Abstract: Artificial intelligence technology is currently developing at an unprecedented pace, with safety concerns becoming a global focal point. Traditional AI safety research has predominantly relied on a “control paradigm”, emphasizing limitations, regulations, and value alignment to control AI behavior and prevent potential risks. However, as AI capabilities continue to strengthen, unidirectional control strategies are revealing increasingly significant limitations, with issues such as transparency illusions, adversarial evolution, and innovation suppression gradually emerging. Industry leaders like Sam Altman and Dario Amodei predict that AI may comprehensively surpass human capabilities in multiple fields within the next 23 years, making the reconstruction of AI governance paradigms particularly urgent. This paper proposes a new perspective—the “symbiotic paradigm”—emphasizing humanmachine collaboration as the core and understanding and trust as the foundation. Through establishing four pillars: transparent communication, bidirectional understanding, creative resonance, and dynamic boundaries, it promotes AI safety’s transition from control to cocreation, serving as one of the foundational paths for digital governance transformation. This paper systematically demonstrates the feasibility and necessity of the symbiotic paradigm through four dimensions: theoretical analysis, technological paths, practical cases, and governance recommendations, aiming to provide a sustainable alternative for future AI safety research and digital governance practices.

Key words: AI safety, control paradigm, symbiotic paradigm, humanmachine collaboration, digital governance

摘要： 当前人工智能技术呈现前所未有的快速发展态势，其安全性问题已成为全球关注的焦点.传统的AI安全研究主要基于“控制范式”，强调通过限制、监管与价值对齐控制AI行为，防止其带来潜在风险.然而，随着AI能力日益增强，单向控制策略暴露出日益显著的局限性，如透明性幻觉、对抗演化与创新压制等问题逐渐浮现.业界领袖如Sam Altman和Dario Amodei预测，AI在未来2~3年内可能在多个领域全面超越人类能力，这使得重构AI治理范式变得尤为紧迫.提出“共生范式”的新视角，强调以人机协作为核心、以理解与信任为基础，通过建立透明沟通、双向理解、创造性共振和动态边界4大支柱，推动AI安全从控制走向共创，并作为数字治理转型的基础路径之一.通过理论解析、技术路径、实践案例和治理建议4个维度，系统论证共生范式的可行性与必要性，旨在为未来AI安全研究与数字治理实践提供具有可持续性的替代方案.

关键词: AI安全, 控制范式, 共生范式, 人机协作, 数字治理