{"resourceId":"oecd-public-trust-ai","versions":[{"version":"legacy/2026-08-29/oecd-public-trust-ai","resource":{"id":"oecd-public-trust-ai","title":"Cross-national survey finds deep skepticism toward government AI","organization":"Organisation for Economic Co-operation and Development","sector":"Public administration and citizen trust","geography":"OECD member and European accession-candidate countries","publishedAt":"June 29, 2026","sourceName":"Trustworthy artificial intelligence in the public sector","sourceLabel":"2026 OECD Survey on Drivers of Trust chapter","sourceUrl":"https://www.oecd.org/en/publications/2026/06/results-of-the-2025-oecd-survey-on-drivers-of-trust-in-public-institutions_96323a65/full-report/trustworthy-artificial-intelligence-in-the-public-sector_6f98c91a.html","evidenceClass":"independent-research","outcomeClass":"cautionary","topics":["data-security","governance-procurement","accessibility-workforce","operating-model"],"finding":"The OECD's 2025 trust survey found that 35% of respondents across participating OECD countries expected none of six positive outcomes from government AI use, while only 22% held very positive expectations.","sledRelevance":"State and local AI services operate in a trust environment that can determine adoption, resistance, legal challenge, and perceived legitimacy even when a system is technically accurate.","evidence":"Skepticism rose from 24% among people aged 18–29 to 41% among those 50 and older and was higher among people with lower formal education, financial insecurity, or perceived discrimination. Only about 32% expected government to protect personal information from unauthorized access or misuse when using AI, twenty points below confidence in legitimate government data use generally.","architectureImplications":"Expose purpose, data use, human accountability, appeal routes, and service alternatives in the experience; collect trust and usability signals by demographic group; and make privacy-preserving design visible rather than purely contractual.","governanceImplications":"Treat public legitimacy as an outcome measure. Engage affected communities before deployment, publish impact and evaluation evidence, provide meaningful notice and recourse, and avoid using adoption rates as a proxy for trust.","securityPrivacyImplications":"The gap between general confidence in government data use and confidence under AI makes data minimization, access control, breach readiness, explainable data flows, and enforceable purpose limitation central to adoption.","caveats":"The survey measures expectations and perceptions, not observed system performance or causal effects. Country averages conceal large national and local differences, and attitudes may change with direct experience."}},{"version":"enrichment/2026-09-05T02:33:27.019Z/oecd-public-trust-ai","resource":{"id":"oecd-public-trust-ai","title":"Cross-national survey finds deep skepticism toward government AI","organization":"Organisation for Economic Co-operation and Development","sector":"Public administration and citizen trust","geography":"OECD member and European accession-candidate countries","publishedAt":"June 29, 2026","publicationDate":"2026-06-29","eventDate":null,"sourceName":"Trustworthy artificial intelligence in the public sector","sourceLabel":"2026 OECD Survey on Drivers of Trust chapter","sourceUrl":"https://www.oecd.org/en/publications/2026/06/results-of-the-2025-oecd-survey-on-drivers-of-trust-in-public-institutions_96323a65/full-report/trustworthy-artificial-intelligence-in-the-public-sector_6f98c91a.html","evidenceClass":"independent-research","outcomeClass":"cautionary","topics":["data-security","governance-procurement","accessibility-workforce","operating-model"],"finding":"The OECD's 2025 trust survey found that 35% of respondents across participating OECD countries expected none of six positive outcomes from government AI use, while only 22% held very positive expectations.","sledRelevance":"State and local AI services operate in a trust environment that can determine adoption, resistance, legal challenge, and perceived legitimacy even when a system is technically accurate.","evidence":"Skepticism rose from 24% among people aged 18–29 to 41% among those 50 and older and was higher among people with lower formal education, financial insecurity, or perceived discrimination. Only about 32% expected government to protect personal information from unauthorized access or misuse when using AI, twenty points below confidence in legitimate government data use generally.","architectureImplications":"Expose purpose, data use, human accountability, appeal routes, and service alternatives in the experience; collect trust and usability signals by demographic group; and make privacy-preserving design visible rather than purely contractual.","governanceImplications":"Treat public legitimacy as an outcome measure. Engage affected communities before deployment, publish impact and evaluation evidence, provide meaningful notice and recourse, and avoid using adoption rates as a proxy for trust.","securityPrivacyImplications":"The gap between general confidence in government data use and confidence under AI makes data minimization, access control, breach readiness, explainable data flows, and enforceable purpose limitation central to adoption.","caveats":"The survey measures expectations and perceptions, not observed system performance or causal effects. Country averages conceal large national and local differences, and attitudes may change with direct experience.","streamIds":["state-government","local-government"],"roles":{"sales":"Interpretation — Customer problem: a technically functioning public AI service may face skepticism about privacy, accountability, and fairness. Stakeholders: service leadership, community engagement, privacy, accessibility, communications, and groups affected by the proposed use. Discovery: can residents understand the purpose and data flow; what human appeal or alternative exists; and whose concerns are missing from current engagement? Value hypothesis: transparent design and meaningful recourse may support a more trustworthy experience, to be tested locally. Potential engagement: stakeholder discovery and a service-transparency/usability assessment. Unsupported claims: the survey's expectations are not performance results, demographic associations are not causal explanations, and country averages cannot predict local trust or prove that any proposed intervention will improve it.","engineering":"Interpretation — Fit: apply the findings to the user experience and data handling of a specific public AI service; the survey does not establish a technical product preference. Architecture and integration: expose purpose, data use, human ownership, appeal routes, and accessible alternatives within existing service channels. Prerequisites: accurate data-flow documentation, enforceable purpose limits, and responsible privacy/community-engagement owners. Constraints: aggregate trust scores can conceal group differences, and collecting demographic feedback creates its own privacy responsibilities. Security: minimize data, verify access controls and breach readiness, and ensure public explanations match actual processing. Proposed validation: test comprehension, usability, and recourse with affected groups, measure attitudes separately from system accuracy, and avoid unnecessary sensitive feedback collection.","delivery":"Interpretation — Work: engage affected communities, document the service's purpose and data handling, implement notice and recourse, and review trust alongside operational results. Dependencies: accessible channels, service-owner participation, valid privacy explanations, and capacity to respond to appeals. Ownership: the service leader owns legitimacy and accountability; privacy/security teams validate handling; community and accessibility leads design inclusive engagement. Skills and adoption: train frontline staff to explain the AI role and offer meaningful alternatives rather than pressuring use. Governance checkpoints: predeployment engagement, impact review, and periodic feedback assessment. Proposed acceptance: users can identify purpose and human recourse, alternative paths work in tests, and feedback is reviewed across relevant groups using privacy-approved methods. Risks include token consultation and equating adoption with trust."},"retrievedAt":null,"enrichedAt":"2026-09-05T02:33:27.019Z","enrichmentBasis":"archived evidence"}}]}