Illustrative: layers of units and their weighted connections, an activation front sweeping from input to output
Risk Academy, led by the Nexus Consortium · Risk Science and Methods · Department of Quantitative Risk Analysis
Learning from data without hiding the error
AI and machine learning for risk analysis
Machine learning entered risk analysis through its methods, not its headlines: emulators that stand in for slow hazard and catastrophe models, classifiers of damage from imagery, anomaly detectors on sensor streams and foundation models trained on satellite archives.
- Department
- Department of Quantitative Risk Analysis
- Courses
- In preparation
- Levels
- In development
- Labs
- In development
- Supervised learning for risk outcomes
- Emulators and surrogates of physical and catastrophe models
- Uncertainty quantification in machine learning
- Conformal prediction and distribution-free coverage
- Anomaly detection and change detection
- Geospatial foundation models and satellite embeddings
- Causal inference and machine learning
- Validation, drift monitoring and model risk for machine learning
- Fairness and bias in risk models
Build a twin of any place
Search any place or open the world; the layers and generators follow aI and machine learning for risk analysis on the planet map, the globe and in 3D.
- Terrain 24 to 2,805 m
- 3 reference earthquakes, M6.2 to M6.6
- Earthquakes M4.5 and above, past week: read live when the 3D twin opens
- USGS ShakeMap intensity contours: 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, the three reference epicentres with magnitude and depth, and the numbers below; the sampler's figure shows one precomputed field with its parameters. 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).
Connected activity in South Africa
Night-time lights and population density, the footprint of electrified, connected activity, over South Africa.
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 South Africa.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
The subject, methods and measures and debates and bodies
The field and its scope
For risk analysts and data scientists who use or review machine learning in a risk estimate and need to know when its outputs can be trusted and how its errors propagate.
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.
From a trained model to an interval with assured coverage: residuals, the conformal quantile and the coverage a user can count on
A learned model gives a point prediction with no promise attached; holding out a calibration set, ranking its residuals and taking a quantile wraps the prediction in an interval whose coverage is assured without any assumption about the model, and the size of the calibration set sets how tight that guarantee is.
Sources: Vovk, Gammerman and Shafer, Algorithmic Learning in a Random World (2005); Angelopoulos and Bates, Conformal prediction: a gentle introduction, Foundations and Trends in Machine Learning (2023); Guo, Pleiss, Sun and Weinberger, On calibration of modern neural networks, ICML (2017); Gama et al., A survey on concept drift adaptation, ACM Computing Surveys (2014); Rudin, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nature Machine Intelligence (2019).
At a glance, hub and courses
AI and machine learning for risk analysis at a glance
risksacademy.comrisk-sciencediscipline
- Open, 0
- In preparation, 0
- Subtopics, 9
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 and machine learning for risk analysis, each with its source and date. Open the research hub
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.
How AI and machine learning for risk analysis is taught, as a process you can run
Press Play to run this process from this page's own data.
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.
Department: Quantitative Risk Analysis
Earn two discipline achievements in the Department of Quantitative Risk Analysis. In development.
An achievement is a record of the learning completed and assessed here. How recognition works.
Where the subject is practised
A field platform page on this subject is in development.
The two Foundry systems where it is practised
- FoundryNXS-LAB systemMachine-learning methods are compared under transparent testsExperiments room, Benchmarks room
- NXS-NXQ systemModels have a lifecycle of fitness, validation and driftCapabilities 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
Take part in Risk Science and Methods
Start from who you are.
Core references and where to begin
Start with Hastie, then Guo, then Angelopoulos.
Questions, answers and key terms
Is study here free?
What does completing a course give me?
Where do the data and the places come from?
How do I take part beyond the courses in aI and machine learning for risk analysis?
Key terms
Emulator
A fast statistical or machine-learning model trained to reproduce the outputs of a slower physical or catastrophe model.
Conformal prediction
A method that wraps any model to give prediction sets with a stated coverage under exchangeability.
Distribution shift
A change between the data a model was trained on and the data it is used on, which can invalidate its accuracy.
Calibrated classifier
A classifier whose predicted probabilities match observed frequencies.
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.
Around AI and machine learning for risk analysis
Prepare with a related course
Open courses in connected disciplines teach the methods this discipline will build on.