List of Taxonomies

Summary

The AI Incident Database's List of Taxonomies serves as a central hub for multiple classification frameworks designed to categorize AI incidents and risks systematically. This living repository goes beyond simple incident logging by providing structured taxonomies that help organizations understand the technological and process factors contributing to AI failures. Connected to the MIT AI Risk Repository, it offers a comprehensive approach to risk categorization that bridges academic research with practical incident analysis.

What makes this collection unique

Unlike standalone taxonomies that focus on single aspects of AI risk, this repository provides multiple complementary classification systems that work together. Each taxonomy captures different dimensions of AI incidents - from technical failures in machine learning pipelines to organizational process breakdowns. The taxonomies are continuously refined based on real-world incident data, making them living documents that evolve with the field.

The connection to actual incident cases sets this apart from theoretical risk frameworks. Every category and subcategory is grounded in documented examples, providing concrete reference points for classification decisions.

The taxonomy ecosystem explained

The repository contains several interconnected taxonomies, each serving specific analytical purposes:

Technological Factor Taxonomies categorize incidents based on technical root causes - model architecture issues, data quality problems, deployment failures, and algorithmic biases. These help technical teams identify recurring patterns in AI system failures.

Process Factor Taxonomies focus on organizational and procedural contributors to incidents, including inadequate testing, poor stakeholder communication, and governance failures. These are particularly valuable for risk management and compliance teams.

Incident Severity Classifications provide standardized ways to assess the impact and scope of AI incidents, enabling better resource allocation for response and remediation efforts.

The taxonomies link to the MIT AI Risk Repository for broader risk context, creating a comprehensive classification ecosystem that spans from specific incident details to systemic risk patterns.

Who this resource is for

AI safety researchers and practitioners will find detailed categorization schemes for analyzing incident patterns and identifying emerging risk areas across different AI application domains.

Risk management professionals can use these taxonomies to develop standardized incident classification processes, enabling consistent risk assessment and regulatory reporting across their organizations.

Policy makers and regulators benefit from the structured approach to understanding AI incident types and their contributing factors, supporting evidence-based policy development and enforcement activities.

AI development teams can leverage the taxonomies during design and testing phases to anticipate potential failure modes and implement appropriate safeguards before deployment.

Insurance and legal professionals working with AI liability cases will find standardized terminology and classification schemes that support consistent case analysis and precedent development.

Practical applications in action

Organizations typically start by selecting the most relevant taxonomies for their use cases. A financial services firm might focus heavily on bias-related categories and algorithmic transparency factors, while a healthcare organization might prioritize safety-critical failure modes and patient impact classifications.

The taxonomies work best when integrated into existing incident response workflows. Teams can map their internal incident categories to the standardized taxonomy terms, enabling benchmarking against industry patterns and contributing to the broader knowledge base.

For regulatory compliance, the standardized categories help organizations demonstrate systematic approaches to risk identification and incident analysis, particularly valuable for emerging AI governance requirements.

Getting the most from these taxonomies

Start with the overview documentation to understand how different taxonomies relate to each other before diving into specific classification schemes. The interconnected nature means that most incidents will span multiple taxonomies.

Consider the taxonomies as starting points rather than rigid constraints. Many organizations adapt the categories to fit their specific contexts while maintaining alignment with the core framework for external reporting and benchmarking.

Regular engagement with the AI Incident Database community helps keep classification approaches current as new incident types emerge and taxonomies evolve based on collective learning.