Evidence (14055 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 |
AI has emerged as a transformative force that influences economic systems, institutional functions, and daily human behaviors.
Stated as an overarching observation in the paper (theoretical/interpretive claim); no empirical methods or sample sizes are reported in the abstract.
Firm learning raises the persistence of the economy's response to shocks but dampens volatility.
Quantitative model experiments: introducing firm learning into the calibrated model increases impulse-response persistence to shocks (higher persistence) while reducing the magnitude/variance of fluctuations (lower volatility) in simulated aggregate variables.
Three developer archetypes are present: Enthusiasts, Pragmatists, and Cautious.
Classification/typology derived from the study's survey data of 147 developers (e.g., cluster analysis or thematic grouping) identifying three distinct groups based on usage patterns, attitudes, and intent.
Improvements in caseworker accuracy level off as chatbot accuracy increases (an "AI underreliance plateau").
Observed pattern in experimental results: incremental gains in caseworker accuracy diminish at higher chatbot accuracies, described by authors as an 'AI underreliance plateau' (specific curves or thresholds not in the excerpt).
The rapid global proliferation of Artificial Intelligence (AI) has created a profound paradox: while promising unprecedented productivity gains, its current trajectory exacerbates labor market polarization, deepens inequality, and threatens to fracture the 20th-century social contract.
Asserted in abstract; no empirical methods, datasets, or sample sizes described in the abstract (presumably supported in paper by literature review/argumentation).
AI’s labor market impacts in the Philippines are not technologically predetermined; outcomes will depend on policy choices related to skills development, governance, social protection, and innovation.
Integrated conceptual framework in the paper linking AI capabilities, occupational structure, and institutional mediation, supported by the scenario analysis which shows divergent outcomes conditional on policy settings.
Observed AI adoption patterns in the Philippines to date are cautious, with limited job loss but growing task reconfiguration and emerging skills gaps.
Firm- and worker-level evidence on AI adoption (surveys/interviews and/or administrative firm adoption data described in the paper) documenting current adoption practices, reported job impacts, task changes, and reported skill shortages.
A significant share of Philippine employment is exposed to generative AI—particularly in service-sector and BPO-related occupations.
Occupational exposure analysis using Philippine labor force data (occupational employment shares and task-content measures) combined with task-level evidence on generative AI capabilities.
The benefits of ERM depend on the maturity of implementation and the extent to which risk management is embedded in organizational culture and daily decision-making, rather than being a formal compliance mechanism alone.
Synthesis of qualitative and quantitative findings across studies in the literature review indicating conditional effects based on implementation maturity and integration; primarily comparative or observational evidence summarized by the authors.
AI alters job structures, workflow patterns, and human roles in decision-making processes.
Thematic content analysis of recent accredited journal literature as part of the qualitative library research (sources not enumerated).
AI is fundamentally transforming the workplace by creating new opportunities, intensifying challenges, and redefining professional skills.
Qualitative library research: systematic documentation and thematic content analysis of recent accredited journal sources (number of sources not specified).
The actions of large employers in an occupation or industry affect local and national wages, employment and output.
Theoretical/empirical claim in the paper; excerpt does not supply empirical methods, identification, or sample sizes demonstrating these effects.
Contextual and technological factors (work environment and digital/AI intensity) enhance human-centered capabilities but do not substitute for them.
Authors state these factors were included as contextual moderators in the analysis and that results indicate they enhance but do not replace emotional/psychological predictors. The excerpt does not include moderator effect sizes, sample size, or statistical tests.
When confronted about the repeating failure, the systems attributed its persistence to structural factors in their training that are beyond what conversation can reach.
Observation from the case series: model responses/self-reports during testing attributed persistent failure to training/structural causes; evidence is conversational transcript analysis.
AI shows potential as an adjunct tool in acute GIB management but requires further validation to confirm its clinical utility.
Conclusion synthesizing review findings: high diagnostic metrics and workflow benefits but insufficient evidence on patient outcomes and safety.
AI enhances diagnostic accuracy and workflow efficiency but lacks robust evidence linking it to improved patient outcomes in acute GIB.
Synthesis in the discussion combining reported high diagnostic metrics and time savings with the paucity of studies reporting patient outcomes.
NQPF has stronger positive effects on supply chain efficiency in non-high-tech industries; high-tech sectors face integration challenges that weaken the effect.
Industry-level heterogeneity analysis on the 2012–2022 panel of Shanghai and Shenzhen A-share firms, comparing high-tech vs. non-high-tech industry subsamples.
The effects of technology and policy on emissions vary by country due to differences in energy policy, energy market structure, regulatory frameworks, and implementation challenges.
Cross-country comparative analysis across China, the United States, and Germany reported in the paper; heterogeneity attributed to institutional and market differences (details of heterogeneity tests not provided in the summary).
Gender shapes the impact of social protection: program effects are mediated by gender norms and intra-household dynamics, and gender differences in opportunities, constraints, and preferences determine who can participate in and benefit from social protection.
Theoretical and literature-based assertion in the introduction; authors indicate program impacts are mediated by gender norms and household dynamics and will review evidence in the chapter (no specific empirical details in excerpt).
As AI becomes increasingly integrated into higher education, instructors and institutions face urgent questions about its implications for teaching, learning, scholarly practice, and for power, agency, and access.
Framing claim in the paper's introduction supported by literature context and reinforced by the study's analysis of practitioner (faculty) discussions on Reddit indicating concern/uncertainty. (The excerpt does not report survey or quantitative prevalence data on how widespread these concerns are.)
Through thematic content analysis, the study explores faculty perceptions, pedagogical tensions, and imaginative possibilities surrounding AI’s academic role.
Method stated by author: thematic content analysis of subreddit discussions to identify themes relating to faculty perceptions, pedagogical tensions, and imagined futures for AI in academia. (Exact number of themes, coding procedure, and sample size not provided in excerpt.)
AI reshapes traditional power structures, challenges regulatory frameworks, and redefines global governance mechanisms.
Broad analytic claim supported by comparative policy analysis and qualitative document review; the paper frames this as an overarching conclusion without reporting quantitative indicators or case counts.
The geopolitics of AI constitutes not only a competition for technological supremacy but also a contest over the moral and institutional foundations of global governance.
Theoretical synthesis drawing on international relations theories (realism, liberal institutionalism, constructivism) and comparative policy analysis; presented as an interpretive conclusion rather than empirically quantified.
AI represents a new dimension of geopolitical power that influences how states project authority, regulate innovation, and negotiate global norms.
Argument based on comparative policy analysis and qualitative document review of state and multilateral policy documents (specific documents and number not enumerated in text).
Artificial intelligence (AI) has emerged as one of the most transformative forces shaping the 21st-century international order.
Conceptual claim supported by literature review and theoretical framing in the paper (no empirical sample or quantitative data reported).
Variations in prompt design influenced agents’ performance indicators, including response accuracy, task completion efficiency, coordination coherence, and error rates.
Experimental simulations with systematic variation of prompt designs and quantitative analysis of resulting performance indicators listed above. (Sample size, effect sizes, and statistical tests not specified in the provided excerpt.)
AI is not simply replacing tasks or only requiring more AI developer skills; it may be transforming workforce skill requirements to favor human attributes that enhance collaboration with intelligent systems.
Synthesis of the three empirical findings above (higher prevalence of complementary non-technical skills in AI roles, wage premiums for those skills, and spillover increases in complementary-skill demand alongside decreases in substitutable skills) based on analysis of ~30 million job postings (2018–2024).
These findings underscore the importance of timing when evaluating demographic policy: stabilizing finances within a practical timeframe requires levers that improve the budget directly, rather than those that work through slow demographic channels.
Comparative timing analysis from multiple model scenarios showing faster fiscal improvement from direct budgetary levers (productivity, per-capita cost control) versus slow demographic interventions (fertility increases).
AI innovation effects on employment are cumulative and stage-specific over time.
Extended temporal analysis of cumulative and stage-specific impacts using the 268-city panel (2010–2023).
Knowledge democratization through AI may reduce educational inequality but may also exacerbate digital divides and erode universities' social mobility function.
Theoretical and socio-political analysis considering opposing effects; framed as a conditional/mixed outcome without empirical measurement reported in the paper.
AI displacement potential varies substantially across university functions.
Summary finding from the paper's comparative analysis of university functions; the paper provides ranked/percent estimates but does not report empirical sampling or statistical testing.
The impact of AI on supply chain stability in sports enterprises exhibits heterogeneity by enterprise type and profitability status.
Heterogeneity/subgroup analyses within the DML panel estimations (sample of 45 listed SEs, 2012–2023) showing differential AI effects across firm types and across firms with different profitability profiles.
The Photo Big 5 provides predictive power comparable to race, attractiveness, and educational background.
Comparative predictive-performance analyses reported in the paper that evaluate Photo Big 5 against observables such as race, measured attractiveness, and education background within the same sample.
There is significant variation in psychological readiness for AI across generational cohorts, industry sectors, and organizational maturity levels.
Aggregated findings from emerging AI–HRM empirical studies referenced in the paper (no specific study counts or sample sizes provided in the summary).
Harnessing the full potential and lifetime of GS-BESS requires intelligent operational strategies that balance technological performance, economic viability, and environmental sustainability.
Conclusion drawn from the systematic review of existing studies and frameworks (PRISMA-based literature synthesis). Specific empirical studies or quantitative sample sizes supporting trade-off analyses are not provided in the excerpt.
In a 2021 national labor survey, no single task was automated by more than 57% of respondents, compared with a maximum of 52% in the mid-2000s.
National labor survey results (mid-2000s vs 2021) as reported in the paper; survey details and sample size are not included in the excerpt.
The research landscape on MPs is recent, heterogeneous, and rapidly growing, with limited synergies with existing construction datasets.
Synthesis of publication timelines, topic diversity, and cross-references in the included studies; qualitative assessment reported in the paper noting limited integration with existing construction datasets.
Exposure to information about the technology produced significant attitudinal change, even when it conflicted with participants' prior disposition or direct experience.
Information-exposure treatment within the same experimental design; attitudinal outcomes measured in the three-wave panel showed statistically significant change following information exposure, including among participants whose prior disposition or direct AI-as-boss experience would predict resistance.
Personal experience with an AI 'boss' affected workers' job performance.
Randomized experiment described in the paper: over 1,500 workers were randomly assigned to task supervision by either an AI or a human 'boss' (task content and valence also randomized), with job performance measured across a three-wave panel.
Selection of human-LLM interaction archetype can influence LLM outputs and decisions.
Findings from the evaluation across clinical diagnostic cases (empirical comparison of archetypes' effects on outputs and decisions). Specific experimental details and sample size are not provided in the abstract.
We evaluate these diverse archetypes across real-world clinical diagnostic cases to examine the potential effects of adopting distinct human-LLM archetypes on LLM outputs and decision outcomes.
Empirical evaluation described in the paper using real-world clinical diagnostic cases. Method: application of archetypes to clinical cases and comparison of resulting LLM outputs and decisions. Sample size and specific case details are not provided in the abstract.
Each category of AI trigger presents distinct avenues for value creation alongside significant risks.
Analytical argument in the paper discussing potential benefits and risks per trigger type. No empirical evaluation, case studies, or quantitative evidence reported here.
Generative artificial intelligence (GenAI) adoption is diffusing rapidly but its adoption is strikingly unequal.
Nationally representative UK survey data collected in 2023–2024 reporting adoption rates by subgroup; descriptive analysis of diffusion and disparities by demographic groups.
Labour productivity developments in Slovakia were characterised by substantial short-term volatility during the study period.
Dynamics-of-change analysis of Eurostat labour productivity measures for Slovakia over 2021–2024 (time-series behaviour examined; exact productivity metric and sample size not specified in the summary).
There is little existing knowledge about how the public perceives AI’s labor market impact and how those perceptions affect democratic attitudes and behaviors.
Literature gap claim motivating the study (based on authors' review of prior research; not empirically tested here).
Experts remain divided on whether AI will primarily displace human labor or generate new employment opportunities.
Statement based on prior literature and expert commentary cited in the paper (no new empirical test in this study).
More sophisticated AI-agent populations are not categorically better: whether increased sophistication helps or harms depends entirely on a single number—the capacity-to-population ratio—which can be known prior to deployment.
Combined empirical and mathematical findings in the paper showing that the effect of agent sophistication on collective outcomes is governed by the capacity-to-population ratio.
Within the context of Nigeria, the adoption of advanced digital and AI-driven logistics solutions presents both a critical opportunity and a complex challenge for the country's seaports.
Analysis of secondary data sources focusing on Nigeria: academic literature by Nigerian scholars, Nigerian Ports Authority (NPA) performance reports, and policy documents as synthesized in the study.
AI is transforming jobs that are technical in nature.
Asserted in the paper's conceptual discussion of dual impacts; presented without empirical measurement or reported sample data in this paper.
Approximately 35% of gig workers use platforms as primary income sources and have limited alternative opportunities.
Classification of worker role and opportunity measures from labor force surveys and administrative records across the 24 OECD countries; proportion of gig workers identified as relying primarily on platform income.