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Artificial intelligence and machine learning

Artificial intelligence and machine learning develop algorithms that learn patterns from data to classify, predict or generate outputs. They are increasingly used for hazard detection, forecasting and decision support, and are themselves a source of risk. Artificial Intelligence and Autonomous Systems stewards this page for the Nexus Consortium.

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01Contents

Artificial intelligence and machine learning in six parts.

In this dossier6 parts
  1. 01The methodWhat artificial intelligence and machine learning covers
  2. 02Reference practiceMachine learning in risk work is governed by AI risk frameworks and reporting standards
  3. 03In systemic risk workFast emulators, fragile extrapolation
  4. 04Applied inPlatforms that apply this method
  5. 05StewardshipWho maintains this page
  6. 06SourcesWhere this page’s references lead
02The method

What artificial intelligence and machine learning covers.

  1. 01

    Core methods

    supervised learning; deep neural networks; reinforcement learning; representation learning; probabilistic modelling; model interpretation.

  2. 02

    Data and instruments

    labelled training datasets; sensor and image archives; text corpora; simulation outputs; benchmark suites.

  3. 03

    Validation

    Models are evaluated on held-out data that respects spatial, temporal and group structure. Robustness, calibration and fairness are tested alongside accuracy.

  4. 04

    Limits

    Performance degrades when deployment conditions differ from training data. Models can encode biases and fail without clear warning on rare events.

  5. 05

    Use in risk decisions

    Machine learning can scale detection and forecasting across interconnected systems. Its own failure modes must be weighed when automated outputs inform consequential decisions.

03Reference practice

Machine learning in risk work is governed by AI risk frameworks and reporting standards.

ReferenceWhat it sets
NIST AI RMFRisk management functions for AI systems
ISO/IEC 42001AI management systems
TRIPOD (EQUATOR)Reporting of prediction models, including machine-learning extensions
04Process

How the process runs.

Process

Artificial intelligence and machine learning: AI risk management (NIST)

The AI RMF Core has four functions, GOVERN, MAP, MEASURE and MANAGE, with GOVERN cross-cutting; the order drawn is one common reading, not a checklist.

  1. 01 Govern

    1. 01GOVERN: policies, roles and accountabilityStep · Independent reviewerGOVERN is a cross-cutting function. It is infused throughout AI risk management and enables the other functions.

  2. 02 Map

    1. 02MAP: context and intended useDatasetThe MAP function establishes the context to frame risks related to an AI system, including intended purposes and impacts on people.

  3. 03 Measure

    1. 03MEASURE: evaluate trustworthy characteristicsModelAI systems are evaluated for trustworthy characteristics. They should be tested before deployment and regularly in operation.
    2. 04MEASURE: track identified risks over timeModelMechanisms for tracking identified AI risks over time are in place, including unanticipated and emergent risks.

  4. 04 Manage

    1. 05MANAGE: treat risksStepRisk treatment plans cover response to, recovery from and communication about incidents. Resources go to mapped and measured risks.
    2. 06Proceed or not (MANAGE 1.1)Gate · Independent reviewerA determination is made whether the system meets its purposes and objectives, and whether to proceed. The framework sets no thresholds.
    3. 07Deployment decisionDecision · Independent reviewerThe organisation that owns the system decides whether and under what conditions to deploy.
    4. 08Post-deployment monitoring and incidentsModelPost-deployment monitoring plans are implemented. Incidents and errors are shared with relevant AI actors, including affected communities.

  5. 05 Record

    1. 09Inventory of AI systemsDatasetGOVERN 1.6 asks for mechanisms to inventory AI systems. A model card is common practice and is not named in the framework.

Neutral outline of a published external standard or method, from its issuing body. Not a record of operation. Nexus does not perform, certify or approve any step shown. A gate or decision belongs to the party the source names. The actions are not necessarily an ordered set of steps. The EU AI Act is not drawn in this flow.

05In systemic risk work

Fast emulators, fragile extrapolation.

Machine learning speeds up risk analysis: emulators of expensive physical models, classification of imagery, forecasting from large datasets. Its weakness is extrapolation: a model trained on the historical climate or historical markets may fail exactly in the unprecedented conditions that systemic risk is about.

  • Test performance out of distribution, not only on held-out samples from the same period.
  • Report training data provenance and licence.
  • Keep a physical or mechanistic check where one exists.
  • Applications on AI and Autonomous Systems and Labs.
06Applied in

Platforms that apply this method.

07Stewardship

Who maintains this page.

Artificial Intelligence and Autonomous Systems stewards artificial intelligence and machine learning: it keeps this description current and reviews how the method is applied across the platforms.

08Sources

Where this page’s references lead.

The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.

09Questions

Questions and answers.

Why does one institution host every method page?
GCRI hosts the STEM Commons so that each method has one canonical description. Platforms link to it rather than repeat it, which keeps definitions consistent.
How do I suggest a correction to a method page?
Write to the integrity concern form with the passage and the reason.
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