Legal Risks And Governance Approaches Of Generative Artificial Intelligence

Abstract

As generative artificial intelligence (AI) penetrates deeply into various fields such as content creation, medical diagnosis, and intelligent customer service, its characteristics—such as automated algorithmic decision-making and large-scale data processing—have raised significant legal risks and regulatory challenges. In the domain of intellectual property, ambiguities in copyright attribution for AI-generated content and potential infringements on original work rights through data scraping during training pose multifaceted legal dilemmas. On the front of data security, issues like the leakage of personal sensitive information from training datasets and discriminatory decisions due to algorithmic black boxes challenge existing data protection frameworks. This paper analyzes the contradictions between the technical logic of generative AI and legal regulations, proposing a synergistic governance framework encompassing "legal regulation + technical compliance + industry self-regulation." It advocates for enhancing intellectual property legislation to clarify ownership of AI-generated works and boundaries of data acquisition while strengthening oversight on algorithm transparency. Establishing data compliance auditing systems and ethical review mechanisms for algorithms are essential steps toward achieving a dynamic balance between innovation and risk under the rule of law.