Evidence (7198 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
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Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
8921 claims
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Productivity
8002 claims
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Governance
7198 claims
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Human-AI Collaboration
6864 claims
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Org Design
4398 claims
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Innovation
4286 claims
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Labor Markets
3629 claims
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Skills & Training
3001 claims
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Inequality
2141 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 790 | 208 | 103 | 950 | 2117 |
| Governance & Regulation | 869 | 411 | 195 | 126 | 1630 |
| Organizational Efficiency | 817 | 202 | 126 | 87 | 1243 |
| Technology Adoption Rate | 675 | 258 | 128 | 106 | 1178 |
| Research Productivity | 462 | 138 | 64 | 347 | 1023 |
| Output Quality | 501 | 193 | 61 | 52 | 807 |
| Decision Quality | 346 | 180 | 84 | 51 | 668 |
| AI Safety & Ethics | 235 | 285 | 70 | 34 | 630 |
| Firm Productivity | 452 | 58 | 91 | 20 | 627 |
| Market Structure | 184 | 171 | 123 | 24 | 507 |
| Task Allocation | 221 | 65 | 76 | 34 | 401 |
| Skill Acquisition | 176 | 62 | 62 | 17 | 317 |
| Innovation Output | 207 | 28 | 48 | 18 | 303 |
| Fiscal & Macroeconomic | 135 | 72 | 44 | 26 | 284 |
| Employment Level | 105 | 56 | 108 | 13 | 284 |
| Consumer Welfare | 121 | 67 | 45 | 11 | 244 |
| Firm Revenue | 160 | 50 | 28 | 4 | 242 |
| Task Completion Time | 182 | 33 | 10 | 13 | 239 |
| Inequality Measures | 45 | 126 | 50 | 6 | 227 |
| Worker Satisfaction | 94 | 73 | 23 | 12 | 202 |
| Error Rate | 76 | 98 | 11 | 4 | 189 |
| Regulatory Compliance | 81 | 73 | 17 | 7 | 178 |
| Automation Exposure | 61 | 59 | 26 | 14 | 163 |
| Training Effectiveness | 97 | 21 | 14 | 19 | 153 |
| Wages & Compensation | 78 | 37 | 25 | 6 | 146 |
| Developer Productivity | 105 | 18 | 14 | 6 | 144 |
| Team Performance | 87 | 17 | 28 | 10 | 143 |
| Job Displacement | 12 | 83 | 21 | 1 | 117 |
| Hiring & Recruitment | 52 | 8 | 8 | 3 | 71 |
| Social Protection | 39 | 17 | 8 | 2 | 66 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 49 | 6 | 1 | 61 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 15 | 14 | — | 3 | 32 |
| Industry | — | — | — | 1 | 1 |
Governance
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Mechanism tests indicate the policy primarily enhances inclusive green growth by strengthening government environmental participation.
Mechanism/mediation tests reported in the study (presumably within the DID framework) showing an increase in measures of government environmental participation associated with the policy and linked to inclusive green growth (sample size not reported).
The policy's positive impact on inclusive green growth is particularly pronounced in non-traditional industrial cities.
Heterogeneity/subsample analysis within the DID framework comparing treated non-traditional industrial cities to others (sample size not reported).
The policy's positive impact on inclusive green growth is particularly pronounced in digital economy clusters.
Heterogeneity/subsample analysis within the DID framework comparing treated cities located in digital economy clusters to others (sample size not reported).
The policy's positive impact on inclusive green growth is particularly pronounced in high-quality development pilot zones.
Heterogeneity/subsample analysis within the DID framework comparing treated cities in high-quality development pilot zones to others (sample size not reported).
The 2015 Green Data Center Pilot Policy effectively promotes inclusive green growth in cities, increasing the average annual growth rate of inclusive green growth by 0.9 percentage points.
Quasi-natural experiment using the 2015 Green Data Center Pilot Policy as treatment, analyzed with a difference-in-differences (DID) econometric approach on city-level data (sample size not reported in the provided text).
A four-phase implementation roadmap translates the MIGT into actionable enterprise programs.
Paper claims to include a four-phase roadmap; this is described as a design/implementation contribution in the excerpt.
A cross-jurisdictional regulatory alignment structure mapping enterprise AI identity governance obligations under EU, US, and Chinese frameworks simultaneously, identifying irreconcilable conflicts and providing a governance mechanism for managing them.
Paper claims to produce a mapping/alignment structure comparing EU, US, and Chinese obligations and to identify irreconcilable conflicts; method not detailed in excerpt.
Machine Identity Governance Taxonomy (MIGT): an integrated six-domain governance framework simultaneously addressing the technical governance gap, the regulatory compliance gap, and the cross-jurisdictional coordination gap that existing frameworks address only in isolation.
Paper presents MIGT as a novel, integrated six-domain framework; described as targeting three specific governance gaps. Evidence cited is the framework design itself (conceptual contribution).
AI-Identity Risk Taxonomy (AIRT): a comprehensive enumeration of 37 risk sub-categories across eight domains, each grounded in documented incidents, regulatory recognition, practitioner prevalence data, and threat intelligence.
Paper claims to have produced the AIRT taxonomy and states its grounding sources (documented incidents, regulatory recognition, practitioner prevalence data, threat intelligence); taxonomy size given (37 sub-categories across eight domains).
The scientific novelty of the work is to interpret omniscalers as structural actors of a new phase of technological races and to refine the concept of digital inequality as inequality of access, control, and scaling.
Author's stated contribution based on theoretical synthesis and conceptual innovation (no external empirical validation reported).
Arenas of competition function as interconnected structural nodes of the contemporary economy, and recognizing them is key to understanding global transformations driven by digital and AI-related competition.
Theoretical argument and systematization combining approaches to digital development and technological races; no empirical network analysis reported.
Digital inequality evolves from asymmetry in access to knowledge, infrastructure, and digital markets toward inequality in control over critical technological nodes and the ability to scale advantages across several high-dynamics arenas.
Theoretical differentiation and chronological framing developed via comparative and structural-logical analysis; no empirical longitudinal data reported.
The 'AI foundation'—semiconductors, cloud services, and AI software and services—serves as the core platform of current technological races.
Conceptual synthesis and structural-logical argument drawing on literature about digital infrastructure and AI; no empirical measurement provided.
Digital inequality manifests at micro-, meso-, and macro-levels as asymmetry between firms, sectors, countries, and regions.
Analytical mapping and theoretical systematization (comparative method); no empirical counts or samples reported.
Digital inequality increasingly concerns access to scaling infrastructures (control over critical nodes) rather than only formal access to technologies.
Theoretical generalization and comparative reasoning across arenas of competition; no quantitative data reported.
Omniscalers scale infrastructural capabilities that are reusable across multiple technological and market environments, thereby generating cumulative self-reinforcing effects.
Theoretical argument and systematization; illustrative conceptual analysis rather than empirical measurement.
Omniscalers emerge as a new type of corporate actor capable of transferring accumulated infrastructural, financial, innovation, and data advantages across several arenas of competition simultaneously.
Conceptual definition and theoretical generalization using comparative and structural-logical methods (no empirical sample reported).
Contemporary competition is shifting from rivalry over individual markets toward control over scaling infrastructures that enable data processing, computing capacity, digital integration, and the diffusion of new business models.
Theoretical argumentation based on structural-logical analysis, comparative method, systematization, and theoretical generalization (no empirical sample reported).
AI-enabled trade outcomes depend not only on technological adoption but also on regulatory clarity, robust digital infrastructure, and harmonized data governance frameworks, offering practical insights for policymakers and firms integrating AI into international business law.
Synthesis/conclusion drawn from the paper's empirical results (PLS-SEM on 350 survey responses) showing the joint importance of the four antecedent factors for trade outcomes via compliance effectiveness.
Among the predictors, cross-border data governance quality exerts the strongest influence.
Empirical comparison of path coefficients in the PLS-SEM model (N=350), with cross-border data governance quality reported as having the largest effect.
Compliance effectiveness significantly mediates the relationship between the institutional and technological antecedent factors (AI adoption, regulatory clarity, digital infrastructure readiness, cross-border data governance quality) and international trade performance.
Reported mediation analysis within the PLS-SEM framework on the 350-response survey showing significant indirect effects via compliance effectiveness.
Compliance effectiveness strongly enhances firm-level international trade performance, as reflected in improvements in trade efficiency, risk reduction, and market expansion.
Empirical PLS-SEM findings from the study (N=350) showing a strong positive path from compliance effectiveness to measures of firm international trade performance.
AI adoption, regulatory clarity, digital infrastructure readiness, and cross-border data governance quality each have a significant positive impact on compliance effectiveness.
Empirical result from PLS-SEM analysis on the survey data (N=350); reported statistical significance of paths from each antecedent to compliance effectiveness.
In practice, AI is applied to legal mechanisms such as automated customs compliance, regulatory monitoring, sanctions screening, and cross-border data transfer governance.
Descriptive/practical claim in the paper citing applications and examples; not presented as a quantitatively tested finding in this study.
Artificial Intelligence (AI) is increasingly reshaping international business law by transforming how firms manage regulatory compliance, governance processes, and cross-border trade operations.
Background/theoretical statement in the paper; positioned as a premise supported by literature and examples rather than by the paper's own empirical analysis.
The next stage of research should not treat forecasting, allocation, and ESG-related corporate finance as separate literatures; instead, future work should build integrated frameworks in which market prediction, portfolio design, and firm-level sustainable finance analysis are jointly modeled under explicit assumptions about data quality, decision frequency, and accountability.
Central recommendation/conclusion of the review advocating future integrated research frameworks (normative guidance based on the literature synthesis).
AI is used not only to predict ESG ratings and financial constraints but also to identify firm heterogeneity, financing frictions, and disclosure-based signals.
Summary of corporate finance and sustainable finance literature in the review indicating applications of AI to predict ESG ratings, financial constraints and to detect firm-level heterogeneity and signals (survey-based; no single sample size).
The review synthesizes the evolution of forecasting methods from classical econometric models to recurrent neural networks, transformers, and hybrid architectures.
Literature synthesis reported in the paper; descriptive summary of methodological developments across forecasting literature (no empirical sample reported).
These domains should be interpreted as parts of a broader decision architecture in which algorithms extract signals from noisy data, transform those signals into investment or financing choices, and then evaluate outcomes under multiple objectives that increasingly include environmental, social, and governance criteria.
Normative/conceptual proposal presented in the review arguing for an integrated interpretive framework (theoretical argument drawing on surveyed literature).
Artificial intelligence has become a major methodological force in financial decision-making.
Statement from the paper's abstract/overview describing AI's role; based on a literature review across financial decision-making domains (no empirical sample reported).
Primary aims of AI implementation were to enhance predictive capacity, automate processes, and support data-driven decisions.
Aggregate finding from the content of the 27 reviewed studies describing the purposes of AI systems.
AI is applied across sectors such as industry, agriculture, finance, education, and public services.
Thematically coded applications across the 27 included studies reporting sectoral deployment of AI.
Across multiple sectors, AI-based tools are increasingly used to support complex decision-making processes.
The review's content analysis of the 27 selected studies which report AI applications across sectors and their use in decision-making support.
In recent years, Latin America has experienced a growing incorporation of Artificial Intelligence (AI) into business and organizational environments, driven by digital transformation, data availability, and competitive pressures.
Synthesis statement from the systematic review of literature (27 selected studies) covering publications 2021–2025 in Scopus; claim drawn from patterns reported across the included studies.
Effective AI governance requires stronger policy capacity, clearer allocation of responsibility, and governance mechanisms that remain robust across divergent technological futures.
Conclusion of the article based on its analysis of uncertainty, adoption dynamics, and framework proposals; grounded in cited policy and scholarly sources.
The article proposes an adaptive governance framework for public institutions that integrates capability monitoring, risk tiering, conditional controls, institutional learning, and standards-based interoperability.
Normative framework proposed in the article, derived from the paper's synthesis of foresight reports and governance scholarship.
The article reconstructs the conceptual foundations of the 'evidence dilemma', differentiated AI risk categories, and the limits of prediction.
Declared analytic activity in the article, based on synthesis of the International AI Safety Report 2026, OECD foresight, and recent scholarship.
Public governance for frontier AI should be based on adaptive risk management, scenario-aware regulation, and sociotechnical transformation rather than static compliance models.
Normative recommendation made by the article, supported by conceptual analysis and references to adaptive governance literature and policy documents.
Recent evidence indicates that AI capabilities are advancing rapidly, though unevenly.
Statement in article referencing recent empirical/foresight sources, e.g. International AI Safety Report 2026 and OECD foresight documents (sources cited in the paper).
The governance of frontier general-purpose artificial intelligence has become a public-sector problem of institutional design, not merely a technical issue of model performance.
Conceptual argument presented in the article, drawing on synthesis of policy reports (International AI Safety Report 2026, OECD foresight) and scholarship in digital government.
Ireland’s high levels of educational attainment offer a strong foundation for benefiting from AI adoption, but targeted educational support (especially for older workers or those with lower formal qualifications) and investment in lifelong learning and retraining will be essential.
Policy assessment based on Ireland's workforce characteristics and the report's scenario findings about which groups face disruption; presented as a recommendation/interpretation.
Increases in returns to capital as a result of AI adoption, while modest in percentage terms, benefit households at the very top of the income distribution, where the vast majority of Ireland’s capital income is concentrated.
Simulated changes in returns to capital combined with income distribution data showing concentration of capital income among top households; reported in the report.
For those who remain in work, AI is expected to increase productivity. We estimate that workers who are not displaced may see modest but broadly shared wage gains.
Scenario assumptions and international evidence on productivity effects of AI, incorporated into the report's simulations of wages for non-displaced workers.
Strategic, forward-looking regulatory measures can improve market contestability in AI-driven sectors without undermining innovation incentives.
Inference from the paper's combined conceptual framework and empirical results showing that interventions (e.g., interoperability, data-access) mitigate exclusionary dynamics while the paper argues they can be designed to preserve innovation incentives.
Interoperability and data-access can alleviate the exclusionary effects of algorithmic advantage.
Empirical interaction/moderation analysis and conceptual/legal argumentation in the paper showing that measures improving interoperability and data access reduce the negative association between algorithmic advantage and market entry/contestability.
Elevated levels of algorithmic advantage are consistently linked to improved market concentration.
Empirical panel-data results from the paper's unbalanced sample of AI-intensive markets, with controls for firm size, capital intensity, R&D expenditure, and industry growth.
Exploitative innovation is directly associated with long-term competitive performance.
PLS-SEM analysis of survey data from 104 Portuguese B2B managers showing a significant direct path from exploitative innovation to performance.
Exploratory innovation's association with long-term competitive performance operates indirectly through GenAI adoption (mediation).
Survey of 104 Portuguese B2B managers and PLS-SEM showing a mediated pathway from exploratory innovation to performance via GenAI adoption in the estimated model.
GenAI adoption is positively associated with long-term competitive performance.
Survey data from 104 Portuguese B2B managers; association estimated via PLS-SEM in the study's structural model.
Ethical governance is the strongest organisational correlate of long-term competitive performance.
Survey data from 104 Portuguese B2B managers; analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM); reported as a comparative strength of model paths.