Evidence (3308 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).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
Filter claims →
Productivity
8807 claims
Filter claims →
Governance
7870 claims
Filter claims →
Human-AI Collaboration
7560 claims
Filter claims →
Org Design
4892 claims
Filter claims →
Innovation
4781 claims
Filter claims →
Labor Markets
4004 claims
Filter claims →
Skills & Training
3308 claims
Filtered →
Inequality
2332 claims
Filter claims →
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 | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Skills Training
Remove filter
Distinguishing automation versus augmentation using causal methods changes policy responses (e.g., income support versus reskilling).
Policy implication drawn from conceptual separation of substitution and complementarity effects; logical inference rather than empirical demonstration in the paper.
Methodological caveats across the literature (heterogeneity of tasks/measures, publication bias, short-term studies) limit the generalizability of current findings.
Meta-level critique within the synthesis noting study heterogeneity, likely publication/short-term biases, and variable domain-specific performance dependent on user expertise and workflows.
Standard productivity metrics are likely to undercount the value generated by AI-augmented ideation; quality-adjusted measures of creative output are required.
Measurement critique based on the mismatch between existing productivity statistics and the kinds of upstream idea-generation gains observed in empirical studies; supported by the review's methodological discussion.
Evaluation of the equivalency system should use metrics such as concordance between claimed competencies and verified inputs, predictive validity versus labor-market integration outcomes, and false positive/negative rates in automated decisions.
Methodological recommendation in the paper outlining specific evaluation metrics; this is a prescriptive claim (no empirical implementation reported).
Results and implications are limited by the sample and context: evidence comes from law students on a single issue-spotting exam using one brief training intervention, so generalizability to experienced professionals, other tasks, or other models is untested.
Authors’ reported sample (164 law students) and explicit caution about generalizability in the study summary; the intervention and outcome are specific to one exam and one ~10-minute training.
Some mechanism-specific estimates are imprecise due to the sample size; confidence intervals for those estimates are wide.
Authors report wide confidence intervals for mechanism decomposition (principal stratification) results based on the randomized sample of 164 students.
There is no consensus in the literature on net job effects — studies diverge on whether AI produces net job gains.
Direct finding from the review: the 17 peer‑reviewed studies produce heterogeneous results on net employment impacts (some positive, some negative, some neutral).
Effects of AI adoption are heterogeneous across industries, firm sizes, regions, and worker characteristics (education, experience, occupation).
Microdata and firm-level studies exploiting cross-sectional and panel variation, quasi-experimental designs leveraging differential adoption across firms/regions, and comparative institutional analyses showing variation by context.
The effects of K_T adoption are heterogeneous across industries, firms, countries, and cohorts — early adopters and capital-rich firms/countries gain most — implying important transition dynamics for political economy.
Cross-country comparisons, industry- and firm-level panel heterogeneity analyses, and case studies demonstrating variation in adoption timing and gains; model simulations emphasizing transition path dependence.
Aggregate productivity (output per worker or per unit of inputs) can rise while labor’s share and employment decline due to substitution toward K_T.
Macro growth-accounting exercises decomposing output growth into contributions from labor, traditional capital, and technological capital; model simulations showing productivity gains coexisting with falling labor shares under substitution elasticities.
There are critical gaps in governance mechanisms that are tuned to the scale of SME deployment of BI and AI.
Conclusion drawn in the narrative review of literature (2020–2025); no specific policy evaluations or sample sizes cited in the excerpt.
SMEs face unequal/fairness issues in access to AI and there are biases in algorithms affecting SME deployment.
Identified as a key gap across the peer‑reviewed literature (2020–2025) in the review; the excerpt provides no quantitative measures or specific studies.
There are critical gaps in data literacy among SME personnel.
Reported as a recurring theme in the reviewed literature (2020–2025) in the narrative review; no numeric prevalence or sample sizes provided in the excerpt.
This structural under‑serving of SMEs by advanced BI and analytics is threatening inclusive economic growth and resiliency.
Argument presented in the review synthesizing literature (2020–2025); no quantified causal estimates or sample sizes provided in the excerpt.
SMEs are systematically under-served by advanced business intelligence (BI) and predictive analytics infrastructure.
Narrative synthesis of peer‑reviewed literature (2020–2025) reported in the review; no specific studies or sample sizes given in the excerpt.
These factors (surveillance anxiety, loss of autonomy, deskilling) negatively affect worker well-being and contribute to turnover.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). The paper synthesizes prior empirical and theoretical studies but does not report an original sample size.
Automation and algorithmic systems introduce risks of deskilling that affect workers' capabilities.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). No primary sample size stated.
Algorithmic management reduces worker autonomy (loss of autonomy) in warehouse settings.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). Sample sizes not reported in this paper.
Algorithmic management in automated logistics generates surveillance anxiety among workers.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). No sample size given.
The paper formalises an AI productivity transmission gap between technical adoption and inclusive productivity realisation.
Formal definition and derivation within the DIAC theoretical framework (analytical/modeling content).
AI does not translate directly from firm-level task efficiency into national productivity; its effect is filtered through complementary intangible investment, skills formation, data governance, competition policy, labor-market mobility, and social insurance.
Analytical DIAC model and accompanying theoretical argumentation in the paper; no empirical sample reported.
AI use can reduce visibility of real skill differences among employees.
Reported findings from performance management and knowledge-work studies indicating that AI-mediated outputs can obscure underlying employee skill variation.
Use of AI can produce over-reliance on AI recommendations, reducing active human judgment and accountability.
Cited empirical observations and prior literature on automation bias and AI-supported decision processes in organizational settings.
AI systems miss contextual information that humans use to make better decisions.
Examples and studies cited from hiring, performance management, healthcare, and knowledge work demonstrating omissions of context by AI tools.
Empirical studies of AI use show recurring problems including mistakes in unusual cases.
Cited recent studies across domains (hiring, performance management, healthcare, knowledge work) reporting AI errors on atypical or edge-case instances.
Human judgment rooted in experience cannot be fully replaced by current AI systems.
Argument based on literature synthesis drawing on cognitive science, neuroscience, and organizational studies; supported by cited recent empirical studies of AI use in hiring, performance management, healthcare, and knowledge work (no single new experiment reported).
The paper identifies an emergent phenomenon called 'Precariousness 2.0' — a state of manufactured uncertainty characterized by loss of professional autonomy and chronic anxiety among workers.
Conceptual/qualitative construct developed in the paper from synthesis of secondary reports and national observations; no primary survey data cited supporting prevalence or magnitude.
Women in high-income countries face a risk of automation nearly three times higher than men due to their concentration in administrative roles.
Paper's secondary quantitative synthesis attributing a ~3x relative risk to occupational gender segregation (administrative roles); based on international report data referenced in the study.
39% of current skills become obsolete.
Reported statistic in the paper synthesizing projections from the cited reports (WEF, ILO, McKinsey, PwC); no primary sample size stated.
22% of employment undergoes structural change (masking the net job gain).
Reported summary statistic from the paper's secondary quantitative analysis of international reports; no primary sample size stated.
Public discourse still focuses heavily on job losses while paying less attention to the opportunities that AI creates.
Author's observation/argument in the paper (qualitative commentary comparing public discourse emphasis).
The report identifies 'AI washing,' a practice in which companies mention AI as justification for what are really financially motivated layoffs.
Identification/term introduced in the paper based on examples or synthesis of corporate reporting and layoff cases (as described).
Roughly 92 million jobs might face displacement by 2030.
Projection synthesized from cited external reports (WEF/PwC/MGI/Gartner/IMF) as reported in the paper.
Many participants used the model to rubber-stamp a prior guess and, as a result, performed worse than the model alone.
Pilot analysis comparing hybrid forecasts to model-alone forecasts and observing a subset whose forecasting error exceeded the model's error; described qualitatively in the paper.
The relational value of workplace AI companions remains underexplored.
Claim motivated by the authors' systematic interdisciplinary literature review (paper states relational value is underexplored); no numeric count of studies provided in excerpt.
The existing international competency indices fail to capture the structural differentiation in AI-driven educational transformation across EU moderate innovator economies, rendering evidence-based policy design inadequate.
Stated as a motivating assertion in the paper; based on the author's critique of existing indices and the subsequent focused evaluation of selected EU moderate innovator economies (Visegrad and Baltic states). No specific quantitative comparison of indices is reported in the abstract.
The paper reframes humans not as passive users, but as core system components whose competencies, limitations, and adaptive capacities constrain the performance envelope of optimized AI systems.
Framing/interpretive claim derived from the paper's perspective and literature synthesis (conceptual; no empirical support provided in text).
Organizational structures, bias susceptibility, retraining constraints, and interface design co-determine system stability, error propagation, and optimization ceilings.
Conceptual claim based on synthesis of literature across organizational adoption and ML lifecycle management (no empirical tests or sample sizes reported).
Human interfaces define throughput limits in areas such as prompt engineering, data-stream curation, adjudication of model outputs, and the orchestration of hybrid automation workflows including robotics, scraping, and digitization.
Theoretical assertion supported by the paper's systems-oriented analysis and literature synthesis (no empirical measurement or sample size provided).
Despite accelerating advances in AI capabilities, human capital remains the enduring and dominant system constraint.
Argument and synthesis of emerging research across human-AI interaction, ML lifecycle management, organizational adoption, and adult learning theory (conceptual synthesis; no empirical sample size reported).
Diagnostic heuristic: if letting AI in makes the task feel effortless, it is in the wrong place.
Authors' heuristic for educators (conceptual guidance; no empirical test reported in the excerpt).
An unguarded AI helper left high-school students about 17% worse on an unaided exam than peers with no tool at all.
Described as the 'strongest causal evidence' in the paper; empirical study of high-school students measuring unaided exam performance. (Study design details and sample size not provided in the excerpt.)
Used poorly, AI replaces the cognitive work that learning requires and leaves an illusion of learning: a confident sense of mastery that collapses on the unaided task.
Authors' conceptual claim supported in the paper by reference to causal evidence (see following empirical claims); no sample size given in the excerpt.
Experts in the study assign a 14% probability to 'rapid-progress' scenarios characterized by substantial GDP growth, declining labor force participation, and accelerating wealth inequality.
Result from the 2025 forecasting study of experts (69 economists + 52 AI experts), reporting a probability estimate (14%) for a named scenario with specified macroeconomic and labor-market features.
Developed economies leverage educational capital to mitigate the adverse inequality effects of AI adoption.
Reported interaction/moderation findings from OLS and Random Forest analyses on the World Bank/OECD dataset showing weaker or offset association between AI adoption and Gini in higher-education / higher-development country groups.
Barriers limiting full AI adoption in auditing include resistance to change, algorithm aversion, heuristics and biases, lack of transparency, expertise and training gaps, and technological complexity.
Systematic Literature Review (SLR) of 43 studies synthesizing reported inhibitors and challenges to AI uptake in auditing from empirical and conceptual papers.
GenAI adoption carries risks including overreliance on models, misalignment between model outputs and human needs, and uneven performance across tasks and contexts.
Reported adverse effects and risks identified in the reviewed literature (task-level experiments and applied studies summarized by the paper).
AI has a significant negative influence on value chain upgrading in labor-intensive equipment manufacturing industries.
Industry-type heterogeneity analysis within the same 30-province panel (2010–2022) showing a statistically significant negative coefficient for labor-intensive subsectors.
In manual jobs, AI compresses the returns to undereducation as tasks become more skill-intensive.
Occupation-specific heterogeneity analysis using CLDS and city AI diffusion showing reductions in the undereducation wage premium within manual-occupation subsamples under higher AI diffusion.
AI diffusion slightly lowers the wage premium for undereducated workers.
Interaction effects from fixed-effects models using CLDS and city AI diffusion indicators showing a small reduction in undereducation-related wage premium with higher AI diffusion.