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Risk Academy, led by the Nexus Consortium · Technology and Digital Systems · Department of Artificial Intelligence and Autonomous Systems
Impact assessment, audit and assurance cases for AI systems
AI assurance and audit
Evaluating a model asks what it can do; assuring a system asks whether it is acceptable for a use, and who can show it.
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- AI system impact assessment (ISO/IEC 42005)
- AI risk management (ISO/IEC 23894 and NIST AI RMF)
- Auditing AI management systems (ISO/IEC 42006)
- Conformity assessment under the EU AI Act
- Assurance cases and evidence
- Documentation: model cards, system cards and technical files
- Agentic AI assurance and oversight
- Red teaming and third-party evaluation
- Post-market monitoring and incident reporting
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- Terrain 0 to 245 m
- 4 satellite dishes
- 74 communication masts and towers
- 12 telephone exchanges
- 7 data centres and cable landing stations
- 12 aerodromes
- 1 telecom offices
- Communication line 161 km
- 729 known exploited vulnerabilities since 2023-10-17
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Flat plan of the same data: relief as contours from the inline grid, the mapped ground infrastructure by kind, the mapped cables and the numbers below. The 3D twin opens on devices that can draw it, once its data and code have been checked.
The boundaries, names and designations shown do not imply any judgement on the legal status of any territory, or any endorsement or acceptance of such boundaries. Countries: Natural Earth (public domain).
The subject, methods and measures and debates and bodies
The field and its scope
For auditors, compliance leads, risk officers and conformity assessment bodies who must turn evidence about an AI system into a justified, documented and reviewable judgement.
Quantities and units
Methods and their limits
Each method is listed with what it assumes and where it fails.
What the field debates
Bodies that set practice, and the data they publish
Bodies and standards are named so that learners can find and apply their published work. The Academy teaches the use of that work and links to the publisher. No affiliation is implied.
From a control list to an audit opinion: what a sample of records can establish
Control coverage is two figures, designed and operating; operating effectiveness is judged from a sample of records, and the sample size bounds the exception rate an auditor can rule out, so the opinion rests on how many records were pulled as much as on what they showed.
Sources: Raji et al., Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing (2020); Mökander, Schuett, Kirk and Floridi, Auditing large language models: a three-layered approach (2023); Schuett, Three lines of defence against risks from AI (2023); European Parliament and Council, Regulation (EU) 2024/1689 on artificial intelligence (2024).
At a glance, hub, what is changing now and courses
AI assurance and audit at a glance
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Research and tools
Research and preprints as they are published, by discipline, with the faculty's other tools.
The hub lists the newest research and preprints for AI assurance and audit, each with its source and date. Open the research hub
What is changing now
Recent developments in aI assurance and audit, each with its source; sources checked on 16 October 2026.
Courses in this discipline
Courses for this discipline are in development. Until they open, study the discipline through the connected courses and open lessons below.
The course catalogue lists every open course of the Academy.
How AI assurance and audit is taught, as a process you can run
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How AI assurance and audit is taught
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Achievements, where this is practised, practised in and across the ecosystem
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Where the subject is practised
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The three Foundry systems where it is practised
Across the ecosystem
Take part in Technology and Digital Systems
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Core references and where to begin
Start with Raji, then National Institute of Standards, then Raji.
Questions, answers and key terms
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Key terms
AI assurance
The processes that build and communicate justified confidence that an AI system meets its requirements.
Impact assessment
A structured analysis of how an AI system may affect individuals, groups and society, as in ISO/IEC 42005:2025.
Conformity assessment
The process of demonstrating that a high-risk AI system meets the requirements of the EU AI Act before it is placed on the market.
Assurance case
A structured argument, supported by evidence, that a system is acceptable for a use.
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Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
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