Research on Trust Mechanism Construction and Risk Governance in Public Policy Communication under the Perspective of Human-Machine Collaboration

With the deep integration of artificial intelligence into the field of public policy communication, the paradigm of human-machine collaboration is profoundly reshaping traditional trust building and risk governance mechanisms. This study aims to explore how collaborative interaction between human actors and intelligent systems affects the credibility, transparency, and public acceptance of policy information. Drawing on theories of communication, governance, and science and technology studies, the paper constructs a theoretical framework that integrates trust mechanism design with risk governance strategies in digitally mediated policy processes. Within this framework, it analyzes optimization paths for enhancing information authenticity, improving algorithmic transparency, and broadening meaningful public participation. Particular attention is paid to how algorithmic decision support, automated content generation, and data-driven targeting influence perceptions of fairness, accountability, and reliability in policy communication. The study further discusses potential risks, including information distortion, opacity of algorithmic logic, and the amplification of bias, and proposes corresponding governance tools such as disclosure norms, oversight mechanisms, and participatory feedback channels. The research findings are expected to provide theoretical support and practical implications for enhancing the efficiency, responsiveness, and legitimacy of policy communication, thereby contributing to the modernization of governance capacity in the intelligent era.