Group 1 - The core argument emphasizes the importance of AI transparency, suggesting that without visibility into AI operations, trust and governance become challenging [1][4][13] - AI transparency is increasingly recognized as a global consensus, with regulatory bodies in China and the EU mandating clear labeling of AI-generated content to help users identify misinformation and reduce deception risks [2][5] - The evolution of AI from a tool to an autonomous agent necessitates a deeper understanding of its operational logic and societal impacts, which remains largely unknown [2][3] Group 2 - The concept of "AI Activity Labeling" is highlighted as a fundamental mechanism for enhancing transparency, allowing for the differentiation between human and AI interactions [2][5] - The article discusses the need for effective labeling practices, including what to label, who embeds the labels, and how to verify them, indicating a shift from merely identifying AI content to recognizing AI behavior [6][7][8] - The implementation of model specifications is proposed as another transparency mechanism, where AI companies outline expected behaviors and boundaries for their models, enhancing user understanding and trust [9][10] Group 3 - The article raises concerns about the enforcement of model specifications, questioning whether compliance should be mandatory and how to balance transparency with commercial confidentiality [11][12] - It emphasizes that transparency is crucial for bridging the gap between technological advancement and societal understanding, serving as a foundation for governance research and policy formulation [13][14] - The ultimate goal is to establish a verifiable, feedback-driven, and adaptable AI governance framework, ensuring that AI can be a trustworthy partner rather than an unpredictable force [13][14]