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
Status. Each mapping shows what it rests on. Reviewed means a reviewer has confirmed the mapping against the concept’s current definition and the framework edition the taxonomy currently references; when the only confirmation is that of the person who wrote the mapping, it reads Reviewed (author’s confirmation). Outdated means it was reviewed, but the concept’s definition has changed since, so the review has to be repeated. The other statuses mean that no review has been recorded yet. Inherited mappings have been in the taxonomy since before v5.7.0 and are unchanged. They carry no record of who made them or how, and some were checked by hand when the taxonomy first published its mappings. Agent-proposed mappings come from a review carried out by AI agents, which read the framework’s text and the concept’s definition and agreed on the relation; no person has confirmed them yet. Provisional mappings were made without access to the framework’s text. Unreviewed mappings are older mappings whose target or relation was revised in v5.7.0, and any other mapping without a review. Mappings to a superseded edition of a framework are not shown: where the provision continues, they are carried to the current edition. Mappings a review has changed or rejected are not shown.
| Framework | Reference | Status |
|---|---|---|
| EU AI Act (Regulation 2024/1689) | Article 13 — Transparency and provision of information to deployers | Inherited |
| ISO/IEC 42001:2023 — AI Management System | A.8 Information for interested parties of AI systems | Reviewed (author’s confirmation) |
| AIUC-1 — AI Underwriting Company Standard | Implement AI disclosure mechanisms | Inherited |
| Council of Europe Framework Convention on AI (CETS No. 225) | Article 8 — Transparency and oversight | Unreviewed |
| IEEE Std 7001-2021 — Transparency of Autonomous Systems | IEEE 7001-2021 (whole standard) | Inherited |
| NIST AI Risk Management Framework (AI 100-1) | Explainable and Interpretable | Inherited |
| NIST AI 600-1 — Generative AI Risk Profile | Information Integrity | Inherited |
| OECD AI Principles | 1.3 Transparency and explainability | Inherited |
| IEEE Std 2894-2024 — Architectural Framework for Explainable AI (Guide) | IEEE 2894-2024 (whole guide) | Inherited |
| Framework | Reference | Status |
|---|---|---|
| MIT AI Risk Repository | Lack of transparency or interpretability | Inherited |
| W3C Data Privacy Vocabulary — AI Extension | Transparency Risk | Inherited |