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Published on: October 25, 2024
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Artificial intelligence for risk analysis-A risk characterization perspective on advances, opportunities, and
Kaia Stødle1, Roger Flage1, Seth Guikema2
1Department of Safety, Economics and Planning, University of Stavanger, Stavanger, Norway.
Summary
Artificial intelligence (AI) offers new methods for risk analysis, framing it as an input-algorithm-output process. However, AI cannot fully automate risk analysis and decision-making due to critical concerns about uncertainty and risk-informed processes.
Area of Science:
- Risk analysis and artificial intelligence (AI).
- Development of novel AI-driven methodologies for risk assessment.
- Interdisciplinary applications of AI in scientific research.
Background:
- Artificial intelligence (AI) presents significant opportunities for advancing risk analysis.
- Current risk analysis methods can be enhanced through AI integration.
- The need for improved models in consequence and uncertainty characterization.
Purpose of the Study:
- To conceptualize the application of AI in risk analysis as an input-algorithm-output process.
- To link AI frameworks to key tasks in risk description: consequence, uncertainty, and knowledge management.
- To explore current and future uses of AI in risk analysis and discuss automation limits.
Main Methods:
- Conceptualizing AI for risk analysis using an input-algorithm-output framework.
- Reviewing current AI-based risk analysis concepts and methods.
- Extrapolating AI capabilities for future risk analysis outputs and decision support.
Main Results:
- AI can be utilized more extensively in risk analysis than currently practiced.
- AI frameworks can support consequence characterization, uncertainty characterization, and knowledge management.
- Current AI applications provide a foundation for future, more advanced risk analysis tools.
Conclusions:
- Opportunities exist to increase the use of AI in risk analysis.
- Critical concerns regarding uncertainty representation necessitate careful AI implementation.
- Full automation of risk analysis and decision-making is not feasible; human oversight remains essential for risk-informed decisions.
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