The Eticas AI Risk Taxonomy is a unified, machine-readable vocabulary of AI risk categories developed for use across Eticas’ audit methodologies, assessment frameworks, and reporting outputs. It is maintained by Eticas and published with stable per-concept URIs and a SKOS representation, making it interoperable with knowledge graphs, linked data systems, and semantic web tools.
The taxonomy provides a canonical structured vocabulary for naming and locating AI risks, suitable for citation in audit reports, regulatory submissions, research publications, and external framework cross-walks. Each concept carries a definition, alternative labels, lifecycle stage applicability, and formal cross-references to equivalent or related concepts in fourteen external frameworks.
External frameworks are organised in three buckets:
The bucketing matters in practice: the compliance bucket lets auditors demonstrate adherence to specific obligations, while the reference and academic buckets establish conceptual interoperability with the wider AI-risk research and policy ecosystem.
The taxonomy is organised in three levels — categories, sub-groups, and subcategories — covering ten top-level risk areas:
Not every category has sub-groups; categories with few subcategories (Environmental Impact, Human Agency & Control, Organisational Readiness) keep a flat structure. Concepts carry a maturity field — established or emerging — reflecting how settled their definition and assessment methods are.
The public site covers the whole active taxonomy — categories, sub-groups and subcategories at every maturity level — with external mappings shown without match-type qualifiers. The internal view covers the same concepts and adds the match types and the mechanisms used to assess each risk, as a working surface for ongoing methodology development. Retired concepts stay in the source for institutional memory and appear in neither view.
The boundary between public and internal content also marks the licensing boundary.
Public taxonomy — published under CC BY 4.0. What is covered by this open licence:
/risk/ — categories, sub-groups and subcategories at every maturity level./dist/taxonomy.ttl and /dist/taxonomy.jsonld — the same concepts and mappings, machine-readable.The SKOS distributions emit a dcterms:license triple pointing to the CC BY 4.0 deed, making the licence assertion machine-readable alongside the content.
Internal taxonomy — proprietary to Eticas. What sits on the internal side and is not covered by CC BY 4.0:
/risk-internal/ (Cloudflare Access required) — the same concepts as the public view, plus match-type-qualified mappings, citations on category-level mappings, and mechanisms./dist-internal/taxonomy.ttl and /dist-internal/taxonomy.jsonld (Cloudflare Access required). These distributions deliberately do not emit a dcterms:license triple — the absence is intentional, and consumers must not assume any open licence.The open-core split reflects how Eticas uses the taxonomy in practice. The public surface is the canonical structured vocabulary that external parties can cite and remix; the internal surface is a working source where ongoing methodology and assessment-practice development takes place.
Eticas. (2026). Eticas AI Risk Taxonomy, v5.2.0.
https://taxonomy.eticas.ai/risk/
The taxonomy follows Semantic Versioning: major for structural breaks affecting concept identifiers, minor for new categories or new framework mappings or schema additions, patch for definition refinements. Concept URIs (e.g., https://taxonomy.eticas.ai/risk/bias-fairness) are committed as stable for external citation from v1.0 onwards.
For corrections, alignment suggestions, or collaboration enquiries, contact Eticas at eticas.ai.