, and the complex unknown of the potential harm, result in the resources and capabilities of any single actor not being able to effectively deal with the crisis. It is necessary for actors of government, enterprises and society to form a governance participation model of “diversity + coordination + checks and balances”, and to carry out information governance under the action framework of “context-consciousness-action”. [Originality/Value] This paper introduces AIID as a case source database, provides an intuitive demonstration of the relevant details of existing AIGC incidents and forms a three-level category framework for AIGC incident analysis through content analysis.The action frame of Generative AI governance formed in this study is helpful to promote the exploration and practice of Generative AI governance from a macro perspective.

Chen Guanze

MSc student in Computer Science

My research interests focus on the applcation of Artificial Intelligence in Health, social science and so on.