AI assurance glossary

About 40 terms, each taken from the glossary article. The definition is what that article attributes to the cited official page. We did not add unsourced terms.

AI safety assurance: The FAA roadmap's primary purpose is a path to assuring the safety of AI in aircraft and related systems for inflight operations. The same document says AI must demonstrate its safety before use in aviation.; Source: Roadmap for Artificial Intelligence Safety Assurance, Version I (FAA). See the article write-up.
Learned versus learning AI: The FAA distinguishes static (learned) from dynamic (learning) implementations. Learned AI is frozen after training; learning AI continues to change in operation and is treated as an ongoing research topic.; Source: Roadmap for Artificial Intelligence Safety Assurance, Version I (FAA). See the article write-up.
Learning assurance: EASA proposes a new concept of learning assurance to extend the traditional development assurance, aimed at confidence that trained models generalize on unseen operational data and are robust in foreseeable conditions.; Source: EASA Concept Paper Issue 02 (PDF) (EASA). See the article write-up.
W-shaped process: EASA illustrates learning assurance as a W-shaped cycle that adapts the typical development assurance V-cycle to ML concepts, running with the traditional V-cycle for non-ML constituents.; Source: EASA Concept Paper Issue 02 (PDF) (EASA). See the article write-up.
Operational design domain (ODD): EASA ties data quality to the ODD: a correct and complete definition of the ODD is a prerequisite to an adequate level of quality of the data sets.; Source: EASA Concept Paper Issue 02 (PDF) (EASA). See the article write-up.
Human-AI teaming (HAT): EASA's Issue 2 introduces the concept of human-AI teaming (HAT) for Level 2 systems that automatically take decisions under human oversight.; Source: EASA publishes Artificial Intelligence Concept Paper Issue 2 (EASA). See the article write-up.
DO-178C: RTCA describes DO-178() as the software standards documents used with EUROCAE ED-12 for airborne software development assurance.; Source: DO-178() Software Standards Documents & Training (RTCA). See the article write-up.
Development assurance: EASA treats learning assurance as an extension of traditional development assurance, not a replacement of it.; Source: EASA Concept Paper Issue 02 (PDF) (EASA). See the article write-up.
NIST AI RMF functions: The Core is composed of four functions: Govern, Map, Measure and Manage. NIST says the actions do not constitute a checklist.; Source: AI RMF Core (excerpt from AI RMF 1.0) (NIST AI Resource Center). See the article write-up.
TEVV: NIST AI RMF 1.0 discusses test, evaluation, verification, and validation (TEVV) as part of measuring AI risk. The framework does not turn those words into an aviation qualification method.; Source: Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1 (NIST). See the article write-up.
AI management system: ISO/IEC 42001 specifies requirements for an Artificial Intelligence Management System. ISO describes it as managing AI-related risks across an organization rather than looking at details of specific AI applications.; Source: ISO/IEC 42001:2023 - AI management systems (ISO). See the article write-up.
DoD AI ethical principles: The Department adopted five ethical principles for the use of AI in February 2020: Responsible, Equitable, Traceable, Reliable and Governable.; Source: DOD Adopts Ethical Principles for Artificial Intelligence (Department of War). See the article write-up.
High-risk AI system: In the EU AI Act, high-risk systems are those that meet the conditions in Article 6 and the Annex III use-case list, subject to the military, national-security and other scope rules in Article 2.; Source: Regulation (EU) 2024/1689 - consolidated text (27.07.2026) (EUR-Lex). See the article write-up.
Controlled Unclassified Information (CUI): NIST SP 800-171 Rev. 3 is the set of security requirements for protecting the confidentiality of CUI when that information is processed, stored or transmitted by nonfederal organizations.; Source: NIST SP 800-171 Rev. 3 (PDF) (NIST). See the article write-up.
CMMC: The CMMC Program in 32 CFR Part 170 is the Department of Defense assessment mechanism for contractor implementation of those CUI protections. It is not an AI product-assurance standard.; Source: 32 CFR Part 170 - CMMC Program (eCFR). See the article write-up.
Super Intelligence (SI): Executive Order 14434 (signed 29 September 2026; Federal Register 2 October 2026, document 2026-20321) is the policy that, to the maximum extent permitted by law, the executive branch shall use Super Intelligence and SI — formerly called Artificial Intelligence and AI in those settings — in official correspondence, public communications, websites, reports, policy documents, and other non-statutory documents. The official published title is Inaugurating the Era of Super Intelligence. For purposes of the order, SI means the technologies and systems encompassed by artificial intelligence as defined in 15 U.S.C. 9401(3). The Order does not require alteration of previously issued regulations, Presidential actions, contracts, grants, or other historical documents.; Source: Inaugurating the Era of Super Intelligence (Federal Register). See the article write-up.
Responsible (DoD AI principle): Department personnel will exercise appropriate levels of judgment and care while remaining responsible for the development, deployment, and use of AI capabilities.; Source: DOD Adopts Ethical Principles for Artificial Intelligence (Department of War). See the article write-up.
Equitable (DoD AI principle): The Department will take deliberate steps to minimize unintended bias.; Source: DOD Adopts Ethical Principles for Artificial Intelligence (Department of War). See the article write-up.
Traceable (DoD AI principle): Capabilities are developed and deployed so that personnel understand the technology, including transparent and auditable methodologies, data sources, and design procedures and documentation.; Source: DOD Adopts Ethical Principles for Artificial Intelligence (Department of War). See the article write-up.
Reliable (DoD AI principle): Capabilities have explicit, well-defined uses, with safety, security and effectiveness subject to testing and assurance within those uses across their entire life-cycles.; Source: DOD Adopts Ethical Principles for Artificial Intelligence (Department of War). See the article write-up.
Governable (DoD AI principle): Capabilities have the ability to detect and avoid unintended consequences, and to disengage or deactivate deployed systems that demonstrate unintended behavior.; Source: DOD Adopts Ethical Principles for Artificial Intelligence (Department of War). See the article write-up.
RAI Strategy and Implementation Pathway: CDAO’s toolkit release says the June 2022 pathway spells out 64 lines of effort for operationalizing the Department of Defense’s AI Ethical Principles, including developing technical tools and guidance.; Source: CDAO Releases Responsible AI (RAI) Toolkit (Department of War). See the article write-up.
AIA Toolkit: The live Tradewinds page titles the public CDAO self-evaluation resource the AI Assurance (AIA) Toolkit. It is a self-evaluation resource that helps teams assess their AI projects at any stage of development.; Source: AIA Toolkit | Tradewinds (CDAO / Tradewinds). See the article write-up.
Model card: The December 2024 CDAO Generative AI Responsible AI Toolkit lists a Hugging Face model-card template as a documentation tool for traceability (transparent and auditable documentation).; Source: CDAO Generative AI Responsible AI Toolkit (December 2024) (CDAO). See the article write-up.
Govern (NIST AI RMF): One of the four AI RMF Core functions: Govern, Map, Measure and Manage. NIST says the actions do not constitute a checklist.; Source: AI RMF Core (excerpt from AI RMF 1.0) (NIST AI Resource Center). See the article write-up.
Map (NIST AI RMF): One of the four AI RMF Core functions: Govern, Map, Measure and Manage. NIST says the actions do not constitute a checklist.; Source: AI RMF Core (excerpt from AI RMF 1.0) (NIST AI Resource Center). See the article write-up.
Measure (NIST AI RMF): One of the four AI RMF Core functions: Govern, Map, Measure and Manage. NIST says the actions do not constitute a checklist.; Source: AI RMF Core (excerpt from AI RMF 1.0) (NIST AI Resource Center). See the article write-up.
Manage (NIST AI RMF): One of the four AI RMF Core functions: Govern, Map, Measure and Manage. NIST says the actions do not constitute a checklist.; Source: AI RMF Core (excerpt from AI RMF 1.0) (NIST AI Resource Center). See the article write-up.
SSDF Community Profile (SP 800-218A): NIST augments the secure software development practices and tasks defined in SSDF version 1.1 with practices specific to AI model development. The additions are documented in the form of an SSDF Community Profile.; Source: SP 800-218A, Secure Software Development Practices for Generative AI and Dual-Use Foundation Models (NIST CSRC). See the article write-up.
Dual-use foundation model: SP 800-218A is a companion resource to the SSDF to incorporate secure development practices for generative AI and for dual-use foundation models.; Source: SP 800-218A, Secure Software Development Practices for Generative AI and Dual-Use Foundation Models (NIST CSRC). See the article write-up.
Adversarial machine learning (AML): NIST AI 100-2e2025 provides a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML).; Source: AI 100-2 E2025, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST CSRC). See the article write-up.
FDAL and IDAL: FAA AC 20-174 says applicants should propose and document in the certification plan the functional development assurance level (FDAL), item development assurance level (IDAL) and justification for the levels.; Source: AC 20-174 - Development of Civil Aircraft and Systems (FAA). See the article write-up.
IDAL A/B ML ceiling (EASA Issue 2): EASA will initially accept only applications where AI/ML constituents do not include IDAL A or B items for supervised learning.; Source: EASA Concept Paper Issue 02 (PDF) (EASA). See the article write-up.
Level 1 and Level 2 (EASA): Issue 2 is guidance for Level 1 and Level 2 machine-learning applications. Level 2 is the human-AI teaming case on the EASA news item.; Source: EASA publishes Artificial Intelligence Concept Paper Issue 2 (EASA). See the article write-up.
Aeronautical data: AC 20-153B defines aeronautical data as data used for aeronautical applications such as navigation, flight planning, flight simulators, terrain awareness, and other purposes.; Source: AC 20-153B - Acceptance of Aeronautical Data Processes and Associated Databases (FAA). See the article write-up.
Letter of Acceptance (LOA): An LOA confirms compliance with RTCA/DO-200B.; Source: AC 20-153B - Acceptance of Aeronautical Data Processes and Associated Databases (FAA). See the article write-up.
Runtime assurance (RTA): NASA/TM–20220015734 defines RTA as the combination of RTM and one or more functions triggered by RTM, such as recovery, failover, warning alarm, or shutdown.; Source: NASA/TM–20220015734 PDF (NASA). See the article write-up.
Annex III (EU AI Act): In the EU AI Act, high-risk systems include those that meet Article 6 and the Annex III use-case list, subject to the military, national-security and other scope rules in Article 2.; Source: Regulation (EU) 2024/1689 - consolidated text (27.07.2026) (EUR-Lex). See the article write-up.
DO-330: The FAA AI roadmap says the FAA is supporting application and possible extension of existing standards including DO-178C, DO-330 and related documents. DO-330 is the tool-qualification consideration document used with airborne software.; Source: Roadmap for Artificial Intelligence Safety Assurance, Version I (FAA). See the article write-up.
Article 2 scope (EU AI Act): High-risk classification under Article 6 and Annex III is subject to the military, national-security and other scope rules in Article 2 of Regulation (EU) 2024/1689.; Source: Regulation (EU) 2024/1689 - consolidated text (27.07.2026) (EUR-Lex). See the article write-up.

Questions

Are these definitions written by this directory?

No. Each definition is what the glossary article attributes to the cited official page. We did not add unsourced terms.

Why do some U.S. federal terms say Super Intelligence (SI)?

Site branding and own-voice copy may say Super Intelligence (SI). Official titles of cited documents stay exactly as published — NASA SWEHB topics 7.25 and 8.25 are Artificial Intelligence And Software Engineering / Assurance. Executive Order 14434 directs executive-branch websites, to the maximum extent permitted by law, to use SI in new non-statutory documents. Official names of statutes and non-federal standards stay as published.