AI Risk Database: A Comprehensive Understanding of Potential Threats from Artificial Intelligence

Explore the AI Risk Database developed by the MIT FutureTech team, a comprehensive resource containing over 700 AI risks. This article delves into the structure, uses, and importance of this powerful tool, providing valuable insights for researchers, policymakers, and industry professionals.

What is the AI Risk Database?

The AI Risk Database is a comprehensive resource designed to help us better understand and address potential risks associated with artificial intelligence. It consists of three main components:

1. AI Risk Database: Contains over 700 risks extracted from 43 existing frameworks, including citations and page numbers.
2. AI Risk Causal Taxonomy: Categorizes how, when, and why these risks occur.
3. AI Risk Domain Taxonomy: Classifies these risks into seven domains (such as “Misinformation”) and 23 sub-domains (such as “False or Misleading Information”). This database not only provides valuable resources for researchers and developers but also offers a common frame of reference for businesses, assessors, auditors, policymakers, and regulators.

How to Use the AI Risk Database?

The AI Risk Database has a wide range of applications, including but not limited to:

• Providing a comprehensive overview of AI risk areas
• Regularly updating information on new risks and research
• Offering a common frame of reference for various professionals
• Assisting in the development of research, courses, audits, and policies
• Facilitating easy lookup of relevant risks and research Users can copy and use the database through Google Sheets or OneDrive.

AI Risk Causal Taxonomy

The AI Risk Causal Taxonomy categorizes how, when, and why AI risks occur. This taxonomy provides three levels of depth, which users can explore in an interactive chart. This classification method helps:

• Identify specific types of risks (such as pre-deployment or post-deployment risks)
• Understand how each causal factor (entity, intent, and time) relates to each risk domain
• Identify intentional and unintentional variants of discrimination and toxicity For a deeper understanding of this taxonomy, read the preprint paper.

AI Risk Domain Taxonomy

The AI Risk Domain Taxonomy classifies AI risks into seven domains and 23 sub-domains. This classification method helps:

• Quickly identify risks in specific areas (such as misinformation)
• Understand the connections between different risk domains
• Provide a structured framework for research and policy-making Users can explore this taxonomy in an interactive chart to gain a deeper understanding of each domain and sub-domain.

Practical Applications of the Database

The AI Risk Database has wide-ranging applications, suitable for professionals in multiple fields:

Policymakers

• Understand the research and policy landscape
• Conduct risk assessments to inform policy decisions
• Monitor emerging risks to ensure comprehensive oversight
• Prioritize and plan funding

Risk Assessors

• Identify new, previously undocumented risks
• Understand the risk landscape to plan or create relevant assessments
• Develop specific risk determination criteria
• Define and communicate audit scope

Academics

• Develop other classification systems (such as actions to address specific types of risks)
• Discover underexplored areas in AI risk research
• Develop educational and training materials
• Validate newly identified risks

  Industry

• Conduct internal risk assessments
• Evaluate risk exposure and develop risk mitigation strategies
• Develop research and training programs

Frequently Asked Questions

1. How can I access the database without a Google account? You can access it through OneDrive. Better formatted versions or solutions will be provided in the future.
2. How was the AI Risk Database created? The team used a systematic search strategy, forward and backward searching, and expert consultation to identify 43 AI risk classifications, frameworks, and taxonomies. They adopted the Best Fit Framework Synthesis approach to create the taxonomy.
3. How should I cite the AI Risk Database? You can cite the preprint paper: Slattery, P., et al. (2024). A systematic evidence review and common frame of reference for the risks from artificial intelligence.
4. What are some limitations of the database?
   • Limited to risks from 43 documents (although over 17,000 records were screened)
   • May miss emerging, domain-specific risks and unpublished risks
   • May contain errors and subjective biases
   • Taxonomies prioritize clarity and simplicity over nuance
5. Why are there two taxonomies? During the synthesis process, it was found that the database roughly contained two classification systems: a high-level classification of AI risk causes and a mid-level classification of harms or injuries caused by AI. As these classification systems were vastly different and difficult to unify, two separate classification systems were created.

Conclusion

The AI Risk Database is a powerful tool that provides a comprehensive perspective for understanding and addressing potential risks brought by artificial intelligence. Whether you are a researcher, policymaker, or industry professional, this resource can provide valuable insights and guidance for your work. As AI technology rapidly develops, it becomes increasingly important to remain vigilant and knowledgeable about potential risks. By utilizing this database, we can better prevent and mitigate the challenges posed by AI while fully realizing its potential. For more information or to provide feedback, please visit the MIT FutureTech website or contact the project team directly. Let’s work together to ensure that AI development is both innovative and responsible.