Evidence (1835 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Inequality
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This lack of focus creates uncertainty about whether regulatory technology helps legitimate economic recovery or instead strengthens exclusion and informality.
Interpretive observation from gaps identified in the reviewed literature; no empirical resolution provided.
Digital transformation reconfigures development patterns across regions and countries, altering established trajectories of regional development.
Theoretical integration of a technology–labor–space framework together with comparative regional field evidence illustrating changing development patterns (no quantified effect sizes or sample sizes reported).
Accounting for heterogeneity in AI literacy (agents' ability to identify and adapt to inaccurate AI outputs) can produce skill polarization in the long-run steady state.
Analytical/theoretical steady-state distribution analysis of agent skill dynamics with heterogeneous AI literacy parameters; paper reports conditions under which polarization emerges (theoretical, no empirical sample).
The intervention serves as a middle ground in the trade-off between higher costs (from more granular demographic targeting) and skew (from ignoring demographics entirely).
Authors' comparative claim about cost–skew trade-offs observed in their intervention versus alternatives; no quantitative cost or skew figures provided in the excerpt.
Aggregate effects are geographically uneven (geographic unevenness in AI-driven labor market impacts).
Synthesis across studies observing variation by geography and noting non-Anglophone markets and developing economies as under-studied and differentially affected.
Wage polarization characterizes the aggregate pattern of labor market change associated with recent AI advances.
Aggregate characterization from synthesized studies reporting divergent wage outcomes (higher wages for AI-augmented workers, pressures on junior/routine roles) consistent with polarization.
Sectoral effects are heterogeneous: infrastructure, security, and quality-assurance roles have expanded while developer roles have contracted.
Qualitative and quantitative results aggregated across the included studies noting role-level expansions and contractions; no single pooled effect size provided.
Depending on the used fairness metric, the Pareto frontier may include upper-bound threshold rules, thus preferring individuals with lower success probabilities.
Analytical derivations showing that for certain fairness metrics the set of Pareto-optimal rules includes rules that impose upper-bound thresholds; theoretical examples and arguments in the paper.
Responses [about AI's effects] vary by cohort and depending on survey framing.
Paper asserts heterogeneity in survey responses across demographic cohorts and due to framing effects (no subgroup sample sizes or framing experiment details in excerpt).
This [model divergence] may explain why public opinion is not settled about the effects of AI.
Paper's interpretive claim linking model divergence to unsettled public opinion (presented as a plausible explanation; no causal test or survey linkage provided in excerpt).
Current models about the vulnerability level of occupations and economic sectors differ widely in their forecasts.
Paper's comparative statement about existing models and their forecasts (no specific models, quantitative comparisons, or sample sizes provided in the excerpt).
Introducing taxes on AI returns (τ_ai) and financial gains (τ_f) yields three distinct long-run regimes: low-tax (extreme inequality), moderate-tax (stable mixed economy), and high-tax (post-scarcity with universal basic income).
Model extension with tax parameters τ_ai and τ_f and analysis of steady states/long-run regimes; bifurcation analysis identifying regime types associated with ranges of (τ_ai, τ_f).
The finding that recurrence and neighborhood statistics are stronger predictors than complaint volume has direct implications for complaint routing given the demographic correlates of those features.
Interpretive implication drawn by the authors from the SHAP results; presented as a logical consequence rather than a separately tested empirical result in the excerpt.
We empirically validate these theoretical observations using both synthetic and real datasets.
Experimental evaluation reported in the paper applying proposed policies and measures to synthetic data and at least one real dataset (details not given in abstract).
Modeling fiscal policy as a government problem (instead of an abstract planner) implies a tax changes the firm's automation first-order condition, raises revenue only on the remaining automation base, and requires specifying rebates and administrative losses.
Explicit governmental optimization and budget-accounting setup in the model: taxes enter firms' automation first-order conditions; revenue is computed on post-tax automation activity and rebates/administration are modeled.
The central analytic object is the derivative of household consumption demand and the collective wage bill with respect to automation.
Paper's stated modeling focus: comparative-static derivatives linking automation to household consumption demand and aggregate wages; used to characterize incidence and welfare effects.
Automation reallocates income and ownership claims.
Theoretical model with heterogeneous households who hold capital/equity claims; equilibrium determines wages and returns and shows changes in income and ownership shares when automation increases.
While Agentic AI enhances economic performance, its benefits are mediated by structural conditions and are unevenly distributed across countries (i.e., reinforcing core–periphery inequalities).
Combined findings from fixed-effects regressions, mediation analysis, and observed heterogeneity between developed and emerging economies in the 2015–2024 panel.
Generative AI-powered tools like ChatGPT are reshaping market skill demands while also offering new forms of on-demand learning support to meet those demands.
Framed in paper as background/motivation; asserted from prior literature and the paper's motivating claims rather than reported as a quantified result in this study.
The rise of digital agents will transform the foundations of production, labour markets, institutional arrangements and the international distribution of economic power.
Synthesis and theoretical projection across sections of the paper; presented as a broad conclusion without reported empirical quantification in the provided text.
There is a fundamental asymmetry between economic and social reproduction: digital agents can compensate for productive functions of the population but are unable to substitute the population's functions of social reproduction.
Theoretical argument and conceptual distinction in the paper; no empirical study measuring substitution in social reproduction provided.
The rapid growth of AI and automation offers Sub-Saharan Africa economic opportunities as well as labor market challenges.
Systematic review of the literature reported in the paper; scope and number of studies not specified in the abstract/summary provided.
AI adoption leads both to job displacement and job creation, including the emergence of new occupational categories.
Abstract states the review examines empirical evidence on both job displacement and creation and the emergence of new occupations; no numeric counts or sample sizes provided in abstract.
The study identifies short-term transitional risks and long-term productivity gains associated with AI integration in the workforce.
Abstract states the paper evaluates both short-term risks and long-term productivity gains from AI integration based on the reviewed literature; no empirical quantification given in abstract.
AI-driven automation and augmentation are reshaping employment landscapes, with emphasis on sector-level disruption, skill transformation, and socioeconomic consequences.
Abstract states this as a conclusion of the review drawing on interdisciplinary empirical literature; no specific studies or sample sizes cited in abstract.
The accelerating deployment of artificial intelligence across industries has fundamentally altered the structure of global labour markets.
Statement in abstract summarizing a systematic review of interdisciplinary literature (economics, computer science, organizational behaviour, public policy); no specific sample size reported in abstract.
Survey evidence suggests public attitudes towards AI combine optimism with apprehension, and most respondents oppose granting AI systems final authority over hiring and dismissal decisions.
Review cites multiple public opinion and survey studies reporting mixed (optimistic and apprehensive) attitudes and opposition to AI final authority in employment decisions (survey evidence summarized).
There are important regional differences—especially in developing contexts—that necessitate context-specific approaches to improving women’s participation in AI-enabled work.
Observation reported in the review drawing on geographically diverse studies and policy analyses; the abstract does not quantify differences or report sample sizes for cross-region comparisons.
Social, cultural, and ethical considerations influence women’s engagement in AI-centric workplaces.
Claim made in the review, based on interdisciplinary literature that includes sociocultural analyses and ethical discussions; the abstract does not provide empirical effect estimates or sample sizes.
AI applications—ranging from recruitment algorithms to workplace automation—can either reinforce gender disparities or promote equitable employment outcomes.
Stated in the review based on collated findings from multiple studies and analyses that document both harms (e.g., biased recruitment algorithms) and potential benefits (e.g., tools designed to reduce bias); no single empirical study or pooled effect size provided in the abstract.
Artificial Intelligence (AI) is rapidly transforming workplaces across the globe, offering both novel opportunities and unique challenges for women in technology-driven industries.
Stated in the paper's introduction/abstract as a summary conclusion based on a narrative literature review of peer-reviewed studies, policy analyses, and preprint research; no specific sample size or primary empirical method reported in the abstract.
These efficiency gains are offset by a growing 'Efficiency-Legitimacy Paradox' (i.e., improvements in efficiency come with worsening legitimacy concerns).
Conceptual synthesis from the systematic review (2018-2026) identifying a recurring trade-off across reviewed studies; specific empirical quantification not provided in abstract.
There is a structural shift from 'street level' bureaucracies to 'system-level' architectures that can be defined as the institutional division of 'Artificial Discretion' to algorithmic infrastructures.
Synthesis from the PRISMA-guided systematic review of literature (2018-2026) reporting observed changes in administrative architectures; specific studies not enumerated in abstract.
As a General-Purpose Technology (GPT), Artificial Intelligence (AI) is fundamentally reconfiguring state capacity, as well as the mechanics of global economic management.
Systematic review of current research studies (2018-2026) conducted following PRISMA guidelines; synthesis of literature claiming broad institutional and macroeconomic effects. Number of studies not specified in abstract.
Uncertainty-aware exploration (in algorithms) alters fairness metrics compared to policies that ignore uncertainty.
Results from simulation experiments compare uncertainty-aware exploration policies to baseline policies and report changes in fairness metrics (as described in the abstract and results).
Operationalizing hardware-based governance must address transition realities including legacy hardware, attestation at scale, and protection of civil liberties.
Policy implementation analysis in the paper identifying practical challenges to deploying hardware-layer controls (conceptual/operational analysis; no empirical trial data provided).
AI is increasingly being integrated into both existing and newly emerging digital infrastructures, altering their architecture, functional role, and strategic significance as these systems begin to operate as embedded cognitive infrastructures shaping knowledge production, decision-making, and institutional processes.
Conceptual and descriptive claim presented by the paper (theoretical analysis/literature-informed observation). No empirical sample size or quantitative methods reported in the provided text.
Variable importance improvements to zero-shot tabular classification produce mixed results with respect to algorithmic fairness.
Authors report experiments applying variable-importance-based adjustments to zero-shot LLM tabular classification and evaluating resulting algorithmic fairness outcomes; described as producing mixed results. (Sample size not provided in abstract.)
Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) verified correlations among educational background, gender inclusiveness, digital literacy, and perceived algorithmic fairness.
Paper reports use of CFA and SEM to test relationships among those variables; reliability/fit supported by Composite Reliability (CR), Average Variance Extracted (AVE), and model-fit indicators.
The rapid advancement of artificial intelligence (AI) technologies, particularly generative AI and large language models, has reignited debates about the future of work and the potential for widespread labor market disruption.
Statement in the paper's introduction/abstract citing recent empirical studies, industry reports, and ongoing debates; no original sample or numerical evidence reported in the abstract.
Outcomes of AI deployment in labor-market settings depend on complementary organizational practices, workers’ access to skills, and the regulatory environment.
Synthesis-derived moderator/ mechanism claim from qualitative analysis of the 19 included studies identifying organizational practices, skill access, and regulation as contextual moderators.
If employment losses are relatively small and productivity gains are realised, AI adoption could boost Exchequer revenues. But if job displacement is sizeable, tax receipts fall while welfare spending rises, resulting in potentially large pressures on the public finances.
Conditional fiscal scenarios simulated in the report combining employment, wage and benefit changes with the public finance implications (tax receipts and welfare spending); reported as scenario-based outcomes.
Ireland’s tax and welfare system absorbs most of the income loss for lower income households, and roughly half of the loss for households at the top of the income distribution.
Microsimulation using SWITCH to model taxes and transfers applied to simulated income changes across income groups; reported as a finding in the report.
India exhibits a distinctive polarisation pattern: a shrinking middle-skill workforce alongside a persistently large low-skill labour segment.
Descriptive analysis of secondary data and official reports from 2020–2024 comparing occupational and skill distributions in India.
Model behaviors vary strongly with levels of reasoning and with users' inferred socio-economic status.
Reported findings from evaluations that varied model reasoning prompts/levels and user socio-economic status signals; paper states behavior differences across these dimensions. Abstract does not give sample sizes or exact quantitative differences.
The inequality-reducing impact of AI is weaker when carbon inequality is measured by the Theil index, implying persistent structural divides between advanced and less developed regions.
Same provincial panel dataset (2003–2021) with the Theil index as the dependent variable; results show a weaker (and impliedly less robust) association between AI development and Theil-measured carbon inequality.
Residual within-task group dynamics dominate the magnitude of the gender wage gap, though task-based employment and wage channels are important for timing and direction of changes in gender inequality in the formal sector.
Decomposition analysis partitioning the gender wage gap into within-task residuals and task-based employment and wage components, with residuals accounting for the largest share of the gap but task channels explaining temporal shifts.
The analysis focuses on formal wage workers in Indonesia from 2001 to 2019.
Stated sample and timeframe in the study description; analyses use data on formal wage workers in Indonesia covering 2001–2019.
AI technologies and digital platforms have fundamentally altered the organization of work and modes of value realization.
Synthesis of contemporary literature and theoretical analysis in a conceptual study (no empirical sample reported).
Using pre-existing exposure as an instrument for ChatGPT adoption in a long-difference IV design, ChatGPT adoption causes households to spend more time on digital leisure activities while leaving total time spent on productive online activities unchanged.
IV long-difference empirical design: instrumenting household adoption with pre-ChatGPT exposure (2021 browsing); outcome measured as changes in categorized browsing durations (LLM-based classification into 'leisure' vs 'productive' sites); controls include demographic-by-region fixed effects and browsing composition controls.