High-quality datasets serve as the critical foundation for the advancement of artificial intelligence; however, their open sharing faces structural tensions between data holders’monopolistic tendencies and the demand for the flow of data elements. The legal governance of competition must prioritize a dynamic balance between incentivizing data investment and facilitating data circulation, shifting from mere mandatory disclosure to a refined governance paradigm that balances rights protection with equitable access. To achieve this, the fundamental objectives must center on breaking data monopolies, preventing unjustified acquisition, and ensuring fair competition. This requires coordinated efforts across the hierarchical allocation of data property rights, tiered and categorized open access, circulation trading mechanisms, and security governance systems. Additionally, a systematic application of antitrust interventions, behavioral regulations, and supplementary intellectual property protections should be implemented. Together, these measures will establish a high-quality dataset governance system characterized by measured openness, orderly circulation, and robust protection, thereby cementing a solid foundation for AI innovation and equitable industrial development.

high-quality dataset, open sharing, legal governance of competition, antitrust law, anti-unfair competition law