  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, as well as updating and maintaining existing ones. To fill the gap,
we present a mapping study of 16 existing AI 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 of a concrete
and connected framework AI risk assessment (C$^2$AIRA) framework. Our findings
and insights can help relevant stakeholders choose suitable AI risk assessment
frameworks and guide the design of future frameworks towards concreteness and
connectedness.
