GRF · Information Integrity and Public Trust
Institutional trust
Trust in governments, courts, science and media lets institutions act with consent and lets people rely on information they cannot check themselves. Trust rises and falls with performance, fairness, openness and the experience of being heard. This page sets out how institutional trust is built and lost, the surveys and records that measure it, and the institutions whose decisions shape it.
Institutional trust in seven parts.
- 01The questionInstitutional trust: how the risk develops
- 02ViashiriaWhat is measured, and how well
- 03VyanzoWhere the evidence comes from
- 04DecisionsWho decides, and what the evidence serves
- 05Core questionsWhat experts ask
- 06Mechanisms and driversTrustworthiness, not only trust
- 07Methods and dataHow the evidence is built, and where it stops
Institutional trust: how the risk develops.
Evidence, transparency and public confidence. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Performance and fairness
People judge institutions on whether they deliver and whether they treat people fairly. Experiences of unfair treatment reduce trust more than general dissatisfaction with outcomes.
Openness signals integrity
Publishing decisions, data and reasons lets people see how institutions work. Secrecy and unexplained reversals invite suspicion even when decisions are sound.
Polarisation splits trust
Trust increasingly follows political identity. An institution trusted by one group can be distrusted by another, which makes any message from it divisive.
Lost trust is slow to rebuild
Scandals and failures cut trust quickly; recovery takes sustained change. Corrections and accountability help only when they are visible and credible.
People and hazards in Eastern Europe.
Population density, night lights and flood water over Eastern Europe, from NASA GIBS. Each layer carries its own date.
Ramani shirikishi inahitaji JavaScript. Orodha ya tabaka na vyanzo imetolewa hapa chini.
Tabaka 3
People and hazards, for this page
Kibodi, ramani ikiwa imelengwa
Shikilia Ctrl au ⌘ na usogeze ili kukuza
Ya karibuni zaidi inayopatikana
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Inapakia picha za hivi karibuni za NASA.
Buruta ili kusogeza. Ctrl au ⌘ na usogeze, bana au tumia vitufe ili kukuza; elea juu ili kuona thamani zilizo chini ya kiashiria; weka upya hurudisha kwenye eneo.
People and hazards, framed on Eastern Europe.Bidhaa za satelaiti na vielelezo kama NASA inavyozichapisha, kila moja na tarehe yake. Ni viashiria, si tathmini za eneo lolote.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · U.S. Geological Survey · NOAA GOES-East ABI GeoColorVyanzo na leseni
- 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). Data huria ya NASA
- 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). Data huria ya NASA
- 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). Data huria ya NASA
- 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). Data huria ya NASA
- 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). Data huria ya NASA
- 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. Data huria ya NASA
- Imetengenezwa kwa Natural Earth. Data huria za ramani za vekta na rasta @ naturalearthdata.com. Kikoa cha umma
- © 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). Data huria ya NASA
- Muktadha kwenye tufe na ramani bapa: mwambao, mipaka, barabara na majina ya mahali kutoka tabaka za marejeo za NASA GIBS (data ya OpenStreetMap, ODbL, © wachangiaji wa OpenStreetMap); picha za Blue Marble kama msingi wa hiari; ardhi ya 3D na usomaji wa mwinuko kutoka faharasa ya rangi ya mwinuko ya ASTER GDEM (madaraja ya 5 m hadi 200 m, yaliyokuzwa ili yasomeke). Mipaka imechorwa kama ilivyochapishwa na haimaanishi msimamo wowote kuhusu eneo lolote.
- Matukio ya moja kwa moja: NASA Earth Observatory Natural Event Tracker (EONET); matukio ni ya kuashiria tu na yanabaki na viungo vya vyanzo vyao asili.
- Moja kwa moja: NASA EONET. Matukio ni ya kuashiria tu, si tahadhari rasmi za mamlaka.
- Moja kwa moja: U.S. Geological Survey. Matukio ni ya kuashiria tu, si tahadhari rasmi za mamlaka.
- 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: Tunakiri matumizi ya picha zinazotolewa na huduma za NASA Global Imagery Browse Services (GIBS), sehemu ya NASA 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.
Tabaka na tarehe
- Msongamano wa watu, 2020: Idadi ya watu: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · tuli
- Taa za usiku, kila siku: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Okt 2026
- Maji ya mafuriko, mchanganyiko wa siku 3: VIIRS Global Flood Product, NASA LANCE · 14 Okt 2026
- Moto unaowaka na hitilafu za joto: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Okt 2026
- Rangi halisi: VIIRS NOAA-20 mng'ao uliosahihishwa, NASA LANCE · 13 Okt 2026
- Snow cover: VIIRS NOAA-20 NDSI, NASA LANCE · 13 Okt 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 |
|---|---|---|---|---|
| Trust in national government | share of respondents with high or moderately high trust | biennial | OECD Trust Survey | ESTIMATED |
| Confidence in the judiciary | share of respondents | periodic | World Values Survey and Gallup World Poll | ESTIMATED |
| Trust in scientists | share of respondents | periodic | Wellcome Global Monitor | ESTIMATED |
| Trust in news | share of respondents who trust most news most of the time | annual | Digital News Report | ESTIMATED |
| Perceived fairness of public services | share of respondents | biennial | OECD Trust Survey | ESTIMATED |
| Open government data availability | score | biennial | OECD OURdata Index | ESTIMATED |
Narrative monitor.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Narrative monitor
Institutional trust · Crisis information cell · Week 24
Narratives
| Tap water poisoned after flood | 92 | High | False | Correction out |
| Shelters charging fees | 31 | Medium | False | Drafting |
| Dam about to fail | 41 | High | Checking | Holding line |
| Aid only for registered voters | 43 | Medium | False | Prebunked |
Spread
Response protocol
- Detectmonitor
- Verifyprovenance
- Assess harmthreshold
- Respondcorrect · prebunk
- Reporttransparency
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.
| Chanzo | What it gives | Licence class |
|---|---|---|
| OECD Trust Survey | Trust in public institutions and its drivers across member countries | Free, OECD terms |
| World Values Survey | Values and confidence in institutions across countries and waves | Free for non-commercial use, terms to check |
| Wellcome Global Monitor | Trust in science and health | Free to read, terms to check |
| Reuters Institute Digital News Report | Trust in news and outlets | Free to read, terms to check |
| V-Dem dataset | Indicators of democracy, media freedom and accountability | CC BY-SA 4.0 |
What tips a population into acting.
How the share of early movers and the thresholds people hold decide whether a protective behaviour, or a false narrative, stays local or spreads to most people. Teaching model with stated illustrative values, not a prediction. The map on this page shows Eastern Europe; the model itself is not tied to a place.
Controls
- Mean threshold (share of neighbours)
- 18%
- Spread of thresholds
- 8 points
- Early movers
- 2.0% of people
- Contacts per person
- 8
Readouts
- Share who acted in the end
- 100%
- Global cascade
- Yes
- Steps to settle
- 14
Numbers, methods and sources
| Step | Share who have acted (%) |
|---|---|
| 0 | 2.8 |
| 1 | 8.5 |
| 2 | 23.8 |
| 3 | 42.3 |
| 4 | 55.0 |
| 5 | 67.3 |
| 6 | 79.3 |
| 7 | 87.3 |
| 8 | 92.5 |
| 9 | 95.3 |
| 10 | 97.0 |
| 11 | 98.8 |
| 12 | 99.8 |
| 13 | 100.0 |
| 14 | 100.0 |
Mbinu
- Threshold models of collective behaviour Granovetter (1978), American Journal of Sociology 83
- Global cascades on random networks Watts (2002), PNAS 99
- Complex contagions Centola and Macy (2007), American Journal of Sociology 113
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme's own evidence.
Across the six pillars, led by Nexus Consortium
- Experiments Maabara
- Built in Information Integrity and Public Trust in Foundry
- Tested at Information Integrity and Public Trust in Universe
- Codified in Governance, Security and Society in Standards
- Published in Information Integrity and Public Trust in Reports
- Taught in Faculty of Governance, Policy and International Affairs in Academy
Who decides, and what the evidence serves.
Information Integrity and Public Trust prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Nani anaamua | The question | Evidence this page serves |
|---|---|---|
| Heads of public institutions | How to publish decisions, data and reasons | Evidence on drivers of trust and comparable practice |
| Public service leaders | How to improve fairness in service delivery | Survey evidence on fairness and trust |
| Legislatures | What transparency and accountability duties apply to institutions | Comparative open government frameworks |
| Media organisations | How to show their methods and corrections | Trust research and professional standards |
Outputs this topic produces
- Guide to trust surveys and what their questions measure
- Comparative notes on drivers of institutional trust
- Dialogue briefs on transparency and corrections as trust practices
- Questions for public institutions on trust-building
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 Eastern Europe; 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 Ulimwengu
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 Ripoti
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)
Mbinu
- 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 Maabara
- Built in Information Integrity and Public Trust in Foundry
- Tested at Information Integrity and Public Trust in Universe
- Codified in Governance, Security and Society in Standards
- Published in Information Integrity and Public Trust in Reports
- Taught in Faculty of Governance, Policy and International Affairs in Academy
What experts ask.
Trust researchers ask what makes institutions trustworthy and how people judge them.
- What drives trust in public institutions: competence, integrity, fairness, shared values or results?
- Why does trust differ between groups, and how do experiences of unfair treatment affect it?
- How does transparency affect trust, and when can it backfire?
- How quickly is trust lost after failures, and how is it rebuilt?
- How should institutions respond to justified distrust?
Exercise a cascade in institutional trust with the people who decide.
Assembled from the Nexus Standards area profile Information integrity and trust: the hazards it names, the mifumo 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.
Majanga
- Civil unrest
- Ice-jam flood
- Tropical cyclone
- Wimbi la joto
- HIV epidemic
- Dam failure
- Storm surge (set off downstream)
- Intense rainfall (set off downstream)
- River flood (set off downstream)
- Flash flood (set off downstream)
- Dangerous surf (set off downstream)
- Widespread power outage (set off downstream)
- Mass displacement (set off downstream)
- Coastal flood (set off downstream)
- Extreme fire weather (set off downstream)
- Moto wa nyika (set off downstream)
- Heat-driven grid stress (set off downstream)
- Agricultural drought (set off downstream)
Systems and couplings
Controls
NS-0Katiba na kanuniNS-2Muundo wa unukuzi na utambuzi wa mchango kwa UNINS-9UNI za kimuktadha (utekelezaji, matukio, matukio ya programu)NS-11Uthibitishaji na ufuatiliaji (ushahidi, uthibitisho, uchunguzi wa ulinganifu usio na imani)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H52Hazard family: Information-integrity attacks (NHS-1 cyber.integrity)NRS-X7Information integrity and public trustNRS-U23Ulimwengu platform profile: RipotiNRS-A4Zero-trust verification of standards and conformanceNRS-H65Hazard family: Planetary boundary transgressions (NHS-1 planet.boundary)NRS-H72Hazard family: Novel entities and emerging contaminants (NHS-1 poll.novel-entities)
Observation
- GDELT event database and global knowledge graph (listed for reference)
- Our World in Data datasets (listed for reference)
- Number of countries that adopt and implement guarantees for public access to information
Example geography
- The Horn of Africa: 253 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Surface air temperature, daytime (AIRS), NASA GIBS
Trustworthiness, not only trust.
Research distinguishes trust, a belief held by the public, from trustworthiness, a property of institutions. People tend to judge institutions on competence, integrity and benevolence, and on whether they are treated fairly. Personal and community experience, such as encounters with police, health services or welfare offices, strongly shapes trust, and historic mistreatment of particular groups can create well-founded distrust.
Transparency can build trust when it reveals competent, fair processes and honest acknowledgement of uncertainty. It can reduce trust when it exposes failures without accountability. Trust lost through scandal or broken promises recovers slowly. The aim for institutions is not to maximise trust uncritically but to deserve it and to make that visible.
Why now: measured trust in public institutions varies widely, and the OECD Trust Survey tracks how competence, fairness and openness shape it. The platform is designed to convene institutions on practices that earn trust, including open correction, and to record what they commit to.
How institutional trust 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.
Accountability cycle
How commitments on institutional trust are held to account, from commitment to correction.
- CommitGovernmentA commitment with a mandate and a legal basis.
- PlanLead agencyA national plan with owners and milestones.
- ResourceMinistry of financeA budget line or financing on record.
- TekelezaAgencies and local governmentDelivery against the plan.
- ReportStatistics officeIndicators reported in agreed definitions.
- ReviewLegislature, audit, civil societyIndependent review of results and gaps.
- CorrectGovernmentChanges to the plan, published with reasons.
How the evidence is built, and where it stops.
- 01
Trust surveys
Measuring trust over time and across groups with social and behavioural science. Survey questions capture general attitudes, not specific judgements.
- 02
Experimental studies
Testing how transparency or communication affect trust. Effects in experiments may not persist.
- 03
Comparative institutional analysis
Linking institutional features to trust across countries. Causation is hard to establish.
- 04
Qualitative research
Understanding how people experience institutions. Findings are rich but not generalisable alone.
How it is studied, and what the methods cannot say.
- 01Sayansi ya jamii na tabiaMeasuring trust and exposure · Testing interventions · Qualitative study of information needsApplied here
- 02Sayansi ya mitandao na maambukizi ya athariDetecting coordinated behaviour · Tracing narrative spread · Mapping media ecosystemsApplied here
- 03Akili bandia na ujifunzaji wa mashineClassifier error in moderation · Synthetic media detection limits · Recommender auditsApplied here
Questions and answers.
Is high trust always good?
What drives trust in government?
Why does trust differ between groups?
Can communication campaigns build trust?
Does GRF measure trust in specific institutions?
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