Skills extraction from job postings
Natural language processing extracts skills from job ads and maps them to taxonomies such as ESCO, tracking how demand for skills changes over time.
GCRI · Future of Work and Skills
AI for labour market analysis
Artificial intelligence and machine learning are both a driver of change in work and a tool for studying it. In this field they help analyse job postings and skills, match jobseekers to vacancies, and raise questions about algorithmic hiring and management.
01Register
AI for labour market analysis on the record.
Method recordSTEM method
- MethodArtificial intelligence and machine learning
- Applied inFuture of Work and Skills
- Data3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What artificial intelligence and machine learning does here.
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. In work and skills, society and human development and artificial intelligence, machine learning can scale detection and forecasting across interconnected systems. Its own failure modes must be weighed when automated outputs inform consequential decisions. The canonical description is on the STEM Commons.
Job matching in employment services
Matching tools suggest vacancies to jobseekers, with evaluation of accuracy and fairness across groups before deployment.
Auditing algorithmic hiring
Hiring and screening algorithms are audited for disparate impact on protected groups using test data and outcome monitoring.
03Methods and evidence
How AI for labour market analysis is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in AI for labour market analysis: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
04Data
What the method consumes.
Datasets and records used by artificial intelligence and machine learning on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Online job postings | Skills demand | OBSERVED |
| ESCO and O*NET taxonomies | Skills mapping | Published statistics |
| Employment service records | Matching outcomes | Published statistics |
05Process
How the process runs.
Process
AI for labour market analysis: 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.
01 Govern
- 01GOVERN: policies, roles and accountabilityStep · Trade unionGOVERN is a cross-cutting function. It is infused throughout AI risk management and enables the other functions.
02 Map
- 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.
03 Measure
- 03MEASURE: evaluate trustworthy characteristicsModelAI systems are evaluated for trustworthy characteristics. They should be tested before deployment and regularly in operation.
- 04MEASURE: track identified risks over timeModelMechanisms for tracking identified AI risks over time are in place, including unanticipated and emergent risks.
04 Manage
- 05MANAGE: treat risksStepRisk treatment plans cover response to, recovery from and communication about incidents. Resources go to mapped and measured risks.
- 06Proceed or not (MANAGE 1.1)Gate · Trade unionA determination is made whether the system meets its purposes and objectives, and whether to proceed. The framework sets no thresholds.
- 07Deployment decisionDecision · Trade unionThe organisation that owns the system decides whether and under what conditions to deploy.
- 08Post-deployment monitoring and incidentsModelPost-deployment monitoring plans are implemented. Incidents and errors are shared with relevant AI actors, including affected communities.
05 Record
- 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.
06Validation
How results are checked.
Models are evaluated on held-out data that respects spatial, temporal and group structure. Robustness, calibration and fairness are tested alongside accuracy.
| Check | How it is done |
|---|---|
| Extraction accuracy | Skill extraction is checked against human-coded samples |
| Fairness testing | Tools are tested for disparate impact across groups |
| Representativeness | Posting data are compared with published vacancy statistics |
Performance degrades when deployment conditions differ from training data. Models can encode biases and fail without clear warning on rare events.
07Connections
Topics that rely on this method.
08Stewardship
Who maintains the method.
Artificial Intelligence and Autonomous Systems stewards artificial intelligence and machine learning for the Consortium and reviews how it is applied across platforms.
09Connected
Where this page leads across the Consortium.
10Questions
Questions and answers.
What are the limits of artificial intelligence and machine learning in this field?
Performance degrades when deployment conditions differ from training data. Models can encode biases and fail without clear warning on rare events.
How are results checked?
Models are evaluated on held-out data that respects spatial, temporal and group structure. Robustness, calibration and fairness are tested alongside accuracy.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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GCRIFuture of Work and SkillsForesight OutlookEvidence Brief
- EmailGateway GW-GCRISend it from this pageEvidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.
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