AI Risk Repository
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AI Risk Repository
Overview :
The AI Risk Repository is a comprehensive living database that catalogs over 700 AI risks, categorized by their causes and risk domains. It provides an accessible overview of AI risks and serves as a common reference framework for researchers, developers, businesses, assessors, auditors, policymakers, and regulators, aiding in the development of research, curriculum, audits, and policies.
Target Users :
The target audience includes policymakers, risk assessors, scholars, and industry professionals who need to understand and evaluate the risks posed by AI technologies and to formulate corresponding policies, audits, and risk mitigation strategies.
Total Visits: 43.8K
Top Region: US(51.10%)
Website Views : 48.9K
Use Cases
Policymakers use this tool to understand the landscape of research and policy, conducting risk assessments to inform policy decisions.
Risk assessors utilize this tool to identify new risks, understand the risk landscape, and discuss potential assessments with clients.
Scholars use this tool as a foundation for developing other classifications, seeking underexplored areas in AI risk research.
Industry professionals use this tool for internal risk assessments, identifying new risks, and developing research and training programs.
Features
AI Risk Database: Links each risk to source information, supporting evidence, and causal and domain classifications.
Causal Classification: Categorizes AI risks based on how, when, and why they occur.
Domain Classification: Divides AI risks into seven domains and 23 sub-domains.
Information Updates: Provides regular updates on new risks and research sources.
Common Reference Framework: Offers a shared framework for various groups to discuss AI risks.
Risk Search: Facilitates the discovery of relevant risks and studies.
How to Use
Visit the AI Risk Repository website.
Browse the AI risk database to learn about specific risks and their classifications.
Utilize the causal classification and domain classification methods to filter and identify specific risks.
Search for specific risks or studies as needed.
Leverage the provided information to develop research, courses, audits, and policies.
Participate in feedback by suggesting any missing risks or resource recommendations.
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