Lack of explainability and interpretability
The system's decisions cannot be adequately explained or interpreted, harming trust and auditability.
- Risk family
- Model & system behaviour
- MIT domain
- 7. AI System Safety, Failures, & Limitations
- MIT subdomain
- 7.4 > Lack of transparency or interpretability
- AI type
- GPAI, Classical_ML, Agentic
- Scope
- Both
- Source standard
- MIT AI Risk Repository v4
Provenance
16 source framework citation keys
Framework crosswalk
Every framework item mapped to this risk. Items marked partial overlap only in part; definitions appear on hover where the source licence permits.
- A.12 ISO/IEC 23894 Annex A A.12
- A.6.2.7 ISO/IEC 42001 Annex A A.6.2.7
- A.8.2 ISO/IEC 42001 Annex A A.8.2
- Art. 13
- Art. 26(11)
- Art. 50
- ibm-inaccessible-training-data Inaccessible training data
- ibm-unexplainable-and-untraceable-actions Unexplainable and untraceable actions
- ibm-unexplainable-output Unexplainable output
- ibm-unreliable-source-attribution Unreliable source attribution
- ibm-untraceable-attribution Untraceable attribution
More in Model & system behaviour
Part of the Deployer AI Risk Register, an open-source resource developed by MindXO. Version 1.0, 3 July 2026. Derived from the MIT AI Risk Repository (V4, December 2025) under CC BY 4.0; an independent derivative work, not endorsed by or affiliated with MIT. Sub-risk decomposition references MITRE ATLAS™ v5.6.0 (© 2021-2026 The MITRE Corporation, reproduced and distributed with permission). ISO/IEC and EU AI Act references are by number only. License: CC BY 4.0. Full attribution and licensing.