GRF · Policy Nexus
Technology policy
Artificial intelligence, biotechnology and other emerging technologies change faster than the rules that govern them, and their risks cross sectors and borders. Technology policy covers how governments set rules, standards, oversight and public investment for innovation while protecting safety, rights and security. This page sets out how regulatory risk develops around new technologies, how governance capacity is measured, and which institutions hold the decisions.
Technology policy in seven parts.
- 01The questionTechnology policy: how the risk develops
- 02IndicatorsWhat is measured, and how well
- 03SourcesWhere the evidence comes from
- 04DecisionsWho decides, and what the evidence serves
- 05Core questionsWhat experts ask
- 06Mechanisms and driversThe pacing problem
- 07Methods and dataHow the evidence is built, and where it stops
Technology policy: how the risk develops.
AI, biotechnology and innovation. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
The pacing problem
Technologies evolve within months while legislation takes years. Rules written for one generation of technology misfit the next, leaving gaps where harms emerge and overlaps where developers face conflicting obligations.
Fragmented jurisdiction
A single AI system or biotechnology product may fall under data protection, product safety, sector regulators and export controls at once. Divergent national approaches create regulatory arbitrage and uneven protection.
Information asymmetry
Developers know far more about capabilities and failure modes than regulators do. Without disclosure duties, access for testing and technical capacity in supervisory bodies, oversight relies on what firms choose to share.
Dual use and diffusion
Knowledge and tools built for benefit can be misused, and they diffuse across borders through publication, trade and openly released models. Controls at one point are bypassed at another unless they are coordinated internationally.
People and hazards in Africa.
Population density, night lights and flood water over Africa, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
People and hazards, for this page
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Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
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People and hazards, framed on 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 EONET · U.S. Geological Survey · NOAA GOES-East ABI GeoColorSources 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 hexagonal 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 hexagonal 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 hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- 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 hexagonal cells where shown (modified). NASA open data
- 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 hexagonal cells where shown (modified). NASA open data
- NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal 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 hexagonal 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 hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal 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.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Live: U.S. Geological Survey Events are indicative, not authority warnings.
- Live: NOAA GOES-East ABI GeoColor (CIRA). 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).
- 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 (5 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
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| National AI strategies and policy initiatives | count of initiatives by instrument type | continuous | OECD.AI national policy database | DECLARED |
| Designated supervisory authority for AI or emerging technology | yes or no, with mandate | periodic review | National legislation and gazettes | Published statistics |
| Regulatory sandboxes and experimentation clauses | count of active schemes by sector | annual | Regulator registers | Published statistics |
| Public consultation on draft technology rules | share of draft rules consulted, with consultation period in days | annual | Regulatory impact assessment registers | Published statistics |
| Regulatory quality | estimate and percentile rank | annual | World Bank Worldwide Governance Indicators | ESTIMATED |
| Biosafety and biosecurity legal framework coverage | categorical, by function | periodic review | WHO and Biological Weapons Convention national reporting | DECLARED |
| Science, technology and innovation policy instruments | count by instrument type and target group | periodic | EC-OECD STIP Compass | DECLARED |
Policy tracker.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Policy tracker
Technology policy · Member state A · Session 3
Policy cycle
Options appraisal, planning law
| A · guidance only | 38 | Low | Mixed | High |
| B · duty with standards | 67 | Medium | Positive | Medium |
| C · duty, standards, funding | 78 | High | Positive | Low |
Decision record
- Owner
- Policy owner
- Decision by
- Legislative committee
- Evidence
- synthesis SY-59
- GRF role
- convenes, does not decide
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| OECD.AI Policy Observatory | National AI strategies, policy initiatives and governance bodies | Open, CC BY 4.0 |
| EC-OECD STIP Compass | Science, technology and innovation policy initiatives reported by governments | Open, CC BY 4.0 |
| EUR-Lex | Legislative texts of the European Union, including the AI Act, with consolidated versions | Free reuse with attribution under the Commission reuse decision |
| Biological Weapons Convention confidence-building measures | State Party declarations on facilities, programmes and legislation, where made public | Public portion, UN terms |
| WIPO Lex | National intellectual property laws and treaties | Free, WIPO terms |
| National regulatory impact assessment registers | Published impact assessments and consultation records for technology rules | Public sector information, national terms |
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. The map on this page shows Africa; the model itself is not tied to a place.
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 Policy Nexus in Foundry
- Tested at Policy Nexus in Universe
- Codified in Foundations and Governance in Standards
- Published in Policy Nexus in Reports
- Taught in Faculty of Governance, Policy and International Affairs in Academy
Who decides, and what the evidence serves.
Policy Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| National legislatures | Whether to legislate horizontally across technologies or through sector rules | Comparative review of legislative approaches and their impact assessments |
| Sector and digital regulators | How to supervise AI systems and biotechnology products within existing mandates | Supervisory practice compared across jurisdictions; capacity and disclosure requirements |
| Data protection authorities | How data protection law applies to model development and deployment | Published decisions and guidance compared across authorities |
| Ministries of foreign affairs and trade | Which technologies to control for export, and how to coordinate controls with partners | Records of multilateral control regimes and treaty obligations |
Outputs this topic produces
- Policy primer on the instruments available for governing AI and biotechnology
- Comparative brief on supervisory mandates and their technical capacity
- Review of regulatory sandbox designs, their safeguards and their exit rules
- Dialogue records from regulator-to-regulator and cross-border exchanges
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. The map on this page shows Africa; the model itself is not tied to a place.
- 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 Policy Nexus in Foundry
- Tested at Policy Nexus in Universe
- Codified in Foundations and Governance in Standards
- Published in Policy Nexus in Reports
- Taught in Faculty of Governance, Policy and International Affairs in Academy
What experts ask.
Technology policy experts ask how to govern what is not yet fully understood.
- How should policy respond to risks that are uncertain, emerging and potentially severe?
- Which instruments, such as standards, licensing, liability, testing or public investment, fit which technologies?
- How can regulators build the technical capacity to oversee rapidly changing technologies?
- How do different jurisdictions' approaches interact, and where is international coordination needed?
- How are the benefits and harms of technology distributed across society?
Exercise a cascade in technology policy with the people who decide.
Assembled from the Nexus Standards area profile Political and governance risk standards: the hazards it names, the systems and communities that carry the loss, the controls and institutions that dampen it and the data that observe it. Pick an initiating event and step through the cascade to see where mandates meet and which decisions fall to whom. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise for dialogue, not a forecast, an assessment or a position. To model a place of your own choosing, open the full System Builder.
Systems and couplings
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NRS-H112Hazard family: Governance and institutional failure (NHS-1 soc.governance)NRS-U9Universe platform profile: Financial Regulation NexusNRS-H59Hazard family: Economic shocks and governance failure (NHS-1 soc.economic)NRS-H118Hazard family: Organised crime and illicit economies (NHS-1 soc.crime)NRS-A5Built-in licensing and rightsNRS-U24Universe platform profile: Policy Nexus
Observation
- Worldwide Governance Indicators (listed for reference)
- V-Dem dataset (listed for reference)
- Global SDG Indicators Database (listed for reference)
- Proportion of population satisfied with their last experience of public services
- Proportion of population who believe decision-making is inclusive and responsive
- Number of countries that adopt and implement national DRR strategies in line with the Sendai Framework
The pacing problem.
Technologies such as AI and biotechnology develop faster than legislative and regulatory processes. Early on, little is known about risks, so regulation risks being either premature or too late. Once a technology is embedded, it becomes harder to change. This dilemma has led to approaches such as risk-based regulation, regulatory sandboxes, mandatory testing and disclosure, and adaptive rules that are revisited as evidence accumulates.
Technology policy also involves strong economic and geopolitical interests. Concentration of capabilities in a few firms or countries raises competition and security questions. Divergent national rules can fragment markets or push activity to less regulated jurisdictions, while coordinated standards can raise the floor of safety everywhere.
Why now: AI, data and platform rules are being adopted in many jurisdictions at once, with different models of oversight. Policy Nexus compares those models, convenes the agencies implementing them, and records lessons that can inform Reports and common practice proposals to the Nexus Standards.
How technology policy 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 technology policy, 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 |
How the evidence is built, and where it stops.
- 01
Comparative regulation analysis
Comparing technology rules across jurisdictions with policy and institutional science. Rules are new and evolving.
- 02
Mechanism design
Designing incentives and institutions for safe development using game theory and mechanism design. Models simplify real behaviour.
- 03
Foresight and scenarios
Exploring possible developments with decision analysis. Scenarios are not predictions.
- 04
Expert and stakeholder dialogue
Structured exchange through policy dialogues. Views of affected groups must be sought deliberately.
How it is studied, and what the methods cannot say.
- 01Decision analysis under uncertaintyOption appraisal for a policy instrument · Adaptive pathways for public planningApplied here
- 02Policy, institutional and decision scienceMandate mapping for a cross-sector risk · Review of regulatory impact assessmentsApplied here
- 03Game theory and mechanism designStability of a transboundary agreement · Design of a regional risk poolApplied here
- 04Causal inference and impact evaluationStaggered adoption of a reform · Eligibility thresholds in programmesApplied here
Questions and answers.
What is the pacing problem?
What is a regulatory sandbox?
Does Policy Nexus assess AI systems or products?
Does regulation slow innovation?
Does Policy Nexus advise technology firms?
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