Evidence (3566 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 |
Labor Markets
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Despite high overall employment (80% for ages 25–54), nurseries reported they were prevented from hiring new workers due to high wages and unqualified workers.
Reported responses from nurseries (survey/industry responses) referenced in the paper; sample size and survey details not provided in the excerpt.
The US nursery industry faces a labor deficit.
Statement in the paper based on industry reporting; specific methodology or sample size not provided in the excerpt.
Selection of a human-LLM archetype brings important risks and considerations for the designers of human-AI decision-making systems.
Analytic discussion and synthesis of evaluation results and literature review; tradeoffs surfaced in the paper (e.g., decision control, social hierarchies, cognitive forcing strategies, information requirements).
Gendered perceptions of AI's social and ethical consequences, rather than access or capability, are the primary drivers of unequal GenAI adoption.
Comparative model results from the 2023–2024 nationally representative UK survey showing perceptions (societal-risk index) have greater explanatory/predictive power than measures of access (e.g., device/internet access) or capability (digital literacy, education).
Intersectional analyses show the largest gender disparities in GenAI use arise among younger, digitally fluent individuals with high societal risk concerns, where gender gaps in personal use exceed 45 percentage points.
Subgroup (intersectional) analysis of the nationally representative 2023–2024 UK survey data stratified by age, digital fluency, and societal-risk concern levels; reported gender gap >45 percentage points in specified subgroup.
The societal-risk concerns index ranks among the strongest predictors of GenAI adoption for women across all age groups, surpassing digital literacy and education for young women.
Multivariable models and predictor ranking using the 2023–2024 UK survey data showing relative predictive strength of the concerns index versus measures of digital literacy and education, with subgroup (age × gender) comparisons.
The societal-risk concerns index explains between 9 and 18 percent of the variation in GenAI adoption.
Regression/statistical models using the composite concerns index as a predictor of GenAI adoption in the nationally representative 2023–2024 UK survey; reported explained variation (9–18%).
Women adopt GenAI less often than men because they perceive its societal risks differently.
Statistical analysis linking a constructed composite societal-risk concerns index (mental health, privacy, climate impact, labor market disruption) to GenAI adoption, using the UK 2023–2024 survey; models compare explanatory power of perceptions versus access/capability variables.
Women adopt GenAI substantially less often than men.
Analysis of the 2023–2024 nationally representative UK survey data comparing personal use/adoption rates by gender.
Across survey and experimental evidence, perceptions that AI will replace labor—regardless of actual labor-market outcomes—may decrease democratic legitimacy and public engagement in shaping AI's future.
Synthesis of correlational findings from the large European survey (N = 37,079) and causal evidence from two preregistered experiments (UK N = 1,202; US N = 1,200).
Controlling for technology-related, political, and sociodemographic factors, perceiving AI as labor-replacing (vs. labor-creating) is associated with lower political engagement with technology.
Multivariable regression analyses on the large European survey (N = 37,079) with controls for technology-related, political, and sociodemographic factors.
Controlling for technology-related, political, and sociodemographic factors, perceiving AI as labor-replacing (vs. labor-creating) is associated with lower satisfaction with democracy.
Multivariable regression analyses on the same large survey (N = 37,079) including controls for technology-related attitudes, political variables, and sociodemographic covariates.
There are ethical concerns surrounding AI and automation including algorithmic decision-making, workforce exclusion, and inequality in access to reskilling opportunities.
Raised as an ethical analysis within the paper's conceptual framework; no empirical study, surveys, or quantified measures of these ethical issues are reported in this paper.
AI is eliminating repeated (routine) jobs.
Stated as part of the paper's argument about AI's dual impact; supported by conceptual analysis rather than new empirical evidence in this manuscript (no sample size or empirical method reported).
Artificial intelligence and automation are reshaping jobs, transforming them from a steady source of income to a dynamic process highly influenced by technology, flexibility, and uncertainty.
Central analytical claim made in the paper based on conceptual reasoning; the paper does not report empirical measures, datasets, or sample sizes to support the transformation quantitatively.
AI and automation pose significant challenges to employment stability, skill relevance, and human dignity.
Claim presented within the paper's conceptual and analytical discussion of AI's dual impacts; no empirical study, sample size, or quantitative measures provided in this paper.
Jurisdictions that implemented employee classification requirements experienced an 18% reduction in platform labor supply.
Comparative policy analysis across jurisdictions within the 24-country dataset comparing platform labor supply before and after employee-classification reforms using administrative and platform transaction records.
Median gig-worker hourly pay ($14.20) is approximately 22% below comparable traditional employment wages.
Comparison of adjusted median hourly gig earnings (platform records) to comparable hourly wages in traditional employment from labor force and administrative wage data for the same populations across the 24 countries.
There are challenges to adopting AI in HRM within IT firms.
Identified through the literature review and the empirical study involving HR professionals; the summary notes challenges but does not enumerate or quantify them.
AI use also poses risks, including systemic discrimination, privacy invasion, and commodification of talent.
Qualitative synthesis and documented instances in the reviewed literature (n=85) reporting discriminatory outcomes, privacy concerns, and labor commodification effects associated with algorithmic HR tools.
Qualitative synthesis reveals a 'gray zone' in labor relations and a 'black box' in algorithmic data processing, both exposing businesses to procedural injustice risks.
Thematic/qualitative synthesis of findings from the reviewed literature (n=85) highlighting issues of labor relations and algorithmic opacity leading to procedural fairness concerns.
Digital transformation raises challenges related to privacy, inequality, and regulatory scrutiny.
Identified as a key challenge in the paper; the abstract provides no details on how privacy concerns, inequality measures, or regulatory incidents were documented or quantified.
Evidence strength is inversely correlated with intervention complexity.
Cross-domain synthesis reported in the paper that formalises an inverse evidence–complexity relationship based on the reviewed literature. The abstract does not quantify the correlation or list the domains/intervention types used to derive it.
Per-capita elderly care costs running 3–5 times those of working-age cohorts.
Cost comparisons reported in sources included in the 81-paper review. The abstract reports a 3–5x multiple but does not specify which cost categories, countries, or methodological adjustments were used.
Conventional policy instruments have failed to resolve pressures that include severe long-term care workforce shortfalls across leading ageing economies.
Synthesis of findings from the structured narrative review of 81 sources (2020–2025) indicating persistent workforce shortfalls. The abstract does not provide quantitative workforce shortfall magnitudes or country-specific data.
Demographic ageing is projected to reduce annual GDP growth by 0.3–1.2 percentage points by 2035.
Projection estimates referenced in the review literature (2020–2025). The abstract reports the 0.3–1.2 p.p. range but does not specify which models or studies generated these projections.
Ageing-related expenditure already absorbs up to 18% of GDP in the most affected economies.
Spending estimates drawn from the reviewed literature (2020–2025). The paper states 'up to 18% of GDP' for the most affected economies but does not list which economies or the original data sources in the abstract.
Advanced economies face a compounding demographic crisis: populations aged 65 and over will reach 30–40% in several nations by 2050.
Demographic projection claims cited in the paper's background literature (sources from the structured narrative review). No specific datasets or country-by-country breakdown provided in the abstract.
Traditional methods for assessing and developing employees' skills often fail to provide real-time feedback.
Statement supported by literature review cited by the authors; the abstract does not provide empirical comparisons, metrics, or sample sizes.
Existing research on AI-driven decision-making remains fragmented and often framed through substitution-oriented narratives that position AI as a replacement for human judgment.
Assessment based on the author's interdisciplinary literature synthesis (conceptual meta-analysis); descriptive evaluation of research framing rather than new empirical testing.
Skills mismatch and SME adoption constraints constitute a binding bottleneck for inclusive digital–green upgrading.
Synthesis of studies on skills, firm capabilities, and SME adoption of digital and green technologies (review-level evidence; no single dataset or sample size provided).
Absent complementary institutions and infrastructure, digitalization may increase electricity demand, widen inequality, and incentivize strategic disclosure (greenwashing).
Literature review drawing on empirical studies of energy consumption from digital systems, labor-market studies, and analyses of ESG disclosure practices (review-level synthesis; no single sample size reported).
More experienced translators appear more likely to exit the market after ChatGPT’s launch than less experienced translators.
Heterogeneous (subgroup) analysis by experience level within the translation market reported in the paper; evidence presumably from DiD estimates of exit/participation rates across experience levels. (Exact sample sizes and exit definitions not provided in the abstract.)
Following ChatGPT’s launch, some online labor markets experienced displacement effects characterized by reduced work volume and earnings, exemplified by the translation & localization OLM.
Empirical analysis using a Difference-in-Differences (DiD) design on online labor market (OLM) data; the abstract identifies translation & localization OLM as an example. (Sample size and exact data window not specified in the abstract.)
The system forces many children to age out at 21, creating deportation risks for those who are American in every meaningful sense except paperwork.
Policy consequence of long backlogs: derivative status rules cause dependents to 'age out' at 21; deportation risk implication is a legal/administrative outcome. The excerpt does not quantify the number affected or present a dataset.
The backlog traps H-4 dependent spouses, over 90% of whom hold bachelor's degrees, in years-long employment prohibition, removing skilled labor from the workforce.
Claim combines (a) an asserted >90% college-degree rate for H-4 spouses—presumably from ACS/DHS or authors' survey analysis—and (b) immigration policy facts that many H-4 spouses lack work authorization for extended periods; the excerpt does not provide the underlying dataset, sample size, or citations.
Constrained mobility suppresses H-1B wages by 12.2%.
Empirical estimate asserted in the paper (likely from econometric analysis comparing wages under constrained vs. unconstrained mobility); the excerpt does not cite the specific study, dataset, sample size, or methods that produced the 12.2% figure.
Employer-specific sponsorship combined with high switching costs—$5,000+ in fees and multi-year delays—concentrates labor-market power among employers.
Policy/mechanism claim supported by typical filing fee estimates and observed multi-year adjudication/porting constraints; the excerpt does not report a formal empirical test or sample size demonstrating employer market power concentration.
These provisions have generated wait times as extreme as 195 years for Indian nationals in the EB-2 category.
Projection based on visa bulletin/backlog dynamics and issuance rates for EB-2 India; the paper does not show the step-by-step projection or assumptions in the excerpt.
The U.S. employment-based immigration system traps over 1.8 million skilled workers and their families in legal limbo.
Paper's aggregate/backlog calculation presumably using Department of State visa bulletin backlogs, USCIS pending adjustment of status (I-485) inventories, and derivative family counts; the paper does not provide the detailed method or sample breakdown in the excerpt.
Occupational sorting explains a somewhat larger share of the gender gap in Ireland than in other European countries, but a substantial portion remains unexplained, pointing to possible unobserved structural, cultural or organisational factors specific to the Irish labour market.
Decomposition analysis for Ireland using ESJS data showing occupation contributes more to the explained component in Ireland than on average, while the unexplained residual remains large.
Gender gaps are larger and less well explained by observable characteristics among younger cohorts (aged under 35), implying under-representation of women in advanced digital roles is emerging early in careers.
Age-cohort subgroup regressions and decomposition analyses on ESJS data comparing explained/unexplained gaps for workers aged under 35 versus older cohorts.
Gender disparities widen significantly at the very upper end of the distribution of digital job intensity — a 'digital glass ceiling' — while lower and middle levels show more modest differences.
Distributional analysis of the Job Digital Intensity Index (JDII), constructed from ESJS digital task items, showing larger gender gaps at the upper tail of the JDII distribution.
AI causes job loss due to the automation of repetitive tasks.
Narrative literature review and synthesis of recent economic studies presented in the paper; no original empirical sample or primary data collection reported.
The findings raise ethical concerns about using such models in sensitive selection processes and highlight the need for transparency and fairness in digital labour markets.
Interpretive/concluding claim based on the observed adjective-based gendering and the broader literature on algorithmic fairness; recommendation rather than direct empirical result.
Gendered linguistic patterns emerged in the adjectives attributed to female and male candidates: GPT-5 tended to associate women with emotional and empathetic traits and men with strategic and analytical traits.
Empirical/qualitative analysis of the adjectives and descriptive language in GPT-5's outputs for the 24 simulated profiles; categories reported (emotional/empathetic vs strategic/analytical).
Large language models (LLMs) risk reproducing, and in some cases amplifying, gender stereotypes and bias already present in the labour market.
Framed as an assertion supported by prior literature and used as motivation for the study; partially evaluated empirically in this paper via the GPT-5 experiment.
Developing economies face heightened risks from AI due to large informal sectors, limited reskilling infrastructure, weaker labor mobility, and constrained social protection.
Comparative institutional analysis and application of structural-transformation theory; argument is qualitative and no explicit cross-country regression or representative sample of developing countries is provided in the paper.
Displacement often occurs faster than job creation and worker reallocation, producing transitional unemployment and skills gaps.
Temporal-mismatch argument based on historical patterns of technological adoption and task-based substitution theory; paper synthesizes prior theoretical work rather than presenting new time-series microdata or measured reallocation speeds.
Developing economies are more vulnerable where employment is concentrated in routine or informal tasks and where reskilling, mobility, and institutional buffers are limited.
Comparative consideration of advanced vs developing economies drawing on macro/sectoral indicators, labor market structure discussions, and existing empirical studies cited conceptually.