nexustandards.com/digital-ai · 7 taught
Digital, Cyber and AI
NRS-H95 AI system failure and misalignment; NRS-H96 AI security and model supply chain
7standards1discipline
Illustrative: layers of units and their weighted connections, an activation front sweeping from input to output
Risk Academy, led by the Nexus Consortium · Technology and Digital Systems · Department of Artificial Intelligence and Autonomous Systems
Evaluation design, red-teaming, benchmarks and safety cases
AI safety and evaluation
AI safety and evaluation covers how the capabilities, reliability and risks of AI systems are tested before and after deployment.
- Courses
- In preparation
- Levels
- In development
- Labs
- In development
- Evaluation design and metrics
- Red-teaming and adversarial testing
- Benchmarks and data contamination
- Dangerous capability evaluations
- Safety cases for frontier models
- Agentic system evaluation
- Monitoring after deployment
- Incident reporting and disclosure
Courses in development extend the scope to further subjects: AI system evaluation methods; Frontier AI risk and safety cases.
02Maps
Build a twin of any place
Search any place or open the world; the layers and generators follow aI safety and evaluation on the planet map, the globe and in 3D.
- 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
- Night-time lights, VIIRS day-night band: read live when the 3D twin opens
Drag to move, right drag or Shift with a drag to turn and tilt, Ctrl with the wheel to zoom. Keyboard: arrows move, Shift with arrows turn and tilt, + and – zoom, 0 resets, V steps through the views, Escape clears.
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).
03Live map
Connected activity in the United Kingdom and Ireland
Night-time lights and population density, the footprint of electrified, connected activity, over the United Kingdom and Ireland.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 2
Connected activity
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Connected activity, framed on the United Kingdom and Ireland.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONETSources and licences
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- Optional layers (3 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
04The subject
The subject, methods and measures and debates and bodies
The field and its scope
For evaluators, red teams, safety researchers and the public officers who read their results, who need to design tests of AI systems and know what a result can and cannot support.
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 learners can find and apply their published work; the Academy teaches their use and links to the publisher, and no affiliation is implied.
05The method
From one attempt to an attacker's budget: how a per-attempt success rate becomes a release gate
An attack success rate is measured per attempt against a stated attacker budget; the chance that at least one of k attempts succeeds grows as 1 minus (1 minus s) to the k, and a release gate compares that cumulative figure, after defences, with the level a safety case can tolerate.
Sources: Shevlane et al., Model evaluation for extreme risks (2023); Phuong et al., Evaluating Frontier Models for Dangerous Capabilities (2024); Clymer et al., Safety Cases: How to Justify the Safety of Advanced AI Systems (2024); Bengio et al., International AI Safety Report (2025).
06At a glance
At a glance, standards and evidence, research and evidence, what is changing and learning
AI safety and evaluation at a glance
risksacademy.comtechnology-digitaldiscipline
- Open, 0
- In preparation, 2
- Subtopics, 8
Nexus Standards it teaches and relies on
The Nexus Standards the discipline teaches toward, grouped by master standard, with the disciplines that teach each.
nexustandards.com/open-infrastructure · 1 taught
Open Infrastructure and Developers
NRS-A9 Evaluation suites for AI in risk work
1standard1discipline
Nexus Standards at the AcademyThe Academy proposes how each discipline teaches toward the Nexus Standards; the mapping is open for public review.
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 safety and evaluation, each with its source and date. Open the research hub
What is changing now
Recent developments in aI safety and evaluation, 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 it through the connected courses and open lessons below.
The course catalogue lists every open course of the Academy.
07Console
How AI safety and evaluation is taught, as a process you can run
Press Play to run this process from this page's own data.
08Learning
Achievements, where this is practised, practised in and across the ecosystem
Achievements in this discipline
Course achievements
One for each course with this as its primary discipline, earned when you pass the course's assessed activities.
An achievement is a record of the learning completed and assessed here. How recognition works.
Where the subject is practised
- The Global Centre for Risk and Innovation (GCRI Canada)Artificial Intelligence and Autonomous SystemsModel evaluation
Field platforms of the ecosystem where the subject is studied and applied.
The three Foundry systems where it is practised
- FoundryNXS-NXQ systemFitness of a machine capability for a named taskCompetence room
- NXS-LAB systemEvaluation under transparent test conditionsBenchmarks room
- NXS-NSF systemAssurance cases and reliance limitsAssurance room
Foundry system pages describe what each system and its rooms are designed to do; the courses teach the methods these systems apply.
Across the ecosystem
09Take part
Take part in Technology and Digital Systems
Start from who you are.
10References
Core references and where to begin
Start with Bengio, then Shevlane, then Weidinger.
11Questions
Questions, answers and key terms
Is study here free?
Yes. Every course and learning material of Risk Academy, led by the Nexus Consortium, is free to study, self-paced or with a cohort. Ordinary participation has no fee; paid work, internships and fellowships are separate arrangements.
What does completing a course give me?
A completion record: the course, its version, the date and what was assessed, private by default and shared by you. It stacks into discipline, department and programme achievements. A record states what was studied and assessed and does not certify competence; it is not a licence, a professional title or an employment status.
Where do the data and the places come from?
Every faculty teaches on a twin of a real place built from open data; each layer carries its provider, licence and date, and every result on the Academy states its source. Maps and figures are indicators for teaching, never an assessment of a place.
How do I take part beyond the courses in aI safety and evaluation?
Form a competence cell of three to seven people, study a shared pathway, take a supervised assignment in the National Working Groups, or bring a cohort from your institution. For organisations.
Key terms
Term
Red-teaming
Structured attempts to make a system fail or misbehave, to find weaknesses before others do.
Term
Benchmark contamination
When test data have leaked into a model's training data, so scores overstate its ability.
Term
Safety case
A structured argument, supported by evidence, that a system is acceptably safe for a given use.
Term
Frontier model
A general-purpose AI model at or near the most capable level available at a given time.
ReportsContribute
Contribute evidence from this page.
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.
Risk Academy
Related
Around AI safety and evaluation
AI safety and evaluation
Prepare with a related course
Open courses in connected disciplines teach the methods this discipline will build on.