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.

翻译：负责任人工智能（RAI）旨在应对在高风险场景中部署AI系统所面临的伦理与监管挑战。本文提出了一个用于设计RAI系统（RAIS）的综合框架，该框架整合了五个关键维度：领域定义、可信AI设计、可审计性、问责制与治理。与以往孤立处理这些组件的研究不同，我们的方案强调其相互依赖性与迭代反馈循环，从而在整个AI生命周期中实现主动与被动的问责。除了提出该框架外，本文还综合了全球AI治理的最新进展，分析了现有基于原则的方法的局限性，重点指出了其碎片化、实施差距以及对参与式治理的需求。本文亦指出了RAIS框架面临的关键挑战与研究方向，包括特定领域的适应与操作化，以支持认证、部署后监测及基于风险的审计。通过桥接技术设计与制度责任，本工作为在整个AI生命周期中嵌入责任提供了实用蓝图，从而构建透明、符合伦理且合法合规的基于AI的系统。