https://taxonomy.eticas.ai/risk/reliability
Maturity: established
Risks arising from an AI systems failure to perform dependably — whether through degraded output integrity and robustness (e.g., hallucinations, model drift), or through inability to maintain function under adverse or changing conditions (e.g., infrastructure failure, connectivity loss).
Also known as: Validity and Reliability
System type: ADM and LLM systems
Lifecycle stages: Pre Processing, In Processing, Post Processing
| Framework | Reference |
|---|---|
| EU AI Act (Regulation 2024/1689) | Article 15 — accuracy, robustness and cybersecurity |
| ISO/IEC 42001:2023 — AI Management System | AI system verification and validation |
| AIUC-1 — AI Underwriting Company Standard | Reliability domain |
| Council of Europe Framework Convention on AI (CETS No. 225) | Article 4(b) — Reliability (general obligation) |
| NIST AI 600-1 — Generative AI Risk Profile | Confabulation |
| NIST AI 600-1 — Generative AI Risk Profile | Information Integrity |
| NIST AI Risk Management Framework (AI 100-1) | Valid & Reliable |
| OECD AI Principles | Robustness, security & safety |
| Framework | Reference |
|---|---|
| MIT AI Risk Repository | Lack of capability or robustness |
| MIT AI Risk Repository | Misinformation |
| AIR 2024 | System & Operational Risks (L1) |
| IBM AI Risk Atlas | Output → Robustness + Inference → Accuracy |