Evidence (5539 claims)
Adoption
5539 claims
Productivity
4793 claims
Governance
4333 claims
Human-AI Collaboration
3326 claims
Labor Markets
2657 claims
Innovation
2510 claims
Org Design
2469 claims
Skills & Training
2017 claims
Inequality
1378 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 402 | 112 | 67 | 480 | 1076 |
| Governance & Regulation | 402 | 192 | 122 | 62 | 790 |
| Research Productivity | 249 | 98 | 34 | 311 | 697 |
| Organizational Efficiency | 395 | 95 | 70 | 40 | 603 |
| Technology Adoption Rate | 321 | 126 | 73 | 39 | 564 |
| Firm Productivity | 306 | 39 | 70 | 12 | 432 |
| Output Quality | 256 | 66 | 25 | 28 | 375 |
| AI Safety & Ethics | 116 | 177 | 44 | 24 | 363 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 76 | 38 | 20 | 315 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 77 | 34 | 80 | 9 | 202 |
| Skill Acquisition | 92 | 33 | 40 | 9 | 174 |
| Innovation Output | 120 | 12 | 23 | 12 | 168 |
| Firm Revenue | 98 | 34 | 22 | — | 154 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 84 | 16 | 33 | 7 | 140 |
| Inequality Measures | 25 | 77 | 32 | 5 | 139 |
| Regulatory Compliance | 54 | 63 | 13 | 3 | 133 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Task Completion Time | 88 | 5 | 4 | 3 | 100 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 32 | 11 | 7 | 97 |
| Wages & Compensation | 53 | 15 | 20 | 5 | 93 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 24 | 22 | 9 | 6 | 62 |
| Job Displacement | 6 | 38 | 13 | — | 57 |
| Hiring & Recruitment | 41 | 4 | 6 | 3 | 54 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 10 | 6 | 2 | 40 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 5 | 9 | — | 26 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
Adoption
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Dual-track regulatory regimes (US-aligned vs China-aligned) create market fragmentation: firms must adapt products, compliance, and data practices to divergent regimes, increasing fixed and variable costs.
Analysis of diverging regulatory texts and standards; firm reports on product adaptation and compliance burdens; suggested quantitative measures include firm cost estimates and market fragmentation indicators. (Data sources: regulatory texts, firm statements; sample sizes not specified.)
Relocation of assembly or lower-tier manufacturing may occur, but upstream dependencies (leading-edge chips, EDA software, design tools) remain concentrated and politically sensitive, keeping core capabilities inaccessible to many developing countries.
Supply-chain mapping showing concentration of upstream suppliers; network concentration metrics and value-chain analysis indicating where high-value inputs reside; process tracing of technology-control regimes. (Data sources: supply-chain maps, concentration metrics; sample sizes not specified.)
Export controls on semiconductors and advanced manufacturing restrict access to AI-critical hardware (chips, sensors), raising costs and slowing AI capability adoption in developing countries.
Documentation of export-control measures and their target items; trade-flow and price data showing constrained availability and increased costs; firm-level reports of supply constraints. (Data sources: export-control lists, trade/price data, firm statements; sample sizes not specified.)
Net effect: global economic integration is becoming more power-contested (politically mediated) rather than neutral and market-driven; dependence on external suppliers rises even as some production relocates.
Synthesis of process-tracing events showing political conditions attached to trade and technology links; quantitative corroboration suggested via import-dependence ratios and network concentration metrics before/after shocks. (Data sources: trade shares, network concentration metrics; sample sizes not specified.)
Competing US and Chinese regulation (export controls, standards, data rules) force developing countries to choose or juggle incompatible regimes, raising compliance costs and producing policy trade-offs.
Document analysis of export-control lists and regulatory texts; interviews and qualitative materials reporting government and firm-level compliance burdens; firm adaptation evidence from announcements. (Data sources: regulatory texts, interviews, firm statements; sample sizes not specified.)
For developing countries, the trade war generates new, concentrated vulnerabilities—despite some short-term gains from production relocation—because trade diversion, regulatory alignment pressures, and securitization convert participation in global supply chains into a geo-strategic liability that undermines developmental autonomy.
Combined qualitative sequence analysis (process tracing) tracing tariff and control shocks to downstream effects; corroboration with trade and FDI flow data, supply-chain maps, and firm-level relocation announcements. (Quantitative indicators noted: trade shares, import-dependence ratios, network concentration metrics; sample sizes not specified.)
The US–China trade war has produced a structural shift in global economic governance: economic integration is increasingly embedded in geopolitical competition.
Process-tracing of policy events (tariff escalations, export controls, sanction announcements) and chronologies of regulatory interventions; corroborated with policy documents and qualitative materials. (Data sources indicated: chronologies of tariff changes and export-control lists; sample size/details not specified in text.)
Machine learning systems that rely on ESG signals can be misled by greenwashing or earnings management, producing overconfident or systematically biased recommendations.
Logical extension of literature on disclosure manipulation and model vulnerability; proposed as a risk for AI systems but not empirically validated within the review.
ESG disclosures that are unaudited or manipulated introduce noise and bias into datasets used by machine‑learning models (e.g., credit scoring, portfolio optimization).
Conceptual inference based on literature-documented unreliability of ESG reporting; no primary ML experiments presented in the paper—claim is drawn as an implication for AI/economic modeling.
The reliability of ESG information is often weak; external public auditors and stronger internal controls are critical to ensure trustworthy disclosure.
Aggregated findings from studies on assurance uptake and quality reported in the review; observational evidence in the literature shows low prevalence and variability of assurance services for ESG reporting.
Without reliable assurance and internal controls, ESG disclosure can undermine its credibility for stakeholders.
Literature synthesis noting limited assurance practices, heterogeneous reporting standards, and documented credibility problems in prior studies; conclusion based on secondary analysis rather than new audit data.
ESG disclosure can mask earnings management and opportunistic accounting — the paper terms this an 'ESG paradox'.
Review of empirical and theoretical studies documenting cases and statistical associations between ESG reporting and earnings management indicators (e.g., abnormal accruals, restatements). The paper synthesizes prior findings; it does not present new causal tests.
The persistence of interpretive, human-in-the-loop evaluation implies ongoing labor requirements (annotation, sense-making, governance roles), affecting forecasts of automation and labor substitution in sectors adopting LLMs.
Interview reports describing continued manual work for evaluation tasks across participants; authors draw implications for labor demand.
Evaluation metrics for multi-hazard forecasting and decision tools should go beyond predictive accuracy to include calibration, sharpness, decision-relevance, fairness metrics, and economic utility loss.
Recommendations in the paper's implications section for AI economics and tool evaluation, based on stakeholder needs and decision-relevance considerations identified by MYRIAD-EU.
Open, benchmarked multi-hazard datasets with standardized metadata and labels are needed to enable method comparison and transferability.
Concrete research/practice actions recommended in the synthesis; identification of data standardization and benchmarking gaps from project experience.
Decision and valuation frameworks (e.g., cost–benefit and cost–effectiveness analyses) should be extended to multi-hazard contexts to account for cascading and correlated losses across sectors and time.
Implications for AI economics and concrete recommendations in the paper calling for extensions to existing economic evaluation frameworks to handle multi-hazard complexity.
Early Career Researchers (ECRs) should be empowered through leadership roles and capacity-building within project structures to sustain interdisciplinary innovation.
Project recommendations emphasize ECR leadership and capacity-building as a priority based on internal reflection and organizational learning from MYRIAD-EU activities.
Development and operationalization of Multi-Hazard Early Warning Systems (MHEWS) require support, and MYRIAD-EU engaged practitioners and policymakers to evaluate MHEWS needs and operational uptake.
Project engagement activities with practitioners and policymakers reported to evaluate needs for MHEWS and their operational uptake; conclusions and recommendations for support included in the synthesis.
Equity considerations must be explicitly integrated into multi-hazard multi-risk research and practice to achieve equitable disaster risk reduction and adaptation.
Project emphasis on participatory approaches and stakeholder-derived qualitative data highlighting distributional vulnerability and equity concerns; recommendations for explicit equity integration.
There is insufficient availability of appropriate, solutions-oriented, and user-friendly tools for practitioners and decision-makers; availability should be increased.
Tool development and iterative testing with end users within MYRIAD-EU, and stakeholder feedback pointing to a demand for more usable tools.
Methods are needed to generate both present-day and future multi-hazard and multi-risk scenarios that integrate climate, socio-economic change, and cascading effects.
Project development and testing of scenario methods reported, plus identification of remaining methodological gaps in scenario integration.
Concepts, definitions, and terminologies for multi-hazard and multi-risk work must be mainstreamed and harmonized to enable comparability and communication across disciplines and stakeholders.
Stakeholder feedback and the project's synthesis of interdisciplinary outputs highlighting conceptual fragmentation and communication barriers.
If quantum advantages accrue initially to well-capitalized incumbents (cloud providers, financial firms, pharmaceuticals), we should expect increased market power and higher rents.
Scenario analysis and historical analogs where early compute advantages concentrated market power; qualitative market-structure modeling.
Benefits of quantum diffusion are likely to be uneven across countries, firms, and workers—boosting regions with strong innovation ecosystems and possibly increasing market concentration among compute-capable incumbents.
Multi-region/sectoral modeling with heterogenous adoption and capability parameters; historical analogs showing concentration following early compute advantages; scenario comparisons.
Without coordinated investments and governance, large theoretical gains may remain unrealized or be very unevenly distributed.
Policy counterfactual scenarios in which underinvestment, fragmented governance, or restrictive export regimes reduce adoption elasticities and infrastructure readiness, producing lower and more concentrated macro gains compared with coordinated-investment scenarios.
High executive digital cognition on its own tends to weaken the policy's positive effect on energy utilization efficiency (interpreted as short-run adjustment costs from digital transformation).
Interaction tests between policy treatment and an executive-level digital-cognition measure show a negative interaction coefficient in DID regressions; authors interpret this as evidence of short-run adjustment costs.
The under‑use of external text sources in the reviewed literature may be due to privacy, legal/regulatory uncertainty, or integration costs.
Authors' interpretation linking observed low coverage of external text sources (social media, news, reviews) in the 109 articles to plausible barriers (privacy/regulation/integration); no direct empirical test in the review.
Restrictions on cross‑border data flows or fragmented privacy rules reduce the training data available to AI systems, lowering the quality and scalability of AI services exported internationally.
Theoretical linkage and literature on AI training data needs synthesized in the paper; no original empirical measurement of AI performance loss presented.
Support systems for digital services exporters, especially SMEs, are inadequate in China.
Review of policy documents and literature highlighting gaps in finance, legal support, and standards compliance assistance for SME internationalization (qualitative).
China's platform firms show uneven internationalization and platform infrastructure is not consistently internationally competitive.
Case examples and synthesis of domestic/international studies on platform internationalization included in the review (qualitative evidence).
China has limited influence in high‑level trade rule formation.
Policy review and comparative institutional analysis within the literature review; descriptive assessment of China's participation in multilateral rule‑making (no formal measurement of influence).
Current institutional, technological, and market shortcomings limit China’s ability to close the gap with economies operating under high‑standard trade regimes.
Qualitative comparative analysis of policy and institutional frameworks against high‑standard trade members; literature and case examples (no new microdata).
Widespread deployment of similar models could create correlated failures or fraud vectors, implying systemic risk that may warrant macroprudential attention.
Analytic caution based on model homogeneity and case/literature discussion; speculative systemic risk concern rather than empirically demonstrated.
There is regulatory uncertainty around AI-generated filings and responsibility/liability for automated outputs.
Analysis and literature review discuss unclear regulatory positions and legal risks noted in case organizations' deployment considerations.
Integration complexity with legacy ERP/financial systems and sharing-center processes is a significant implementation challenge.
Case study narratives describe integration work and friction points; analytic framing highlights ERP compatibility issues.
Model hallucinations, lack of explainability, and limited audit trails limit safe adoption.
Paper cites literature and case observations about model reliability and explainability issues; examples and discussion are qualitative.
Data privacy, confidentiality, and cross-border data transfer concerns are important barriers to deployment.
Challenges enumerated from case studies and literature; specific organizational concerns cited in cases (Xiaomi, Deloitte) and in regulatory discussion.
Explainability, auditability, or data-localization requirements could favor larger vendors with compliance capacity, increasing market concentration and affecting competition among AI suppliers.
Market-structure argument grounded in regulatory-compliance burden analysis and comparative examples; not supported by empirical market data in the study.
Legal uncertainty and strict procedural requirements increase compliance costs and regulatory risk, which can slow AI adoption by firms and public agencies.
Theoretical economic implications drawn from legal analysis and comparative observations; no empirical measurement of costs or adoption rates in the study.
AI can restrict or reshape human administrative discretion in legally sensitive ways.
Doctrinal analysis of statutory specificity and formal procedural requirements in civil-law contexts, illustrated with Vietnam as the exemplar case; comparative observations.
Five qualitatively distinct D3 reflexive failure modes were identified in model responses, including categorical self-misidentification and false-positive self-attribution.
Qualitative coding and taxonomy reported in Results: five D3 categories cataloged with examples; identification based on analysis of model responses to narrative dilemmas (sample drawn from the study runs).
A probe composed of deliberately unresolvable moral dilemmas embedded in literary (science-fiction) narrative resists surface performance and exposes a measurable gap between performed and authentic moral reasoning.
Experimental application of the probe to 13 distinct LLM systems across 24 experimental conditions (13 blind, 4 declared re-tests, 7 ceiling-probe runs), with scoring and qualitative coding showing discriminating failure modes and a measurable gap in responses.
Existing AI moral-evaluation benchmarks largely measure surface-level, correct-sounding answers rather than genuine moral-reasoning capacity.
Comparative argument based on study results showing a measurable gap when applying the authors' narrative-based probe (unresolvable SF dilemmas) versus standard benchmarks; empirical support comes from experiments across 24 conditions and 13 systems showing systems produce plausible-sounding but reflexive/invalid reasoning on the narrative probe.
Capabilities and data advantages for certain vendors could lead to market concentration and platform dominance in AI-driven educational feedback.
Expert concern synthesized from the workshop of 50 scholars about market dynamics; theoretical warning without empirical market-structure analysis in the report.
Differential access to high-quality AI feedback systems and bias in training data can exacerbate educational inequalities and harm marginalized groups.
Expert consensus and thematic analysis from the 50-scholar workshop, raising equity and bias risks; no empirical subgroup effectiveness estimates included.
Learners may over-rely on AI feedback or game systems to obtain desirable responses, reducing effortful learning.
Workshop participant concerns synthesized qualitatively; cited as risk and an open empirical question—no experimental data provided.
Field observations from an enterprise deployment demonstrate production failure modes traceable to missing identity propagation, timeout/budgeting policies, and machine-readable error semantics.
Empirical context described as field lessons from an enterprise agent platform integrated with a major cloud provider's MCP servers; production failure vignettes and operational log analysis (client redacted).
MCP lacks three protocol-level primitives needed for reliable, production-scale agent operation: identity propagation, adaptive tool budgeting, and structured error semantics.
Observational analysis and classification of production failures from an enterprise agent deployment; taxonomy of failure modes identifying gaps in these specific areas.
Chat-like interfaces commonly activate misleading beliefs including overtrust in correctness/robustness, attribution of goals or moral agency, and underestimation of hallucination/bias/privacy risks.
Aggregated observations from literature in HCI and ethics; suggested examples rather than empirical prevalence estimates; no sample size given.
Natural conversational style creates the impression the system is human-like, intentional, or reliably knowledgeable.
Conceptual claim supported by synthesis of prior work on anthropomorphism and conversational interfaces; no new quantitative data provided.