Illustrative: layers of units and their weighted connections, an activation front sweeping from input to output

Risk Academy, led by the Nexus Consortium · Course catalogue · Governance, Policy and International Affairs

Science, Technology and Society

AI and digital governance

AI and digital governance, taught twice over: as the working methods of the field and as its frontier since 2024, with labs on the Academy's tools, open data and the Nexus Standards.

Core course Frontier course

The discipline

01Core course · L2 Intermediate

AI and digital governance: foundations and practice

This area covers how AI and digital systems are overseen and held accountable: risk-based AI regulation, algorithmic decisions in the public sector, data governance and sharing, digital rights and privacy, liability for autonomous agents, procurement and standards, and international cooperation.

Level
L2 Intermediate
Study time
about 24 hours (planning estimate)
Status
In authoring
Prerequisites
An L1 course in the area, or an entry review

Why now

Compliance programmes and conformity planning for high-risk AI must be re-sequenced around the new dates. European Commission, AI Act, 27 July 2026

Learning outcomes

  1. Define risk-based regulation and the other key terms of the discipline and use them correctly in a written case
  2. Explain AI oversight and accountability and algorithmic public decisions with a worked example and its source
  3. Apply the discipline's method to a documented case and explain each result with its source
  4. Analyse a named case against data governance and sharing, stating evidence, date and limits

Modules

  1. AI oversight and accountability
    • AI oversight and accountability
    • Algorithmic public decisions
    • Data governance and sharing
  2. Digital rights and privacy
    • Digital rights and privacy
    • Liability for autonomous agents
    • Procurement and standards
  3. International AI cooperation
    • International AI cooperation
    • Risk-based AI regulation
  4. Lab: the discipline's method
    • An automated system's error rate per demographic group, multiplied by the decisions it takes and the share of people in each group, gives wrongful decisions per group; the gap between groups is the disparity a risk-based regime asks a provider to measure before and after deployment.
    • Work the method through a documented case and explain each result
    • Read the method's sources and stated values

Labs with Academy tools

Nexus Standards

Teaches

  • NRS-P5 Digital and AI governance and regulation
  • NRS-T1 Artificial intelligence risk
  • NRS-H99 Hazard family: Algorithmic decision and structural harm
  • NRS-M12 Method fabric: Policy, regulation and governance

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C3 Hazard, exposure, vulnerability and capacity data
  • NRS-C4 Risk data model, identifiers and exchange
  • NRS-C7 Early warning, triggers and anticipatory action
  • NRS-C8 Evidence, uncertainty and provenance

Assessment

  • 40%Lab recordWork the discipline's method through a documented case and explain each result with its source and date
  • 40%Case memoApply the discipline's methods to a named place or organisation, stating evidence and limits
  • 20%Key terms checkShort answers on the key terms, marked against the definitions

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Intermediate, 4 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

References

  1. European Commission, AI Act, 27 July 2026
  2. European Parliament and Council, Regulation (EU) 2024/1689 (Artificial Intelligence Act), 2024
  3. Buolamwini and Gebru, Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification, 2018
  4. NIST, Artificial Intelligence Risk Management Framework (AI RMF 1.0), 2023
02Live map

Connected activity in China

Night-time lights and population density, the footprint of electrified, connected activity, over China.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 2

Connected activity

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 China.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

03How learning runs

How learning runs, as a process you can run

Select a scenario and press Play: course, pathway, practice, faculty, cells and record.

04What is changing

Frontier course · L3 advanced, programme courses and standards

AI and digital governance: frontier methods

The methods, data, tools and studies that changed AI and digital governance between 2024 and 2026, taught by reproducing them on open data.

Level
L3 Advanced
Study time
about 28 hours (planning estimate)
Status
Planned
Prerequisites
AI and digital governance: foundations and practice; Working knowledge of a scripting language (Python or R) for the labs

Why now

Compliance programmes and conformity planning for high-risk AI must be re-sequenced around the new dates. European Commission, AI Act, 27 July 2026

Learning outcomes

  1. Explain what changed in AI and digital governance since 2024 and cite the studies, data or rules behind each change
  2. Reproduce EU AI Omnibus delayed high-risk AI obligations on open data and state the uncertainty of the result
  3. Evaluate a 2024 to 2026 study: its question, data, method, limits and what it changes for practice
  4. Design an analysis that combines a frontier method with the faculty's instruments, and justify each choice

Modules

  1. EU AI Omnibus delayed high-risk AI obligations
    • The AI Omnibus entered into force on 27 July 2026, moving high-risk obligations for Annex III uses to 2 December 2027 and for AI in regulated products to 2 August 2028, and adding a prohibition on AI generating non-consensual sexual content.
    • Compliance programmes and conformity planning for high-risk AI must be re-sequenced around the new dates.
    • European Commission, 27 July 2026
  2. US AI Action Plan prioritises removing regulatory barriers and AI exports
    • The White House plan rests on three pillars, accelerating innovation, building AI infrastructure and leading in international diplomacy and security, with actions to remove regulatory obstacles, speed permits for data centres and promote exports of American AI packages.
    • Divergence between US and EU approaches creates compliance and governance complexity for organisations deploying AI across jurisdictions.
    • The White House, 23 July 2025
  3. EU published the General-Purpose AI Code of Practice
    • The code has three chapters, on transparency, copyright and safety and security, the last applying only to providers of models with systemic risk.
    • It is the practical template for how model providers document models and manage systemic risk under EU law.
    • European Commission, 10 July 2025
  4. Reproduction lab
    • Extend the discipline's method with one frontier method and compare its results
    • State the uncertainty and the limits of the result
    • Peer review against a shared checklist
  5. Technology that helps and the risks it carries
    • Foresight tools and regulatory sandboxes.
    • Threats to information integrity, and AI in public decisions.

Labs with Academy tools

Nexus Standards

Teaches

  • NRS-P5 Digital and AI governance and regulation
  • NRS-T1 Artificial intelligence risk
  • NRS-H99 Hazard family: Algorithmic decision and structural harm
  • NRS-M12 Method fabric: Policy, regulation and governance

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C3 Hazard, exposure, vulnerability and capacity data
  • NRS-C4 Risk data model, identifiers and exchange
  • NRS-C7 Early warning, triggers and anticipatory action
  • NRS-C8 Evidence, uncertainty and provenance

Assessment

  • 50%Method reproductionReproduce one frontier method on open data and state the uncertainty of the result
  • 30%Study reviewA critical review of a 2024 to 2026 study: question, data, method, limits, what it changes
  • 20%Peer reviewReview two other learners' reproductions against a shared checklist

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Expert, 5 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

References

  1. European Commission, AI Act, 27 July 2026
  2. The White House, White House Unveils America's AI Action Plan, 23 July 2025
  3. European Commission, The General-Purpose AI Code of Practice, 10 July 2025
  4. European Parliament and Council, Regulation (EU) 2024/1689 (Artificial Intelligence Act), 2024
  5. Buolamwini and Gebru, Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification, 2018
  6. NIST, Artificial Intelligence Risk Management Framework (AI RMF 1.0), 2023
05Take part

Take part in the work

Start from who you are.

06Questions

Questions, answers and key terms

Is the course 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 course?
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

Risk-based regulation

Regulation that sets obligations in proportion to the risk a system poses.

Term

Algorithmic impact assessment

An assessment of the effects of an automated decision system before and during use.

Term

Human oversight

The ability of people to monitor, understand and override an automated system.

Term

Data protection

Legal and technical safeguards for personal data.

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.

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