Evidence (13870 claims)
Adoption
8467 claims
Productivity
7558 claims
Governance
6805 claims
Human-AI Collaboration
6363 claims
Org Design
4132 claims
Innovation
4065 claims
Labor Markets
3526 claims
Skills & Training
2945 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 196 | 98 | 892 | 1984 |
| Governance & Regulation | 817 | 394 | 188 | 121 | 1544 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 627 | 233 | 123 | 96 | 1088 |
| Research Productivity | 411 | 123 | 56 | 332 | 933 |
| Output Quality | 467 | 178 | 59 | 47 | 751 |
| Decision Quality | 320 | 174 | 75 | 42 | 618 |
| Firm Productivity | 435 | 55 | 88 | 20 | 604 |
| AI Safety & Ethics | 214 | 276 | 65 | 33 | 593 |
| Market Structure | 178 | 167 | 122 | 24 | 496 |
| Task Allocation | 207 | 64 | 71 | 32 | 379 |
| Skill Acquisition | 165 | 59 | 60 | 17 | 301 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 52 | 107 | 13 | 279 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 150 | 48 | 26 | 3 | 227 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 63 | 20 | 12 | 184 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 93 | 21 | 13 | 19 | 148 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Automation Exposure | 51 | 54 | 22 | 12 | 142 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Developer Productivity | 94 | 17 | 14 | 6 | 132 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Creative Output | 31 | 17 | 7 | 3 | 59 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Mechanism analysis identifies three systemic transmission pathways for the policy: optimizing factor allocation, deepening digital technology empowerment, and promoting green innovation and sustainability.
Mechanism analysis reported in the study (methods not detailed in abstract) attributing the policy effect to three pathways.
The pilot zones create an optimized 'digital environment' that underlies the positive impact on corporate NQPF.
Empirical analysis in the paper attributes improved corporate NQPF to an optimized digital environment created by the policy intervention; mechanism analysis referenced.
The DML approach flexibly controls for high-dimensional confounding variables and functional form misspecification, enabling highly rigorous causal inference compared with traditional linear models.
Methodological claim based on use of Double Machine Learning in the study (described as addressing high-dimensional confounders and misspecification).
Establishment of China’s National Digital Economy Innovation and Development Pilot Zones significantly enhances corporate New Quality Productive Forces (NQPF).
Quasi-natural experiment using Double Machine Learning (DML) framework applied to A-share listed companies over 2015–2023; empirical results reported as statistically significant.
The proposed system and findings have policy-relevant implications for policymakers and fiscal institutions, improving their ability to name (identify) and react to potential instabilities.
Paper discussion claims implications for policymakers and fiscal institutions based on the proposed framework and synthesized empirical findings; specific policy-impact evaluations are not provided in the excerpt.
This paper proposes a novel framework that uses machine learning and news data to create a regulatory early-warning mechanism for predicting and mitigating fiscal risk.
Paper text describes a proposed framework combining machine learning with news streams; described as a methodological contribution (conceptual design/architecture). No numeric evaluation or sample size reported in the provided excerpt.
Integrating AI into financial ecosystems can strengthen both economic and climate resilience, provided that regulatory frameworks, ethical AI practices, and capacity-building measures are simultaneously addressed.
Paper's concluding recommendation based on combined qualitative and quantitative findings from the three case studies and the 1,500 interviews; framed as conditional policy guidance in the abstract.
Predictive AI models can facilitate climate-resilient decision-making in agriculture.
Reported as a finding from the Thailand AI-supported smart agriculture finance case study, supported by qualitative evidence and (implied) predictive-model-driven finance decisions noted in the abstract.
Women exhibit higher adoption and savings patterns on AI-enabled financial platforms.
Abstract reports gendered impacts derived from 1,500 semi-structured customer interviews plus account-activity data across the three case studies, noting higher adoption and savings for women.
AI-enabled platforms reduce vulnerability to climate-related income shocks.
Abstract claims findings that AI-enabled platforms reduce vulnerability to climate-related income shocks based on case studies (including smart agriculture finance in Thailand), interviews and transaction/loan data analysis.
AI-enabled platforms promote savings behavior among customers.
Abstract reports findings based on mixed-methods: qualitative interviews (1,500) and quantitative account-activity analysis indicating increased savings behavior on AI-enabled platforms.
AI-enabled platforms significantly improve credit access for low-income and rural customers in the case-study contexts.
Quantitative analysis of transaction records and loan repayment histories combined with qualitative insights from 1,500 interviews across three case studies (M-KOPA, TymeBank, and smart agriculture finance in Thailand) as described in the abstract.
Policymakers should pursue integrated policies linking energy transition, macroeconomic stability, and digital innovation to preserve the United States' technical supremacy in AI.
Normative recommendation based on the paper's empirical findings (WQR/WQC on 2013Q1–2024Q4 US data) showing links between energy policy, macro determinants, and AI investment.
Stable energy policy, continuous economic growth, and improved global integration are significant for promoting AI development in the United States.
Policy implication drawn from empirical associations found using WQR/WQC on US quarterly data (2013Q1–2024Q4), where renewable energy, growth, trade openness, and globalisation positively associate with AI investment and energy policy uncertainty exhibits nonlinear effects.
Wavelet Quantile Regression (WQR) and Wavelet Quantile Correlation (WQC) effectively capture distributional asymmetries and time–frequency dynamics in the relationships between macro/policy determinants and AI investment.
Methodological claim supported by the paper's use of WQR and WQC on the 2013Q1–2024Q4 US quarterly dataset; results are reported across quantiles and scales (as stated).
Globalisation positively influences AI investment in the United States.
Empirical analysis using WQR and WQC on US quarterly data from 2013Q1 to 2024Q4 (48 quarters).
Trade openness positively influences AI investment in the United States.
Empirical analysis using WQR and WQC on US quarterly data from 2013Q1 to 2024Q4 (48 quarters).
Economic growth positively influences AI investment in the United States.
Empirical analysis using WQR and WQC on US quarterly data from 2013Q1 to 2024Q4 (48 quarters).
Renewable energy consumption positively influences AI investment in the United States.
Empirical analysis using Wavelet Quantile Regression (WQR) and Wavelet Quantile Correlation (WQC) on US quarterly data from 2013Q1 to 2024Q4 (48 quarters).
AlphaFold represents an 'oracle' breakthrough in AI for scientific discovery.
Cited as an example of an algorithmic breakthrough that changed a specific scientific subtask (protein structure prediction). The paper frames AlphaFold as a milestone in the history reviewed; no new experimental data presented.
The resulting policy matrix includes R&D funding, regulatory sandboxes, public procurement incentives, and tax relief, tailored to each stage of technological evolution.
Paper presents a policy matrix produced by the study listing these instruments mapped to maturity stages; no quantitative evaluation of impact reported in text provided.
To validate and prioritise policy instruments, Delphi rounds with domain experts and Analytic Hierarchy Process (AHP) weighting are employed.
Paper reports use of Delphi method and AHP for validation and prioritization; methodological description without reported number of experts or rounds.
A technology maturity classification categorises innovations into emerging, developing, and mature stages, forming the basis for strategic policy matching.
Paper defines a maturity classification (emerging/developing/mature) and indicates it is used to match policy instruments; categorical description provided, no quantitative validation details in text provided.
Temporal mapping and citation networks reveal distinct technology maturity patterns, which are visualised using S-curve and hype cycle models.
Paper describes use of temporal mapping and citation network analysis and visualization via S-curve and hype cycle models; methodological description without quantitative sample-size details.
Technologies such as AI-driven healthcare, quantum communication, hydrogen energy, and smart educational AI are identified as key domains of convergence.
Paper reports these domains were identified via the applied analytic framework and multi-source data triangulation; no numeric counts/sample sizes provided.
The study applies advanced techniques such as LDA topic modelling, BERT-based clustering, and co-citation analysis to detect innovation trajectories.
Paper states these specific analytic techniques were applied (method description).
The research leverages large AI models and multi-source data—including global patent databases (WIPO, USPTO, Lens.org), scientific literature corpora, and industry intelligence platforms (CB Insights, Qichacha).
Paper statement of data sources and use of large AI models; methodological description (no sample sizes reported).
Recommended regulatory responses include algorithmic transparency mandates, mandatory mental health risk audits, participatory co-design, human review of deactivations, and minimum wage protections aligned with ILO principles.
Authors' policy recommendations derived from the review's synthesis and identified psychological risks.
Phase Three employs AI for comprehensive sensitivity analysis while humans provide strategic interpretation.
Descriptive claim about the third phase of the framework and its use in the paper's applied test; presented as the intended role split between AI (computational sensitivity tasks) and humans (interpretation).
Phase One leverages AI for rapid market research aggregation and preliminary pro forma generation.
Descriptive claim about the first phase of the proposed three-phase framework as presented in the paper; conceptual rather than a separate empirical finding.
The framework achieved seventy-one to ninety percent time reduction while maintaining analytical quality comparable to traditional methods.
Empirical result reported from the controlled ChatGPT-4 test on the single 150-unit scenario comparing time to complete underwriting tasks versus traditional methods.
This research develops and empirically validates a three-phase framework for AI-augmented multifamily underwriting through controlled testing with ChatGPT-4 using a standardized 150-unit development scenario in Seattle's Greenwood neighborhood.
Controlled testing described in paper: use of ChatGPT-4 on a single standardized 150-unit development scenario in Seattle Greenwood to evaluate the proposed three-phase framework.
Generative artificial intelligence demonstrates significant promise for efficiency gains across financial services.
Introductory assertion in paper; general statement about the potential of generative AI, not directly derived from the paper's controlled test.
Empirical findings demonstrate that digitalization significantly boosts efficiency and competitiveness of industrial production.
Correlation and regression analyses reported in the study linking digitalization measures to indicators of efficiency and competitiveness across levels of analysis.
Digital technologies (automation, IIoT, ERP systems, AI applications) reduce nonproductive costs, increase per-worker output, and improve the cost-efficiency of production in Kazakhstani enterprises.
Case studies and real examples from named enterprises (Asia Auto, Karaganda Foundry and Engineering Plant, Eurasian Resources Group) presented in the article.
The number of employees and working time have a positive but limited effect on labor productivity.
Results from the study's correlation and regression analysis comparing labor input measures (employee count and working time) with productivity outcomes.
Digitalization is the key driver of labor productivity growth in Kazakhstan.
Empirical correlation and regression analysis reported in the study across enterprise, industry, and national economy levels.
A stylized-facts analysis using OECD and World Bank indicators shows that economies with higher digital capacity, greater R&D intensity, and stronger institutions exhibit superior productivity and growth performance.
Stylized-facts (cross-country) analysis based on OECD and World Bank indicators; descriptive correlations reported in the paper (sample of countries not enumerated in the provided summary).
AI adoption stimulates institutional innovation, which in turn increases total factor productivity (TFP) and supports sustainable economic growth.
Theoretical mediation claim developed in the paper (integration of Schumpeterian growth theory with institutional economics); supported conceptually and argued with stylized-facts analysis but not presented as causally identified empirical estimates.
AI improves governance quality.
Argument within the conceptual framework linking AI capabilities (information processing, monitoring) to improved governance; stated qualitatively in the paper rather than supported by causal empirical tests.
AI lowers transaction costs.
Paper's conceptual/theoretical framework that characterizes AI as lowering transaction costs through improved information and coordination; no quantitative causal estimate reported.
AI reduces information asymmetries.
Theoretical/conceptual argument in the paper framing AI as a general-purpose technology that improves information flows; supported by the paper's conceptual framework (no experimental or causal identification reported).
These systems are now being widely used to produce software, conduct business activities, and automate everyday personal tasks.
Authors' statement describing observed applications and uses (policy/legal analysis; specific empirical data or sample size not provided in excerpt).
AI agents have entered the mainstream.
Authors' declarative statement based on their review of recent developments and observed uptake (policy/legal analysis in the paper). No empirical sample size reported in excerpt.
Opportunities arising from cyborg workflows include hyper-personalized narratives, democratized production, and ethical augmentation of underrepresented voices.
Forward-looking/interpretive claim in the paper describing potential benefits and opportunities; conceptual rather than empirically demonstrated in the excerpt.
Scalability is addressed via edge computing to support cyborg workflows.
Design/architectural claim in the paper mentioning edge computing as a scalability mechanism; no deployment-scale measurements reported in the excerpt.
The proposed workflows include robust bias mitigation strategies.
Paper asserts bias mitigation approaches are included and demonstrated in case studies; no quantitative fairness metrics or evaluation details provided in the excerpt.
Cyborg workflows produce enhanced creative output via iterative human–AI refinement.
Qualitative claim supported by case studies and examples presented in the paper (no quantitative creativity metrics or sample sizes reported in the excerpt).
Empirical evaluations validate 25-60% improvements in key metrics.
Paper states empirical evaluation results with a 25–60% improvement range; specific metrics, methods, and sample sizes are not provided in the excerpt.
Case studies in content generation, news curation, and immersive production demonstrate efficiency gains of up to 3x in throughput.
Reported results from unspecified case studies described in the paper; numeric claim provided but case study sample sizes and methodological details are not reported in the excerpt.