Transparency & Explainability

https://taxonomy.eticas.ai/risk/transparency-explainability

Maturity: established

The risk that stakeholders cannot understand how an AI system works, what it does, or why it produces specific outputs. Lack of transparency undermines informed consent, impedes oversight, and erodes trust.

Also known as: Explainability & Transparency · Transparency and Explainability

Applies to: ALL
Lifecycle stages: Post Processing

Risk groups

Explainability — Risks from stakeholders being unable to understand why the system produces specific outputs.
Communication & disclosure — Risks from inadequate communication about system capabilities, limitations, and AI involvement.

Mappings to external frameworks

Compliance

Framework	Concept
EU AI Act (Regulation 2024/1689)	Article 13 — Transparency and provision of information
ISO/IEC 42001:2023 — AI Management System	A.8 Information for interested parties + A.6.2.8 documentation
AIUC-1 — AI Underwriting Company Standard	Implement AI disclosure mechanisms + E.17 transparency policy

Reference frameworks

Framework	Concept
NIST AI Risk Management Framework (AI 100-1)	Explainable & Interpretable
OECD AI Principles	Transparency & explainability
NIST AI 600-1 — Generative AI Risk Profile	Information Integrity (provenance) + Value Chain

Taxonomies & vocabularies

Framework	Concept
MIT AI Risk Repository	Lack of transparency or interpretability
W3C Data Privacy Vocabulary — AI Extension	Transparency
IBM AI Risk Atlas	Output → Explainability + Non-technical → Transparency

Part of the Eticas AI Risk Taxonomy. URI: https://taxonomy.eticas.ai/risk/transparency-explainability