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This paper provides a comprehensive overview of risk assessment and management methodologies for AI systems in light of emerging regulatory frameworks, particularly the AI Act. It systematically reviews the global regulatory landscape and characterizes various AI-related risks, including technical failures and ethical concerns. Key findings emphasize the need for standardized risk assessment practices and identify methodological gaps that require further exploration to enhance the safety and reliability of AI systems.
Current AI risk assessment practices are inadequate, revealing critical gaps that could jeopardize the safety of intelligent systems in a rapidly evolving regulatory environment.
The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems. In response to this imperative, this paper presents an overview of AI risk assessment (identification and analysis) and management methodologies. It begins by reviewing the worldwide regulatory landscape that drives the need for systematic AI risk assessment. Then we characterize the spectrum of AI-related risks identified in the literature, from technical failures to ethical and social impacts. Subsequently, it reviews key risk assessment methodologies proposed for AI systems, focusing on general frameworks. The paper highlights best practices and illuminates methodological gaps, highlighting areas for further research on AI risk assessment.
