构建可信赖AI系统的五维框架，推动技术与治理协同落地
A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governance
- 从领域定义到治理，整合五大责任维度并强化闭环反馈
- 提出贯穿全生命周期的问责机制，支持部署后监控与风险审计
- 适合政策制定者、企业AI负责人及合规团队参考实践
负责任人工智能（RAI）应对高风险场景下AI部署的伦理与监管挑战。本文提出一个整合五个核心维度的RAIS框架：领域定义、可信AI设计、可审计性、问责制与治理。不同于以往将这些要素孤立处理的研究，本框架强调其相互依赖关系与迭代反馈回路，实现全生命周期中的主动与被动问责。除呈现框架外，本文还综述全球AI治理进展，分析现有原则导向方法的局限性，包括碎片化、执行缺口以及对参与式治理的需求。同时指出关键挑战与研究方向，如行业适配性、可操作化问题，以支持认证、部署后监测与基于风险的审计。通过连接技术设计与制度责任，该工作为在AI全生命周期中嵌入责任提供实用蓝图，助力实现透明、伦理对齐且合法合规的AI系统。
原文摘要 · Abstract (English)
Responsible Artificial Intelligence (RAI) addresses the ethical and regulatory challenges of deploying AI systems in high-risk scenarios. This paper proposes a comprehensive framework for the design of an RAI system (RAIS) that integrates five key dimensions: domain definition, trustworthy AI design, auditability, accountability, and governance. Unlike prior work that treats these components in isolation, our proposal emphasizes their inter-dependencies and iterative feedback loops, enabling proactive and reactive accountability throughout the AI lifecycle. Beyond presenting the framework, we synthesize recent developments in global AI governance and analyze limitations in existing principles-based approaches, highlighting fragmentation, implementation gaps, and the need for participatory governance. The paper also identifies critical challenges and research directions for the RAIS framework, including sector-specific adaptation and operationalization, to support certification, post-deployment monitoring, and risk-based auditing. By bridging technical design and institutional responsibility, this work offers a practical blueprint for embedding responsibility throughout the AI lifecycle, enabling transparent, ethically aligned, and legally compliant AI-based systems.
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