Artificial Intelligence and Autonomous Systems
The Global Centre for Risk and Innovation (GCRI)
Model descriptions for Artificial Intelligence and Autonomous Systems are published here once reviewed, with their assumptions, evidence and validation. They describe models; they are not live simulations.
Reviewed models and scenarios · Workspace WS-43
| Model registry | v3.0 | To check | OFFICIAL |
| Evaluation results | v5.2 | Open | ASSESSED |
| Incident log | v1.3 | Open | REPORTED |
| Usage telemetry | v5.3 | To check | OBSERVED |
share used vs alert level
Artificial Intelligence and Autonomous Systems · PipelinePipeline, in outline: domain structure with placeholder names and illustrative values, not a record.
Artificial Intelligence and Autonomous Systems reviewed models are published descriptions of the models used in the artificial intelligence field, with their assumptions, data, validation and limits, built on methods such as artificial intelligence and machine learning as well as AI evaluation and assurance engineering. They describe models; they are not live simulations, and no output on them is a finding.
Fig.A programme from scoping to the owner's decision. Design, not a record.
For reviewed models and scenarios, in GCRI's spatial-intelligence reading, the map opens on night lights, true-colour imagery and population density with true colour from NASA GIBS, framed on East Asia; 3 further NASA layers wait in the list.
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NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
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For reviewed models and scenarios, in GCRI's spatial-intelligence reading, the map opens on night lights, true-colour imagery and population density with true colour from NASA GIBS, framed on East Asia; 3 further NASA layers wait in the list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
Layers and dates
| Model class | Open examples | Question it answers | Validation |
|---|---|---|---|
| Capability evaluations | Open evaluation suites such as HELM | What a model can do | Held-out tests, reproducibility |
| Red-team evaluations | Structured adversarial testing | How a model can be misused or fail | Coverage and independent repetition |
| Socio-technical impact models | Labour and economic models | Effects on work and services | Observed adoption data |
| Incident analysis | OECD AI incidents monitor | Patterns of real failures | Incident verification |
Analysis runs in Nexus Foundry; reviewed releases are listed here.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Model validation | Comparing model predictions with independent observations to judge fitness for a stated use. |
| Model card | A short document describing a model's intended use, training data, performance and limits. |
| Uncertainty quantification | Characterising and propagating the uncertainties in a model to state confidence in its outputs. |
| Surrogate model | A fast statistical model trained to approximate a slow simulation or experiment. |
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.