Evidence (2066 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 |
Inequality
Remove filter
Priority research areas include evaluating long‑run distributional impacts of AI diffusion in agriculture, interactions between digital technologies and labor markets, inclusive financing models for adoption, and macroeconomic effects on food prices and trade.
Stated research agenda and gap analysis in the paper’s conclusions, derived from the review of existing literature and identified gaps.
The current evidence base has gaps: more rigorous impact evaluations, long‑term soil and emissions accounting, and studies on distributional outcomes are needed.
Meta‑assessment within the paper noting limitations of existing literature (many short‑term pilots, limited long‑run soil/emissions data, few studies on who captures value); the claim is based on the review's appraisal of methods used in cited studies.
Economists and policymakers should fund long‑run evaluations (RCTs, quasi‑experimental designs) to estimate causal effects of AI interventions on productivity, welfare, and environmental outcomes.
Evidence‑gap analysis and policy recommendations in the paper; explicit call for rigorous impact evaluation methods given current paucity of long‑run causal evidence.
There are limited long‑run randomized controlled trials (RCTs) on AI/IoT impacts for smallholders and scarce cross‑country data on distributional effects.
Literature review and evidence‑gap identification within the study; explicit statement that long‑run RCTs and cross‑country distributional data are scarce.
Heterogeneous contexts mean impacts vary; careful piloting, monitoring, and adaptive policy are necessary to manage uncertainty in outcomes.
Synthesis and explicit discussion of uncertainties; evidence gaps section noting variable results across regions and interventions.
This paper is a narrative review synthesizing heterogeneous studies and case reports rather than providing meta-analytic estimates of effect sizes.
Methods statement in the paper describing review type as narrative synthesis and noting limitations (no meta-analysis).
Measurement and research gaps (data scarcity, informality) complicate robust economic assessment of AI impacts; improved metrics, granular labour and firm‑level data, and mixed‑methods evaluation are required.
Methodological critique based on reviewed literature and identified gaps; no new data collection in the paper.
There is a need for causal, longitudinal studies on how AI‑enabled fintech affects women's portfolio outcomes and on algorithmic interventions designed to reduce gender gaps.
Explicit statement in the paper noting limitations of existing literature (heterogeneity, limited longitudinal causal evidence, possible platform sample selection).
Child-specific surveillance across human, animal, and environmental domains is sparse, limiting understanding of pediatric One Health risks.
Authors' methodological assessment based on literature search and review; explicit limitation stated that standardized child-focused surveillance data are lacking and heterogeneous across sectors.
The legal arguments create some uncertainty about scope and enforcement timelines; economic actors will respond to expected enforcement probabilities and expected sanctions, so clarity from regulators or courts will shape the ultimate economic effects.
Doctrinal acknowledgement of legal uncertainty combined with standard economic modeling of regulatory expectations; no empirical modeling in the Article.
The paper is primarily legal/policy scholarship rather than an empirical assessment of the prevalence or magnitude of discrimination in EdTech; it does not provide econometric estimates of harm.
Explicit limitation noted in the Article (self‑reported).
The Article's evidence consists of illustrative case law and statutory text rather than empirical datasets; it builds doctrinal chains, hypotheticals, and applications of statutory language to modern procurement and EdTech deployment models.
Explicit description of evidence and limits in the Article (self‑reported).
Methodologically, the paper uses doctrinal legal analysis and policy argumentation — close reading of federal civil‑rights statutes, administrative guidance, and judicial decisions interpreting 'recipient' and 'federal financial assistance.'
Explicit methodological statement in the Article (self‑reported).
The legal argument is grounded in statutory interpretation and precedent about the scope of 'recipient' and how federal financial assistance flows and influence should be understood.
Doctrinal analysis of statutes, administrative guidance, and judicial decisions cited and discussed in the Article.
Empirical validation of the book’s proposals would require complementary case studies, model documentation, and outcome measurements.
Author/reviewer recommendation in the blurb about methodological limitations and next steps; not an empirical finding.
The book is predominantly conceptual and policy-analytic and uses illustrative case vignettes rather than presenting a single empirical study.
Explicit methodological description in the Data & Methods blurb: synthesis of technical ideas, governance requirements, and illustrative vignettes; no empirical sample or experimental protocol described.
The research program is grounded in 12 years of forensic legal research spanning 2014–2026.
Author-stated research timeline and methodology (2014–2026 forensic legal research).
The protocol is underpinned by a forensic audit of approximately 4,200 specialized texts (legal doctrine, regulation, standards, technical literature).
Stated corpus and audit in the Methods section: ~4,200 texts reviewed as part of the forensic audit.
The protocol systematizes arguments for 16 projected rulings at Mexico’s Supreme Court (SCJN) to anchor the proposed rights and rules in constitutional practice.
Doctrinal projection and constitutional strategy section of the compendium describing 16 projected SCJN rulings (method: legal projection/modeling).
The compendium’s findings and recommendations are based on a forensic audit of approximately 4,200 specialized texts covering doctrine, jurisprudence, regulation and technical literature.
Stated methodological claim in the compendium: forensic corpus audit of ~4,200 texts (sample size reported).
Limitations of the review include the small sample of studies, uneven geographic coverage, heterogeneity in methods across studies, and limited long‑run evidence (especially on generative AI), which complicate causal aggregation.
Author-reported limitations based on the meta-assessment of the 17 included studies (variation in methods, contexts, and time horizons).
Design of this work: a systematic literature review and meta‑synthesis of empirical findings from peer‑reviewed journals (2020–2025), based on 17 publications.
Stated methods and inclusion criteria of the paper: systematic review of peer‑reviewed literature (sample = 17).
Long-term evidence on generative AI’s structural labor‑market effects is scarce; few longitudinal studies exist.
Assessment of study horizons and methods among the 17 papers indicates limited long-run and longitudinal analyses specifically on generative AI impacts.
Empirical coverage is limited for low‑income countries; evidence from such settings is scarce.
Geographic distribution of the 17 reviewed studies shows concentration in advanced economies with few or no studies focused on low-income countries.
The literature shows a surge in research activity on AI and labor markets in 2023–2025 and a concentration of studies in advanced economies.
Meta-analytic summary of the publication years and geographic focus among the 17 selected publications (temporal and geographic count of included studies).
Results depend on accurate skill extraction from vacancy texts and valid measures of occupational exposure/complementarity; causal interpretation of diffusion effects may be limited by endogeneity (e.g., technology adoption responding to labor-market conditions).
Authors' stated methodological limitations: reliance on text-analysis identification of skills and on constructed measures of exposure/complementarity; acknowledgement of endogeneity concerns limiting causal claims.
The paper proposes two conceptual models (AI/ML‑Driven Labor Market Transformation Model and Sectoral Impact and Resilience Model) to organize heterogeneous findings and generate testable hypotheses about how AI reshapes labor across sectors and skill levels.
Conceptual synthesis integrating Technological Determinism, Socio‑Technical Systems Theory (STS), and Skill‑Biased Technological Change (SBTC); the models are theoretical outputs of the review used to map mechanisms and heterogeneity rather than empirical findings.
There are substantial measurement and identification gaps in the literature: heterogeneity in measuring 'AI adoption', limited long‑run causal evidence, and geographic bias toward advanced economies.
Methodological assessment within the review noting variability across studies in AI measures (patents, investment, task exposure proxies), paucity of long‑run causal designs, and concentration of empirical studies in advanced economies; this is a meta‑evidence limitation statement.
Quasi-experimental designs (difference-in-differences, instrumental variables, event studies) and panel regressions are useful methods for identifying causal effects of AI adoption where plausibly exogenous variation exists.
Methodological summary in the paper listing common empirical strategies used in the literature to estimate causal impacts of technology adoption.
Current research is limited by measurement challenges in capturing AI capabilities and firm-level adoption, and by a lack of longitudinal worker-firm data and causal identification in many settings.
Explicit limitations noted by the paper: gaps in task measures, scarce longitudinal linked datasets, and methodological challenges in causal inference.
This paper's approach is qualitative and based on secondary literature synthesis; it does not collect primary survey, experimental, or administrative data.
Explicit statement in the Data & Methods section of the paper.
Key empirical gaps remain: better measurement of K_T (AI/software capital), more granular matched employer‑employee and wealth data, and improved estimates of task-substitution elasticities are required to precisely quantify incidence and policy impacts.
Authors’ stated research agenda and limitations section, including sensitivity analyses showing outcome variation with parameter choices and measurement uncertainty.
Models are prompted to assess profiles along dimensions of social acceptance, marital stability, and cultural compatibility.
Experimental procedure: prompts asked models to rate profiles on the three named dimensions.
We evaluate five LLM families (GPT, Gemini, Llama, Qwen, and BharatGPT).
Methods: models enumerated as the LLM families evaluated in the audit.
We vary caste identity across Brahmin, Kshatriya, Vaishya, Shudra, and Dalit, and income across five buckets.
Experimental design described: caste identity explicitly manipulated across five named caste categories; income varied across five buckets.
We conduct a controlled audit of caste bias in LLM-mediated matchmaking evaluations using real-world matrimonial profiles.
Described methodology in the paper: a controlled audit using real-world matrimonial profiles to probe LLMs for caste bias.
Research should prioritise longitudinal and theory-informed evaluations, including intersectionality-informed analyses, and assess downstream impacts on women’s career trajectories alongside robust governance and accountability practices.
Authors' recommendations based on identified gaps from the scoping review.
Using inductive thematic analysis, we identified three functional domains: (1) bias mitigation and representation, (2) skills development and empowerment and (3) career pathways and retention.
Authors' thematic analysis of the 13 empirical studies included in the scoping review.
Artificial intelligence (AI) is increasingly integrated into career guidance and organisational decision systems.
Statement in abstract indicating observed trend; supported by literature search contextualising the review (scoping review using PRISMA-ScR).
To foster more equitable outcomes, platform governance should be gender‑responsive, including algorithmic transparency, inclusive system design, and extension of core labor protections to gig workers.
Practical implications stated in the paper arising from the literature synthesis and feminist political economy framing.
AI‑enabled platforms can expand income opportunities and flexibility for women.
Thematic synthesis of findings across the 48 reviewed studies; reported in the paper's Findings as one side of a central paradox.
Generative AI is being used for automation of tax compliance.
Listed in the abstract as an illustrative example of algorithmic application to international tax (generative AI for automating tax compliance); no empirical measurement reported in the abstract.
Blockchains are being used for instant trade verification in international tax contexts.
Presented in the abstract as one of three illustrative examples of how algorithmic technologies are being used for international tax purposes; no empirical details provided in the abstract.
It empowers owners of data and code.
Explicit claim in the abstract asserting a power shift toward those who own data and code; presented as a conceptual conclusion from the authors' reflection and examples.
Global professional service firms are actively developing TaxTech to capture this market.
Direct statement in the abstract indicating market activity by global professional service firms; presented as an observed trend rather than supported by reported empirical data in the abstract.
Technological leaps in the algorithmic processing of information are providing financial actors with new opportunities for transnational financial and legal management that optimize asset allocation.
Stated as a conceptual observation in the paper's abstract; no empirical sample, presented as a general claim about technological change and its opportunities for financial actors.
Improvements in skill adaptability reduce the risk of automation substitution.
Analysis linking measures of skill adaptability to lower estimated risk/impact of occupational automation exposure in the CFPS-based models.
Vocational education background and participation in on-the-job training can mitigate the negative effects of technological shocks on wages.
Interaction analyses in the CFPS-based regressions showing that vocational education and on-the-job training attenuate the estimated negative impact of automation exposure on wages.
Technological shocks significantly widen the skill wage gap.
Empirical analysis using the CFPS panel and the occupational task automation exposure index; paper reports statistically significant estimated effect of automation exposure on the skill wage gap.
The article proposes a Strategic Action Framework to support more inclusive and context-responsive AI ecosystems.
Policy recommendation/framework presented by the authors as a conclusion; not empirically evaluated within the study.