Evidence (6869 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Governance
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Perception of increased legal risk and regulatory uncertainty may slow adoption of GLAI and redirect investment toward safer subfields (verification tools, retrieval-augmented systems, formal-reasoning hybrids).
Economic reasoning and market-design argumentation based on risk/uncertainty dynamics; no econometric or survey data presented.
Divergent regulatory regimes (e.g., strict EU rules vs. looser regimes elsewhere) may produce regulatory arbitrage, influencing where GLAI companies locate, invest, and trade internationally.
Cross-jurisdictional regulatory analysis and economic inference about firm behavior under differential regulation; no firm-level relocation data provided.
The positive macroeconomic effects of AI are severely limited by structural issues, notably large petroleum import volumes and the fiscal burden of incomplete fuel subsidy reforms.
Integrated quantitative analysis showing that operational savings are outweighed by import volumes and subsidy fiscal costs; contextual fiscal data cited (fuel subsidy reform peak).
Evaluations that measure outcomes only via official-language channels risk underestimating impacts where vernacular mediation is central.
Argument based on the discrepancy between vernacular-mediated comprehension/adoption observed in the sample and the likely invisibility of those effects in official-language measurement channels; supported by questionnaire and qualitative data.
DPPs raise privacy and surveillance risks if personal data are linked to product use; economic regulation should incentivize privacy-preserving analytics (e.g., federated learning, differential privacy) and data minimality to maintain trust.
Risk assessment and governance recommendation grounded in stakeholder concerns and standard privacy literature; not empirically measured in the surveys.
This paper provides the first empirical demonstration of knowledge graph poisoning against a production-scale agentic system, distinct from CTI embedding poisoning.
Authors' novelty claim based on their literature/contextual positioning and the reported production-scale experiments; asserted as 'first' in the paper summary.
Whether any deployed agent does this, and by how much, no one can currently measure.
Paper's statement about prior lack of measurement capability for prose-recommendation steering in deployed LLM-OTAs.
Interpretive, ad-hoc human-centered evaluation practices (e.g., “vibe checks”, team sense-making) are rational adaptations to LLM behavior rather than merely sloppy or inferior methodological choices.
Authors' interpretive argument based on interview evidence where practitioners explained why such practices persist and how they serve sense-making for unpredictable model behavior.
The possibility of strategic argument construction (gaming) motivates governance needs: standards for provenance, certification, and liability rules.
Policy recommendation based on anticipated incentive problems; no empirical governance evaluations.
Standard GDP statistics can mask AI-driven demand shortfalls; central banks and statistical agencies should therefore monitor labor-share–velocity links, distributional income measures, and consumption by income quantile in addition to headline GDP.
Theoretical Ghost GDP channel and calibration results showing divergence between measured GDP and consumption-relevant income; policy recommendation follows from those model results.
Health technology assessment (HTA) frameworks should be adapted to evaluate models trained on synthetic or hybrid data, incorporating metrics for fidelity, domain generalization, and economic impact (cost-effectiveness, budget impact, distributional effects).
Recommendation from the review synthesizing HTA literature and gaps identified when applying existing HTA to AI models trained on non-traditional data sources; based on policy analysis rather than empirical HTA trials of synthetic-data models.
Technical fixes alone are insufficient: governance, validation pipelines (e.g., health technology assessment), and capacity building are needed for safe, effective uptake of synthetic-data–trained AI.
Cross-disciplinary synthesis of governance analyses, health technology assessment literature, and implementation studies in the review arguing for combined technical and institutional interventions; recommendation-based evidence rather than new empirical trials.
AI changes the nature of capital (digital/algorithmic assets) and complicates productivity accounting; researchers should decompose firm-level productivity gains into AI technology, complementary organizational capital, and human capital effects.
Theoretical proposal grounded in productivity accounting literature and conceptual discussion; no single decomposition empirical result presented.
Policy and governance issues become salient: liability, IP, security, and certification of AI-generated code require new standards for provenance, testing, and accountability.
Argument based on practitioner-raised concerns about security, IP, and provenance in the Netlight study; authors recommend policy attention; no legal/regulatory analysis or empirical policy evaluation provided.
Time-series metrics (e.g., derivatives like d/dt(student enrollment)) are useful monitoring signals for validation and system oversight.
Methodological suggestion in the paper proposing time-series analysis of enrollment and other administrative data; no empirical demonstration or threshold criteria provided.
Most prior work studies AI sabotage in AI-only settings and pays limited attention to the role of human oversight in detecting and mitigating such malicious behavior.
Authors' characterization of existing literature (literature review / related work section).
AI development did not moderate the COVID-19–driven decline in tourism’s GDP share (no significant interaction effect).
Interaction specifications in the fixed-effects panel models (33 countries, 2017–2023) showed no significant moderation by AI development on the COVID-19 effect; authors state AI development did not moderate this decline.
This work provides the first systematic evaluation of LLM bidders in repeated spectrum auctions.
Statement of novelty/claim of contribution in the paper's abstract. This is a claim about the literature coverage and originality rather than an empirical outcome; supporting evidence would be the authors' literature review (not provided here).
No formal framework exists for auditing whether AI-generated summaries faithfully represent the source population.
Statement in paper's introduction/abstract, based on authors' literature review and positioning of their contribution (qualitative claim).
Five interaction mechanisms were identified, with the majority propagating across the subsystem boundary.
Authors' thematic analysis and STS mapping identifying five cross- or within-subsystem interaction mechanisms; qualitative assessment that most propagate across subsystem boundary.
The operative risk for legislators is not stable ideological bias in LLMs but contextual ignorance shaped by training data coverage.
Authors argue from observed model behavior on the 15 proposals (good performance on well-covered standardized templates; failures on idiosyncratic items) and interpret this as evidence that errors are driven by training-data coverage rather than consistent ideological bias.
Most action tools support medium-stakes tasks like editing files.
Classification of action tools by task consequentiality using O*NET mapping and inspection of tool functions (paper states majority are medium-stakes, e.g., file editing).
CAFTA spillovers stabilized import volumes from third countries (reduced volatility) for Chinese agricultural imports.
Analysis of import volume volatility metrics over 2000–2014 using customs data within DID framework; volatility/variance decline identified as an outcome in the mechanisms/secondary channel tests.
The report provides scenario-based forecasts for HACCA emergence across near-, mid-, and long-term timelines, identifying capability thresholds to monitor.
Capability trajectory assessment combining trends in AI capabilities, automation of software tasks, computation availability, and diffusion dynamics; scenario and expert-judgment approach (qualitative forecasting).
A Sankey diagram of thematic evolution shows lexical convergence over time and indicates that a small set of authors has disproportionate influence in structuring the discourse.
Thematic evolution analysis visualized with a Sankey diagram; author influence inferred from performance trends (citations/publication counts) in the bibliometric data.
CID does not significantly mediate the relationship between SCD and strategic green innovation.
Mediation tests showing that while CID is related to substantive innovation, the indirect effect via CID on strategic green innovation was statistically insignificant.
This paper is one of the first systematic reviews focused specifically on NLP in bank marketing, organizing findings along the customer journey and the marketing mix to provide a practical taxonomy.
Authors' stated novelty claim based on the scoped literature search (2014–2024) and topical focus; novelty inferred from the small number of prior papers identified at the intersection.
There is a need to develop new trade statistics that capture AI‑enabled services and platform‑mediated cross‑border transactions.
Methodological gap identified across reviewed literature and statistical analyses; recommendation based on descriptive assessment (no development of such statistics in the paper).
Productivity gains from AI may be under- or mis-measured if national accounts and tax systems do not adjust for AI-driven quality changes in services.
Analytic observation in the paper's measurement and externalities discussion; not empirically tested within the study.
Distributed agency (Problem C) complicates classical principal–agent models; economists should develop models that capture multiple, overlapping agents and ambiguous attribution of outcomes.
Conceptual implication for economic modeling derived from the paper’s diagnosis of distributed agency; recommendation for formal modeling and simulations but none provided.
An orchestrator coordinates components with intent-aware routing and layered safety checks, enabling multi-step workflows and productized services.
Paper describes an agentic tool-calling framework and multi-layer orchestrator used for intent-aware routing, defense-in-depth safety validation, and multi-step workflows.
Aura is a long-form ASR system capable of handling hours-long audio.
Paper lists Aura in the product stack as 'long-form ASR handling hours-long audio.' Specific evaluation metrics or training data for ASR are not provided in the summary.
Arabic content comprises only about 0.5% of web data despite roughly 400 million native speakers.
Paper cites this data-point to motivate intentional data strategies for Arabic underrepresentation on the web; exact source of the web-proportion not specified in the summary.
Three primary adoption archetypes in large pharma are (1) partnership-driven acceleration, (2) culture-centric transformation, and (3) production-first democratization.
Conceptual classification in the editorial derived from trends and illustrative examples rather than empirical survey or sampling; no quantitative validation provided.
This paper systematically studies the Impact Mechanism of artificial intelligence on the Globalized Division of Labor and reveals the Structural Transformation under Technology Substitution and Data Elements Dual-wheel Drive through Literature Review and Theoretical Analysis.
Methodological claim: supported by the paper's literature review and theoretical analysis; no quantitative sample or empirical design indicated for this specific conclusion in the excerpt.
The information wedge vanishes precisely when signals are exogenous to controls, thereby delineating when strategic belief manipulation matters.
Analytical condition in the paper: shows V^i_t = 0 if and only if the signal-generating process does not depend on agents' controls; uses this equivalence to identify boundary between endogenous and exogenous-signal regimes.
There is a gap in the existing literature regarding empirical evidence about the relationship between AI/Big Data use and market uncertainty during economic downturns.
Paper motivates the study by citing this gap based on its literature review (the summary does not list the reviewed works or systematic review method).
This study empirically tests a theoretically acknowledged but rarely tested relationship (AI adoption → performance conditional on structural constraints) in an emerging-economy setting.
Literature gap claim supported by the authors' review and execution of an empirical test using survey data from 280 Tunisian SMEs and PLS-SEM.
Institutional conditions do not exert a significant moderating influence on the relationship between AI adoption and firm performance in this sample.
PLS-SEM moderation tests on the 280 Tunisian SMEs found the institutional-environment moderator to be non-significant.
Empirically, many markets are concentrated and characterized by large, dominant employers.
Empirical assertion in the paper; the excerpt does not provide the datasets, measures of concentration (e.g., HHI), sample sizes, or citations supporting this statement.
Robust methodology (panel VAR and DID) was used to assess the impact of technology and public policy interventions on emissions reductions.
Methods stated in the paper (panel VAR and difference-in-differences); robustness is claimed by the authors based on using these established econometric approaches, though formal robustness checks are not detailed in the summary.
Previous studies have identified language barriers as impediments to labor market engagement but empirical information assessing both policy reductions and the relative efficacy of professional, AI-assisted, and hybrid translation methods is scarce.
Paper's literature review claim that existing literature documents language barriers but lacks comparative empirical evaluations of policy reductions and multiple translation models; asserted as motivation for current study.
The article clarifies theoretical relationships and gaps between Material Passports, Digital Product Passports, and Digital Building Logbooks.
Theoretical analysis and synthesis section of the SLR where the authors compare concepts and identify overlaps and gaps among MPs, DPPs, and DBLs.
Personal experience with an AI 'boss' did not affect workers' attitudes on using AI in public decision making.
Same randomized design (N > 1,500) with attitudinal measures collected across a three-wave panel; comparison between AI-assigned and human-assigned participants showed no measurable effect on attitudes about AI in public decision making.
Median hourly compensation for gig workers, after accounting for expenses and unpaid time, averages $14.20.
Earnings analysis using platform transaction records adjusted for reported expenses and estimated unpaid labor time; comparative baseline drawn from labor force and administrative wage data (24 countries, 2015–2025).
The paper contributes to both theory and policy by reconceptualizing procurement value and offering an actionable roadmap for embedding ESG principles in public healthcare procurement.
Scholarly contribution claimed via literature synthesis and framework/roadmap creation; contribution is normative and conceptual rather than empirically validated.
We conducted a systematic review and meta-analysis of the literature on AI/HR analytics and organizational decision making, using 85 publications and grounding the work in theories of algorithm-automated decision-making (AST) and matching/hybrid models (STS).
Paper's methods: systematic review and meta-analysis; sample = 85 publications; theoretical framing explicitly stated as AST and STS.
Macroeconomic fiscal moderation remains empirically unvalidated.
Synthesis conclusion from the review noting an absence of empirical evidence that Agentic AI produces macroeconomic fiscal moderation; i.e., no validated studies showing broad fiscal relief effects were identified in the reviewed literature.
By 2024 the RL-FRB/US model produced a federal budget deficit similar to the baseline: RL-FRB/US model: -1,767 trillion $ vs. FRB/US model: -1,758 trillion $.
Reported fiscal balance (federal budget deficit) simulation outputs for 2024 from comparative model runs in the paper.
Empirical evaluation is needed on how AI-induced productivity gains translate into aggregate demand and labor absorption.
Identified research priority in the paper, based on theoretical uncertainty about demand-side labor absorption and lack of conclusive empirical evidence.