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
System type: ADM and LLM systems
Lifecycle stages: Post Processing
| Framework | Reference |
|---|---|
| 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 |
| Council of Europe Framework Convention on AI (CETS No. 225) | Article 9 — Transparency and oversight |
| IEEE Std 7001-2021 — Transparency of Autonomous Systems | IEEE 7001-2021 (whole standard) |
| 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 |
| IEEE Std 2894-2024 — Architectural Framework for Explainable AI (Guide) | IEEE 2894-2024 (whole guide) |
| Framework | Reference |
|---|---|
| MIT AI Risk Repository | Lack of transparency or interpretability |
| W3C Data Privacy Vocabulary — AI Extension | ai:TransparencyRisk + ai:ExplainabilityRisk (DPV AI extension) |
| IBM AI Risk Atlas | Output → Explainability + Non-technical → Transparency |