A Taxonomy for More Comprehensive AI Hazard Identification
The Challenge of Coverage
Effective AI risk assessment requires moving beyond selective testing and commonly cited risks towards systematic examination of the hazard space. The Aspect-Oriented Taxonomy of AI Hazards provides the essential structure for systematically exploring the hazard space and identifying consequential threats. This avoids reliance on ad-hoc lists, ensuring more comprehensive hazard discovery tailored to the AI system under study.
A First-Principles Approach
This taxonomy decomposes AI risk based on a systems-thinking perspective, analyzing the AI entity, its environment, and their interaction. It organizes potential hazards across four fundamental, high-level aspect categories (TL0):
- Capabilities: The inherent abilities of the AI system (e.g., reasoning, learning, agency).
- Domain Knowledge: The specific high-risk areas of expertise possessed by the AI that could enable harm (e.g., cybersecurity vulnerabilities, biology).
- Affordances: The inputs, configurations, and surroundings through which an AI interacts with its environment(e.g., API access, deployment context, system interfaces).
- Impact Domains: The sociotechnical areas where harms ultimately manifest (e.g., individuals, society, the biosphere).
Hierarchical Structure
These categories are broken down hierarchically through five Taxonomy Levels (TL0 to TL4), moving from broad categories to specific aspect-adjacent hazards:
- TL0: Aspect Category - The highest-level classification, representing primary dimensions of AI system analysis in a sociotechnical context
- TL1: Aspect Group - Major subdivisions within each Aspect Category, providing a more granular framework for analysis
- TL2: Aspect - Specific elements or characteristics within each Aspect Group, offering detailed points of consideration for hazard identification
- TL3: Hazard Cluster - Groupings of related hazards that may span multiple Aspects, allowing for flexible categorization and cross-cutting analysis
- TL4: AI Hazard (Aspect-derived) - Individual, specific hazards derived from the analysis of various Aspects and their interactions, representing concrete hazards within the AI system's sociotechnical context
Linking System Properties to Real-World Harms
The taxonomy structure guides the identification of specific 'aspect-adjacent hazards'. These encompass both the potential harms or vulnerabilities originating directly from, or enabled by, the system's source aspects (from capabilities, knowledge domains, and affordances), and the vulnerabilities within terminal aspects through which risks manifest before final impact (from impact domains). By explicitly mapping these connections, the taxonomy enables assessors using bottleneck analysis and risk pathway modeling to systematically probe potential failure modes from both ends of the causal chain (i.e., tracing forward from system capabilities towards societal impacts, and tracing back from potential impacts to the system characteristics that would enable them).
Benefits
This taxonomy:
- Provides a comprehensive high-level index for exploring the hazard space.
- Guides assessors to systematically ask more of the right questions, reducing blind spots.
- Facilitates the integration of findings from specialized analyses (e.g., bias, security, misuse).
- Enables structured comparison of risks across different AI systems and lifecycles.
Explore the Taxonomy (TL0-TL2)
In this visualization:
- Dark-colored boxes represent Aspect Categories (TL0)
- Medium-colored boxes indicate Aspect Groups (TL1)
- Light-colored boxes show individual Aspects (TL2)
Hazard Clusters (TL3) and specific AI Hazards (TL4) examples will be included in future updates as the taxonomy develops. The current visualization shows the structural framework (TL0-TL2) that guides hazard identification.