The rapid development of artificial intelligence (AI) has led to increasing concerns about the capability of AI systems to make decisions and behave responsibly. Responsible AI (RAI) refers to the development and use of AI systems that benefit humans, society, and the environment while minimising the risk of negative consequences. To ensure responsible AI, the risks associated with AI systems' development and use must be identified, assessed and mitigated. Various AI risk assessment frameworks have been released recently by governments, organisations, and companies. However, it can be challenging for AI stakeholders to have a clear picture of the available frameworks and determine the most suitable ones for a specific context. Additionally, there is a need to identify areas that require further research or development of new frameworks. To fill the gap, we present a survey of 16 existing RAI risk assessment frameworks from the industry, governments, and non-government organizations (NGOs). We identify key characteristics of each framework and analyse them in terms of RAI principles, stakeholders, system lifecycle stages, geographical locations, targeted domains, and assessment methods. Our study provides a comprehensive analysis of the current state of the frameworks and highlights areas of convergence and divergence among them. We also identify the deficiencies in existing frameworks and outlines the essential characteristics a concrete framework should possess. Our findings and insights can help relevant stakeholders choose suitable RAI risk assessment frameworks and guide the design of future frameworks towards concreteness.

翻译：人工智能（AI）的快速发展引发了对AI系统决策能力及其负责任行为的日益关注。负责任AI（RAI）指的是开发和使用的AI系统应造福人类、社会与环境，同时最大限度降低负面后果风险。为确保AI的负责任性，必须识别、评估并缓解与AI系统开发和使用相关的风险。近年来，各国政府、组织及企业相继发布了多种AI风险评估框架。然而，AI利益相关方很难全面了解现有框架体系，并针对特定情境选择最合适的框架。此外，识别需要进一步研究或开发新框架的领域也迫在眉睫。为填补这一空白，我们对来自工业界、政府及非政府组织（NGO）的16个现有RAI风险评估框架进行了综述。我们梳理了每个框架的关键特征，并从RAI原则、利益相关方、系统生命周期阶段、地理分布、目标领域及评估方法等维度进行分析。本研究对现有框架现状进行了综合分析，揭示了各框架之间的共性与差异，同时指出现有框架的不足之处，并阐述了具体框架应具备的核心特征。我们的发现与见解可帮助相关利益方选择合适的RAI风险评估框架，并为未来框架的具体化设计提供指导。