OECD AI Surveys Overview

• OECD AI Surveys are structured instruments that systematically assess AI diffusion, governance, and socioeconomic impacts across member and partner economies.
• They employ diverse methodologies including stratified sampling, validated psychometric scales, and network analysis to provide quantitative insights.
• Findings highlight varied effects on worker well-being and trust, revealing gaps in ethical AI policy alignment and implementation frameworks.

The OECD AI Surveys comprise a set of structured, large-scale survey instruments and associated policy monitoring activities designed to systematically assess the diffusion, governance, impacts, and social perceptions of AI across member and partner economies. These surveys serve as a critical empirical basis for both quantitative analysis of AI’s socioeconomic effects—including workplace transformation, trust, and well-being—and systematic comparison of national AI policy strategies.

1. Survey Design, Sampling, and Data Collection

The OECD AI Surveys encompass multiple modules targeting both individual workers and employers, as well as populations drawn from broader samples for attitudinal or experimental work. Worker-level modules, as detailed in "AI and Worker Well-Being: Differential Impacts Across Generational Cohorts and Genders" (Nakavachara, 14 Nov 2025), were fielded in early 2022 across seven OECD economies: Austria, Canada, France, Germany, Ireland, the United Kingdom, and the United States, using stratified random samples spanning manufacturing and financial/insurance services. The total worker sample size was 2,917, with national proportions ranging from 9.9% (Ireland) to 17.4% (United States).

Attitudinal and experimental modules, such as those described in "From OECD to India: Exploring cross-cultural differences in perceived trust, responsibility and reliance of AI and human experts" (Agrawal et al., 2023), recruited 351 participants in English-speaking OECD countries using web panels (e.g., Prolific). Participants completed simulated “drone mission” tasks (Search & Rescue or Defence scenarios) with embedded manipulations of AI versus human advisors and interaction modalities ("human-in-the-loop" vs. "human-on-the-loop").

Country-level policy monitoring and content analysis were implemented through comprehensive harvesting of national AI strategies, action plans, and resource frameworks, as described in "Strategic Alignment Patterns in National AI Policies" (Azin et al., 7 Jul 2025). Source documents were drawn from the OECD AI Policy Observatory, spanning 15–20 national strategies published between 2017–2025, with textual content standardized and coded for subsequent matrix and network analysis.

2. Measurement Constructs, Scales, and Coding Schemes

The OECD AI Surveys operationalize a wide array of constructs through validated psychometric instruments, direct behavioral measures, and codified policy attributes.

• Worker Perceptions and Outcomes: Self-reported well-being is at the core, with three targeted items for mental health, job enjoyment, and physical health/safety. Each is assessed by a single question with six response options; empirical models aggregate responses into binary “improved” vs. “not improved” indicators.
• Trust, Responsibility, and Reliance: Trust in human vs. AI experts is measured with the Multi‐Dimensional Measure of Trust (MDMT)—a 16-item, 7-point Likert scale comprising "capacity trust" (e.g., task competence) and "moral trust" (e.g., sincerity of intentions). Responsibility attributions utilize a 7-point scale across entities (self, human expert, AI expert, programmer, seller). Behavioral reliance is quantified as the proportion of trials in which advice is followed.
• Policy Analysis Constructs: Coding in national strategy analysis employs three primary schemes: (1) strategic objectives (e.g., economic competitiveness, ethical/responsible AI, workforce development), (2) foresight methods (e.g., horizon scanning, scenario development), and (3) implementation instruments (e.g., research funding, regulatory frameworks). Dual-coder text segment evaluations assign 0–3 alignment-intensity scores (Cohen’s κ > 0.7).

3. Statistical and Analytical Methodologies

Quantitative and computational methodologies underpin the interpretation of OECD AI Survey data.

• Regression and Subgroup Analysis: Primary estimators for individual-level outcomes are Linear Probability Models (LPMs), modeling binary indicators as a function of AI use, generational cohort dummies, gender, sector, education, firm size, and country fixed effects. Subgroup analysis stratifies β₁ (AI use effect) by cohort and gender. Probit and logit models yield comparable marginal effects.
• Experimental ANOVA Designs: Attitudinal experiments employ two-way mixed ANOVAs (Sample × Expert Type), with follow-up t-tests, effect-size (Cohen’s d), and variance-explained (η²) reporting. Linear mixed-effects regressions decompose trust into capacity and moral dimensions, with random intercepts at the participant level.
• Matrix Visualization and Network Analysis: For policy strategy alignment, pairwise alignment matrices $M_{ij}$ aggregate weighted co-occurrences of objectives, foresight methods, and instruments. Weights (w_k) are derived from document page count or expert-assigned scores, with normalization to facilitate comparison. Matrix entries feed into multidimensional heatmaps and construct a weighted undirected network G=(V,E), where node centrality, clustering, and community modularity (Q) metrics are computed analytically.

4. Empirical Findings and Cross-Country Patterns

OECD AI Surveys have yielded several robust, replicable empirical findings, with both attitudinal and structural implications.

• Worker Well-being: Across the seven-country sample, AI use is associated with absolute increases of 8–21 percentage points in self-reported improvement of mental health, job enjoyment, and physical health, conditional on worker and sectoral controls. Generation Y benefits most (mental health: +17.4 pp; enjoyment: +21.9 pp; physical: +13.1 pp, all significant), while Generation Z sees no significant mental or physical health effects, but a job enjoyment gain (+15.2 pp). Gender disaggregation reveals that women experience stronger mental health benefits (+11.9 pp), whereas men exhibit greater physical health improvements (+8.9 pp).
• Trust and Responsibility Attribution: OECD respondents assign higher “capacity trust” to AI experts, but greater “moral trust” and responsibility to human experts. This dichotomy neutralizes overall preference, with no systematic difference in whether advice from AI or humans is followed. Responsibility attributions are highest for self and human experts; programmers and sellers are consistently rated below-median. These results suggest limited perceived “responsibility gaps” in contexts with clear human decision-override mechanisms.
• National Policy Architecture: Analysis of 15–20 OECD and partner national strategies reveals four alignment archetypes: Economic Innovator (market-led, funding-centric), Ethics-Regulator (rights-based, regulatory clustering), State-Directed Builder (public sector-focused, institutional creation), and Hybrid Developer (mixed objectives/instruments). A prevailing vulnerability is weak alignment between ethical/responsible AI objectives and implementation frameworks (mean M_{ethics,reg} ≈ 0.40), in contrast to strong economic–funding linkage (M_{econ,fund} ≈ 0.85). The coverage of workforce objectives by explicit skills programs remains incomplete (~55%), and international collaboration objectives often lack matching instruments.

5. Methodological and Policy Implications

The OECD AI Surveys framework enables the identification of dynamic interaction effects among technological adoption, user perception, and governance strategy.

• Implications for System and Policy Design: In OECD contexts, building user acceptance and ethical assurance for AI requires not only demonstrable system “capacity” but also robust “moral trust” mechanisms—primarily through transparency, explainability, and clear human accountability (human-in/on-the-loop architectures). At the policy level, high strategic coherence is generated by explicit, well-mapped pathways from strategy objectives to concrete instruments, with narrative tracing of foresight-driven policy evolution. Multi-method foresight “portfolios” and permanent, cross-agency governance councils are recommended institutional features, alongside periodic network-analytic monitoring of alignment.
• Interpretation of Heterogeneity: Generational and gender-based differential effects underscore the need for targeted implementation—mid-career adults (Generation Y) and women may disproportionately benefit from tailored AI deployment, whereas digital natives (Generation Z) may not experience further psychological or physical relief due to prior technological acclimatization.

6. Limitations, Robustness, and Future Research Directions

Limitations of the OECD AI Surveys include cross-sectional design (limiting causal inference), reliance on self-report (subject to reporting, recall, and selection biases), and aggregation of distinct AI tool types under a single binary indicator. Robustness is validated through alternative model specifications (logit, probit), inclusion of job satisfaction as an additional control, and subgroup stratification.

A plausible implication is that longitudinal survey waves, finer granularity of AI types, and integration of contextual organizational variables (training regimes, implementation strategies) will be necessary to isolate causal mechanisms and inform differentiated policy responses. Further research leveraging the OECD AI Survey architecture could extend to outcome domains such as productivity, diversity, and long-run labor market trajectories.

7. Contribution to the Comparative Study of AI Governance and Impact

The OECD AI Surveys provide a uniquely comprehensive, cross-national empirical platform for analyzing the societal penetration of AI, the institutional coherence of national AI strategies, and the distributional effects of AI-driven transformation in the workforce. By integrating fine-grained individual survey data with systematic content analysis of governance architectures, the surveys enable the quantitative and structural diagnosis of strengths, gaps, and vulnerabilities in the emerging AI order within and across OECD jurisdictions. This scaffolding supports both evidence-based policymaking and comparative academic research, allowing for the identification of generalizable patterns and context-specific divergence in AI diffusion and regulation.