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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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On the compatibility of established methods with emerging artificial intelligence and machine learning methods for
Shital Thekdi1, Unal Tatar2, Joost Santos3
1Robins School of Business, University of Richmond, Richmond, Virginia, USA.
Risk Analysis : an Official Publication of the Society for Risk Analysis
|September 20, 2024
Summary
Advanced analytics like artificial intelligence (AI) and machine learning (ML) are transforming disaster risk analysis (RA). This study compares established RA methods with AI/ML approaches, evaluating their compatibility for future disaster risk assessment.
Area of Science:
- Disaster Risk Analysis
- Artificial Intelligence
- Machine Learning
Background:
- Growing interest in AI/ML for disaster risk analysis (RA).
- Need to compare emerging AI/ML methods with established RA practices.
- Established RA methods are widely accepted and used across various applications.
Purpose of the Study:
- Investigate the compatibility of established RA methods with AI/ML advancements.
- Evaluate AI/ML methods in disaster RA considering usefulness, trust, and explainability.
- Provide insights for integrating AI/ML into disaster RA practices.
Main Methods:
- Leveraged expert input from RA and AI professionals.
- Investigated compatibility between established RA methods and AI/ML techniques.
- Utilized empirical evidence from expert perspectives.
Main Results:
- Provided key insights into established and AI/ML-based disaster RA methods.
- Empirical evidence supports the compatibility evaluation of these methods.
- Highlighted factors like usefulness, trust, and explainability in AI/ML integration.
Conclusions:
- AI/ML methods offer new capabilities for disaster RA.
- Compatibility of AI/ML with established methods is crucial for advancement.
- Expert perspectives are vital for guiding the future of AI/ML in disaster RA.
Keywords:
artificial intelligencecausal inferencedisaster risk analysisexplainabilitymachine learningtrustworthinessMore Related Videos
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