Coding policy and legal texts
Classifiers and language models tag national AI strategies, regulations and standards by topic, obligation and actor, with a sample double-coded by people and disagreement reported beside the result.
GRF · Innovation Nexus
AI as subject and tool in innovation governance
Artificial intelligence is both a subject of this platform's governance work and a tool within it. As a subject, its capabilities, failure modes and deployment conditions define what regulators and standards bodies must address. As a tool, classifiers and language models help code large bodies of policy text and patents, under human review.
01Register
Key facts.
Method recordSTEM method
- MethodArtificial intelligence and machine learning
- Applied inInnovation Nexus
- Data5 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 innovation, artificial intelligence and industry and robotics, 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.
Mapping patents and papers to technologies
Text models link patent families and publications to emerging technology domains, so diffusion and convergence can be traced where classification codes lag behind the field.
Comparing evaluation practice
Benchmarks, red-teaming protocols and model documentation are compared with the requirements of ISO/IEC 42001, the NIST AI RMF and the EU AI Act, showing where practice and obligations align and where they diverge.
03Monitor
Model training.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Model training
Innovation · AI as subject and tool · Run R-6239
Drift on one sliceGRF · Innovation Nexus
Evaluation by slice
| All | 0.80 | 0.02 | 5478 | Pass |
| Region A | 0.86 | 0.05 | 1696 | Pass |
| Region B | 0.64 | 0.09 | 558 | Review |
| Rare class | 0.65 | 0.13 | 113 | Below bar |
Method card
- Method
- Artificial intelligence and machine learning
- Split
- time-based, no leakage
- Features
- 55
- Label
- MODELLED
Population stability
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 |
|---|---|---|
| Policy and legal text corpora | Training and test material for text coding | Published statistics |
| Patent full texts and classifications | Technology mapping and diffusion tracing | Published statistics |
| Model documentation and safety frameworks | Comparison of developer practice | DECLARED |
| Benchmark and evaluation results | Capability and risk tracking | REPORTED |
| Hand-coded validation samples | Reference for classifier accuracy | OBSERVED |
05Policy scenarios
Compare instruments: rules, incentives and information.
Three families of instruments side by side against the baseline, each with its own lag and adoption speed, under the coverage and compliance you set; effect sizes are teaching values to replace with evidence. Scenario: named assumptions about the future, not a forecast.
Controls
- Baseline trend of the outcome
- 2.0% a year
- Coverage of the instrument
- 70%
- Compliance
- 75%
- Effect size, against the teaching value
- 100%
- Years ahead
- 20 years
Readouts
- Largest reduction at the horizon
- Regulation
- Its reduction against the baseline
- 21%
Numbers, methods and sources
| Year | Baseline | Regulation | Economic incentive | Information and capacity |
|---|---|---|---|---|
| 0 | 100.0 | 99.5 | 98.8 | 99.1 |
| 1 | 102.0 | 101.2 | 100.1 | 99.8 |
| 2 | 104.0 | 102.5 | 101.1 | 99.9 |
| 3 | 106.1 | 103.5 | 101.6 | 100.0 |
| 4 | 108.2 | 103.7 | 101.8 | 100.7 |
| 5 | 110.4 | 103.2 | 101.7 | 102.1 |
| 6 | 112.6 | 102.0 | 101.6 | 103.9 |
| 7 | 114.9 | 100.4 | 101.6 | 105.9 |
| 8 | 117.2 | 99.2 | 102.1 | 108.0 |
| 9 | 119.5 | 98.6 | 102.9 | 110.1 |
| 10 | 121.9 | 98.9 | 104.2 | 112.3 |
| 11 | 124.3 | 99.7 | 105.7 | 114.5 |
| 12 | 126.8 | 101.1 | 107.4 | 116.8 |
| 13 | 129.4 | 102.7 | 109.4 | 119.2 |
| 14 | 131.9 | 104.5 | 111.4 | 121.6 |
| 15 | 134.6 | 106.5 | 113.5 | 124.0 |
| 16 | 137.3 | 108.5 | 115.7 | 126.5 |
| 17 | 140.0 | 110.7 | 118.0 | 129.0 |
| 18 | 142.8 | 112.9 | 120.4 | 131.6 |
| 19 | 145.7 | 115.1 | 122.8 | 134.2 |
| 20 | 148.6 | 117.4 | 125.2 | 136.9 |
Methods
- Tools of government Hood (1983), The Tools of Government, Macmillan
- Carrots, sticks and sermons Bemelmans-Videc, Rist and Vedung (1998), Transaction
- Diffusion of innovations Rogers (2003), Diffusion of Innovations, Free Press
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Borders Natural Earth
- Cities Natural Earth
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Innovation Nexus in Foundry
- Tested at Innovation Nexus in Universe
- Codified in Open Infrastructure and Developers in Standards
- Published in Innovation Nexus in Reports
- Taught in Faculty of Technology and Digital Systems in Academy
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 |
|---|---|
| Human double-coding | A random sample of machine-coded texts is coded independently by two people, and agreement is reported beside every result |
| Drift and coverage | Classifiers are re-tested when new legal texts or languages enter the corpus, and coverage gaps by language are stated |
| Error parity | Error rates are compared across jurisdictions, languages and document types before any comparison is published |
Performance degrades when deployment conditions differ from training data. Models can encode biases and fail without clear warning on rare events.
07Implementation
From mandate to evaluation, by pillar.
The steps an instrument takes from mandate to evaluation, each with the pillar that runs it, and the decisions that stay with the authority. Teaching model with stated illustrative values, not a prediction.
- Mandate and problem framing Competent authority (outside the programme)
A competent authority frames the problem and the mandate under its own rules. Howlett, Ramesh and Perl (2009) - Evidence and system model Foundry
The system is modelled with its feedbacks, data and stated limits. OECD (2014), principle 2 - Options appraised with scenarios Foundry
Instruments are compared under scenarios, with uncertainty shown. Hood (1983) - Exercise with stakeholders Universe
The options are tested in an exercise with those who would implement and bear them. OECD (2014), principle 3 - Standard or guidance drafted Nexus Standards
What works is codified as a standard or guidance, open to comment. Nexus Standards process - Decision by the authority Competent authority (outside the programme)
The authority decides, outside the programme, with the evidence on record. Howlett, Ramesh and Perl (2009) - Capacity and training Risk Academy
Those who implement are trained on the decided instrument and its data. Sendai Framework (2015), priority 2 - Monitoring and public report Reports
Indicators are published with sources, limits and corrections. OECD (2014), principle 5 - Evaluation and revision Nexus Standards
Results return to the standard and the model: the cycle closes. Howlett, Ramesh and Perl (2009)
Methods
- Policy cycle Howlett, Ramesh and Perl (2009), Studying Public Policy, Oxford University Press
- Governance of critical risks OECD (2014), Recommendation of the Council on the Governance of Critical Risks
- Layered drawing of directed graphs Sugiyama, Tagawa and Toda (1981), IEEE SMC 11
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Innovation Nexus in Foundry
- Tested at Innovation Nexus in Universe
- Codified in Open Infrastructure and Developers in Standards
- Published in Innovation Nexus in Reports
- Taught in Faculty of Technology and Digital Systems in Academy
09Instruments and commitments
How AI as subject and tool in innovation governance is governed: instruments, mandates and commitments.
The instruments, mandates, actors and accountability arrangements a policymaker, diplomat or development agency works with, citing the agreements that set them. Structures follow the frameworks named. Any state or progress drawn is illustrative, not a record.
Policy instrument matrix
The kinds of instrument available on AI as subject and tool in innovation governance, by level of government and cooperation.
| Instrument type | Local | National | Regional | Global |
|---|---|---|---|---|
| Regulatory | Zoning, building codes | Laws, standards, licensing | Harmonised rules | Conventions and treaties |
| Economic | Local levies, grants | Budget lines, taxes, insurance schemes | Risk pools, regional funds | Financing mechanisms |
| Information | Local warnings | Disclosure, early warning, statistics | Data sharing arrangements | Global monitoring and reporting |
| Institutional | Local committees | Lead agencies, coordination councils | Regional bodies and secretariats | Multilateral mandates |
| Cooperative | Community agreements | Partnership compacts | Memoranda and joint programmes | Agreements and joint commitments |
Sendai Framework for Disaster Risk Reduction · UNDRRParis Agreement · UNFCCC2030 Agenda and the Sustainable Development Goals · United NationsInternational Health Regulations · WHO
10Stewardship
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.
11Connected
Where this page leads across the Consortium.
12Questions
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.
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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Legal
Legal documents.
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