Evidence (4049 claims)
Adoption
5126 claims
Productivity
4409 claims
Governance
4049 claims
Human-AI Collaboration
2954 claims
Labor Markets
2432 claims
Org Design
2273 claims
Innovation
2215 claims
Skills & Training
1902 claims
Inequality
1286 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 369 | 105 | 58 | 432 | 972 |
| Governance & Regulation | 365 | 171 | 113 | 54 | 713 |
| Research Productivity | 229 | 95 | 33 | 294 | 655 |
| Organizational Efficiency | 354 | 82 | 58 | 34 | 531 |
| Technology Adoption Rate | 277 | 115 | 63 | 27 | 486 |
| Firm Productivity | 273 | 33 | 68 | 10 | 389 |
| AI Safety & Ethics | 112 | 177 | 43 | 24 | 358 |
| Output Quality | 228 | 61 | 23 | 25 | 337 |
| Market Structure | 105 | 118 | 81 | 14 | 323 |
| Decision Quality | 154 | 68 | 33 | 17 | 275 |
| Employment Level | 68 | 32 | 74 | 8 | 184 |
| Fiscal & Macroeconomic | 74 | 52 | 32 | 21 | 183 |
| Skill Acquisition | 85 | 31 | 38 | 9 | 163 |
| Firm Revenue | 96 | 30 | 22 | — | 148 |
| Innovation Output | 100 | 11 | 20 | 11 | 143 |
| Consumer Welfare | 66 | 29 | 35 | 7 | 137 |
| Regulatory Compliance | 51 | 61 | 13 | 3 | 128 |
| Inequality Measures | 24 | 66 | 31 | 4 | 125 |
| Task Allocation | 64 | 6 | 28 | 6 | 104 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 10 | 16 | 93 |
| Worker Satisfaction | 42 | 32 | 11 | 6 | 91 |
| Task Completion Time | 71 | 5 | 3 | 1 | 80 |
| Wages & Compensation | 38 | 13 | 19 | 4 | 74 |
| Team Performance | 41 | 8 | 15 | 7 | 72 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 17 | 15 | 9 | 5 | 46 |
| Job Displacement | 5 | 28 | 12 | — | 45 |
| Social Protection | 18 | 8 | 6 | 1 | 33 |
| Developer Productivity | 25 | 1 | 2 | 1 | 29 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 7 | 4 | 9 | — | 20 |
Governance
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Common barriers to ERM adoption in MSMEs include resource constraints and lack of expertise.
Findings from the literature review identifying determinants and barriers reported across studies (survey and qualitative studies commonly cited in such reviews); specific sample sizes/methods not provided in the summary.
MSMEs are particularly vulnerable to external shocks because of limited financial resources, weak internal controls, and heavy dependence on owner-managers’ intuition.
Background literature summarized in the review describing common structural and governance characteristics of MSMEs; drawn from multiple sources in the literature (specific studies not cited in the summary).
The article identifies and lays out several concerns regarding the government's approach to regulating AI.
Analytical critique presented in the paper (legal/policy analysis summarizing potential regulatory shortcomings). Based on the author's review and argumentation rather than primary empirical data.
Environmental regulations weaken the beneficial influence of generative AI on a company's ESG performance.
Moderation/interaction tests in the panel-data econometric model using measures of environmental regulation (on the same 2012–2024 Chinese A-share firm sample) showing a statistically significant negative interaction effect.
Gaps in infrastructure readiness, digital awareness, and inclusive policy frameworks hinder equitable AI adoption among micro‑enterprises.
Cross‑study synthesis of barriers identified across the 55 included articles; infrastructural, awareness, and policy barriers are explicitly reported as recurring themes.
Entrenched societal inequities imply that women and girls are often disproportionately held back from achieving their potential.
Broad claim referencing societal inequities and their effects on women and girls; stated in the introduction without specific empirical citations in the excerpt.
Significant challenges persist for AI-enhanced GS-BESS deployment, including limited data availability, poor model generalization, high computational requirements, scalability issues, and regulatory gaps.
Barriers and limitations identified across the literature as reported in this systematic review (PRISMA-based synthesis). The excerpt does not enumerate which studies reported each barrier or provide prevalence statistics.
A preregistered, nationally representative replication experiment in the United States (N = 1,200) replicates the causal finding that a labor-replacing (vs. labor-creating) AI frame reduces willingness to politically engage with future AI developments.
Preregistered randomized experiment (nationally representative US sample, N = 1,200) replicating the UK manipulation and measuring willingness to engage politically regarding AI.
A preregistered, nationally representative experiment in the United Kingdom (N = 1,202) shows that exposure to a labor-replacing (vs. labor-creating) AI frame causally reduces trust in democracy.
Preregistered randomized experiment (nationally representative UK sample, N = 1,202) manipulating AI framing (labor-replacing vs. labor-creating) and measuring trust/satisfaction with democratic institutions.
Large-scale survey data indicate that the public tends to view AI as labor-replacing rather than labor-creating.
Cross-sectional survey (N = 37,079 respondents across 38 European countries); descriptive analysis of responses about AI's labor market impact.
Only 12% of gig workers participate in retirement savings programs.
Survey and administrative measures of retirement-savings participation among gig workers in the 24-country sample.
Only 23% of gig workers report access to employer-provided health insurance.
Self-reported benefits coverage from labor force surveys and linked administrative records for gig workers across the 24 OECD countries (2015–2025).
The environmental footprint of healthcare systems is growing and persistent inequities in access and outcomes have intensified calls for procurement reform.
Contemporary literature review and synthesis of sector reports and studies documenting healthcare emissions/footprint and health inequities (no original empirical data reported in this paper).
There exists a systemic governance vacuum around GenAI, including gaps in privacy, accountability, and intellectual property protections.
Authors' synthesis of governance-related gaps reported across the 28 secondary studies and research agendas in the review.
Societal and ethical risks—such as bias, misuse, and skill erosion—constrain GenAI adoption.
Themes synthesized from the reviewed literature (28 papers) reporting societal and ethical concerns associated with GenAI deployment.
Technical unreliability—manifesting as hallucinations and performance drift—is a major constraint on GenAI adoption.
Recurring identification of technical reliability issues (hallucinations, performance drift) in the 28 reviewed papers and authors' aggregation of technical risks.
Adoption of GenAI is constrained by multiple interrelated challenges.
Cross-paper synthesis from the systematic review of 28 studies identifying recurring barriers and constraints reported in the literature.
Ongoing issues remain such as data access, model transparency, ethical concerns, and the varying relevance across Global North and Global South contexts.
Critical synthesis within the review drawing on discussions and critiques in the literature about barriers and ethical challenges; based on reported limitations and regional comparisons in reviewed studies (no numerical breakdown provided).
Human judgment is constrained by bounded rationality, cognitive biases, and information-processing limitations.
Cited as established findings from prior research across decision sciences and related fields (extensive literature evidence referenced; no new empirical data in this paper's abstract).
Key implementation challenges include data quality and integration, model interpretability, cybersecurity and privacy, regulatory/compliance uncertainty, skills gaps among accounting professionals, and implementation costs.
Identified by the paper through literature review and practitioner reports; these are presented as recurring barriers rather than quantified with a specific sample.
Many studies on serious-game DSTs are small-scale or experimental, and long-term impact data on behavioral change and emissions outcomes are sparse, limiting generalizability.
Review of the literature summarized in the chapter showing predominance of case studies, prototypes, and short-term evaluations rather than longitudinal or large-sample studies.
Ensuring scientific validity of game models, scaling co-design processes, measuring real-world behavioral change, and aligning incentives (policy/subsidies, markets) are remaining challenges to using serious games for DST uptake.
Chapter discussion of limitations and gaps identified in the reviewed literature; absence or sparsity of long-term validation studies and large-scale co-design implementations documented in existing research.
Current uptake of DSTs for net zero remains limited because of issues of trust, usability, lack of evidence linking actions to farm profitability, and poor integration into farmer workflows.
Literature synthesis, qualitative interviews and surveys, case studies documenting low adoption and barriers; multiple practice reports and studies cited in the chapter. Many studies report limited or uneven adoption across contexts.
Using LLM participants without rigorous validation can bias external validity and causal inference in economic research.
Review documents cognitive misalignments and distortions that can bias estimated behaviors, preferences, or treatment effects; authors highlight this as a risk.
Overfitting/contamination: LLMs can reproduce pre-training or fine-tuning data (stochastic parroting) and leak training-set content into outputs.
Multiple reviewed studies documenting examples of content reproduction and data leakage; categorized as overfitting/contamination in the review.
Misleading believability: LLM outputs may look plausible but be incorrect or unrepresentative, risking overconfidence in synthetic data.
Reported instances in the literature and organized failure taxonomy describing plausible-looking but inaccurate synthetic responses.
Distortions: LLM outputs can exhibit systematic biases relative to target human distributions.
Empirical findings across reviewed studies showing output distributions from LLMs that deviate from human sample distributions; aggregated in the distortions failure category.
Cognitive misalignments: LLMs differ from humans in reasoning, goals, and bounded rationality, which can alter behavior in economic and strategic tasks.
Multiple studies in the review reported systematic differences in reasoning and goal-directed behavior when comparing LLM outputs to human participants; coded under the cognitive misalignment category.
Major failure modes limiting synthetic participants as direct substitutes for humans are: cognitive misalignments, distortions, misleading believability, and overfitting/contamination.
Standardized taxonomy developed by coding the 182 studies into generalizable indicators and organizing failure types into four categories.
The information-theoretic uncertainty measure provides a mechanism-level explanation for why deception value falls as transparency increases (residual uncertainty explains utility changes).
Analytical linkage in the model connecting the entropy-like residual uncertainty metric to equilibrium utility changes; theoretical argument and derivation in the paper.
The value of deception degrades (falls) as the true system state becomes more observable; this degradation is quantifiable via the price-of-transparency metric.
Analytical definition of price of transparency as marginal change and supporting theoretical results; computational experiments that sweep observability/transparency levels (simulated experiments, parameter sweeps; number of scenarios not specified).
The paper derives closed-form bounds and break-even conditions that delineate when deception is ineffective due to cost or detectability.
Theoretical proofs and closed-form inequalities presented in the analytical section (derivations of bounds and break-even conditions).
If deployed without mitigation, GenAI CDS risks widening disparities by performing worse on underrepresented groups or being unequally distributed across resource-rich versus resource-poor settings.
Fairness literature, subgroup performance concerns, and distributional risk analysis cited in the paper; direct empirical demonstrations of widened disparities due to GenAI CDS are limited in the literature per the paper.
Limited public datasets and vendor lock-in constrain independent reproducible evaluations and audits of current generative models in healthcare.
Observation and policy analysis in the paper noting scarcity of public clinical datasets for state-of-the-art models and proprietary constraints; no dataset counts provided.
GenAI CDS creates data privacy and security risks because of high-value medical data and use of external cloud services.
Known cybersecurity risks and documented incidents in health IT; the paper cites the general risk context rather than specific breach sample counts tied to GenAI deployments.
GenAI CDS can amplify bias and inequities if training data underrepresent groups or reflect historical disparities.
Fairness and robustness audit literature and subgroup performance analyses referenced in the paper; specific empirical demonstrations for contemporary GenAI CDS are limited and sample sizes not given.
GenAI CDS systems hallucinate and can produce incorrect but plausible recommendations, which can cause patient harm if trusted unchecked.
Documented failure modes of generative models and examples from controlled evaluations; the paper references known hallucination behavior from model audits and case reports, though it does not quantify incidence rates or provide large-scale observational harm data.
Reproducibility and deployment gaps are widespread: missing code, inconsistent benchmarks, and insufficient productionization focus (monitoring, model updates, rollback).
Surveyed literature often lacks released code and consistent benchmarks; thematic analysis highlights absence of operational deployment practices.
Common ML pipeline pitfalls include overfitting, poor cross-validation practices, lack of real-time/online evaluation, and inadequate feature engineering.
Critical assessment of experimental practices in the surveyed literature identifying methodological shortcomings that can inflate reported performance.
There is a lack of large, labeled, realistic IoT datasets; class imbalance, concept drift, dataset bias, and synthetic datasets that poorly reflect real traffic are common problems.
Review of datasets (N-BaIoT, Bot-IoT, TON_IoT, UNSW-NB15, KDD variants, custom/synthetic datasets) and critical assessment of their limitations across studies.
Resource constraints (limited CPU, memory, energy, and network bandwidth on devices and edge nodes) significantly limit feasible ML model complexity and deployment choices.
Multiple surveyed studies report hardware constraints and evaluate runtime/memory/latency; survey synthesizes these resource limitations as a recurring challenge.
Despite high reported detection accuracies in academic work, there is a shortage of production-grade, deployable ML-IDS for IoT.
Critical review of surveyed papers showing many report lab metrics but few report deployment case studies, production rollouts, or provide deployment artifacts (code, runtime/energy measurements).
Limitations of the review include restricted sample size, Scopus-only coverage, emergent-literature timeframe, and heterogeneity in study designs and measures, which constrain generalizability.
Authors' limitations subsection explicitly listing these constraints from their SLR process.
There has been insufficient attention in the literature to ethics, fairness, and consumer welfare in algorithmic pricing.
Persistent gap identified in the SLR—few or no included studies focused on ethics/fairness/welfare issues according to authors' coding.
Existing empirical studies on digital VBP exhibit methodological limitations, including small/limited samples, short time windows, and inconsistent measures.
Authors' methodological critique from the SLR based on assessment of study designs and measures reported in the 30 articles.
Automated compliance and credentialing systems raise governance issues (auditability, appeals mechanisms) and risk incorrect automated deregistration if not properly governed.
Governance and algorithmic-risk discussion in the paper; logical argumentation rather than case-based evidence.
The paper models career progression as a continuous function and treats certification gaps as discontinuities that impede labour-market mobility.
Mathematical/conceptual modeling described in the methods (career-progression-as-continuous-function approach); this is a modeling choice reported in the paper rather than an empirical finding.
There is limited long-term impact evidence and few system-level assessments of AI in developing-country agriculture.
Authors' methodological caveat based on the temporal scope and types of studies available in the >60-study review.
The evidence base is skewed toward pilots and high‑performer contexts; there is a lack of long‑panel, multi‑project longitudinal studies to validate typical returns and scalability.
Authors' assessment of evidence types in the 160 studies: mix of conceptual papers, case studies, pilots, and only limited larger empirical evaluations.
Substantial compute and resource requirements for training and inference concentrate capabilities among well‑resourced labs and firms.
Paper discusses large compute budgets for training/inference and states that performance scales with data, model size, and compute; it infers concentration of capabilities but provides no empirical market concentration measures.