Reliability
Güvenilirlik
D4
For an AI system, reliability is the goal of overall correct operation under expected-use conditions over a stated period.
Review status: 2027-08-28
Technical explanation
It is assessed through evidence of intended performance under monitored conditions over time.
Conceptual boundaries
Reliability concerns overall correct operation under expected-use conditions over time; it is not interchangeable with validity, accuracy, robustness, or safety.
Provider-neutral example
A document-routing system can be monitored over its stated operating period to see whether it continues to route required cases correctly and reports failures for review.
Limitations
Ongoing testing and monitoring provide evidence under monitored conditions; they do not guarantee the absence of future failure.
Related concepts
Atomic claims and evidence
1.1NIST AI 100-1, Artificial Intelligence Risk Management Framework (AI RMF 1.0) — D4 evidence slice
- Source
- NIST AI 100-1, Artificial Intelligence Risk Management Framework (AI RMF 1.0) — D4 evidence slice
- Source role
- Authoritative source
- Exact locator
- Section 3.1, printed p. 13
- Supported claim
- NIST describes reliability for AI systems as a goal of overall correct operation under expected-use conditions over a given period, including the system's lifetime.
- Last verification
- Review due
- Scope limitation
- This is NIST's AI-system description and does not reduce reliability to a single model score.
2.1NIST AI 100-1, Artificial Intelligence Risk Management Framework (AI RMF 1.0) — D4 evidence slice
- Source
- NIST AI 100-1, Artificial Intelligence Risk Management Framework (AI RMF 1.0) — D4 evidence slice
- Source role
- Authoritative source
- Exact locator
- Section 3.1, printed p. 14
- Supported claim
- NIST states that validity and reliability for deployed AI systems are often assessed through ongoing testing or monitoring that confirms intended performance.
- Last verification
- Review due
- Scope limitation
- Ongoing testing can supply evidence under the monitored conditions; it does not guarantee absence of future failure.
3.1NIST AI 100-1, Artificial Intelligence Risk Management Framework (AI RMF 1.0) — D4 evidence slice
- Source
- NIST AI 100-1, Artificial Intelligence Risk Management Framework (AI RMF 1.0) — D4 evidence slice
- Source role
- Authoritative source
- Exact locator
- Section 3.1, printed p. 14
- Supported claim
- NIST states that accuracy and robustness contribute to validity and trustworthiness, while validity and reliability for deployed systems are assessed through ongoing testing or monitoring.
- Last verification
- Review due
- Scope limitation
- These relationships do not make accuracy, robustness, validity, and reliability interchangeable.
4.1NIST AI 100-1, Artificial Intelligence Risk Management Framework (AI RMF 1.0) — D4 evidence slice
- Source
- NIST AI 100-1, Artificial Intelligence Risk Management Framework (AI RMF 1.0) — D4 evidence slice
- Source role
- Authoritative source
- Exact locator
- Sections 3.1-3.2, printed pp. 13-14
- Supported claim
- NIST lists Valid & Reliable and Safe as distinct trustworthiness characteristics and states that accuracy and robustness contribute to validity and trustworthiness.
- Last verification
- Review due
- Scope limitation
- These characteristics interact but are not interchangeable.