Evidence (4004 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).
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Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 claims
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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 |
Labor Markets
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This paper systematically reviewed peer-reviewed journal articles indexed in the Scopus and SCI databases.
Stated method in abstract: systematic review of peer-reviewed journal articles indexed in Scopus and SCI; no sample size or study count reported in abstract.
The geometry replicates against Eloundou et al.'s GPT-4 task ratings: DWA-level correlation rho = 0.635.
Reported Spearman/Pearson correlation between the paper's DWA-level OAI and Eloundou et al.'s GPT-4 task ratings (rho = 0.635).
The inferred geometry is robust under a resolution stress test: when K (number of clusters) is varied from 7 to 15 the polar gap widens from 0.45 to 0.57.
Stress-test described by authors varying clustering resolution (K=7..15) and reporting change in the polar gap metric (0.45 -> 0.57).
Planning & Design (macro M7) has mean OAI = 0.499.
Reported macro-level mean OAI computed after projecting DWA OAI values into the 7-macro typology.
Deeper AGI adoption raises the organic composition of capital.
Analytical derivation within the political-economy model showing that substituting living labor with machine-based productive systems increases the capital-to-labor composition (theoretical model; no empirical sample).
The combined findings enable planning for a broader skill-gap analysis of educational curricula to bridge gaps and support upskilling/reskilling of wind-energy professionals.
Conclusion/recommendation in the paper based on integrated results from interviews, surveys, and job postings; this is presented as an intended next step rather than an empirically tested intervention.
Survey results provide insights on preferred training formats for upskilling/reskilling in the wind sector.
Paper states survey collected preferences on training formats; no survey sample size or preference breakdown is provided in the summary.
Job-posting analysis shows that approximately 44% of engineering-related positions in the wind sector require advanced digital skills.
Quantified result reported from the paper's job-posting analysis; the summary gives the percentage but does not report the number of job postings analysed.
Across all sources, scientific programming and numerical modelling consistently emerge as cornerstone competencies for the wind sector.
Result reported as consistent across interviews, surveys, and job-posting analysis; no quantitative ranking, counts, or sample sizes provided in the summary.
Triangulation of survey data, expert interviews, and job-posting analysis suggests a coherent picture of advanced digital skills priorities within the wind energy sector.
Integration of qualitative interviews, quantitative survey results, and job-posting analysis reported in the paper; no numerical concordance statistics presented.
Interviews and job-postings are analysed using Natural Language Processing (NLP), enabling automated analyses that can be repeated in future years to track the evolution of required skills.
Paper states NLP was applied to interviews and job-posting corpora and frames this as enabling repeatable automated analyses; no performance metrics or NLP sample sizes provided.
This study maps demand for advanced digital skills in the wind industry using a mixed-method approach combining expert interviews, survey data, and job-posting analysis.
Methodological description in the paper; explicit listing of the three data sources and their intended complementary roles.
The wind energy industry is facing a growing need for professionals with advanced digital skills beyond traditional IT positions.
Statement in paper based on mixed-method mapping (expert interviews, survey data, and job-posting analysis); no sample size reported for the sector-wide assertion.
The paper proposes the Embedded Formation Degree (EFD), a four-component framework consisting of accelerated domain entry, a four-year AI fluency track, an embedded practice firm, and structurally integrated employer partners.
Conceptual proposal put forward by the author(s) in this paper (descriptive statement in the abstract).
To foster more equitable outcomes, platform governance should be gender‑responsive, including algorithmic transparency, inclusive system design, and extension of core labor protections to gig workers.
Practical implications stated in the paper arising from the literature synthesis and feminist political economy framing.
AI‑enabled platforms can expand income opportunities and flexibility for women.
Thematic synthesis of findings across the 48 reviewed studies; reported in the paper's Findings as one side of a central paradox.
I have developed LLMbench, a research instrument for the comparative close reading of LLM outputs that visualises token probability distributions, entropy curves, and cross-model divergence.
Description of a tool/method developed by the author (LLMbench); claim about the tool's features as stated in the abstract; no implementation details or evaluation sample sizes provided in the abstract.
Digital learning platforms and AI-based training tools are increasingly used as central mechanisms to support continuous skill acquisition and professional growth.
Synthesis of prior studies and thematic literature discussed in the editorial (Bankins et al., 2024a; other cited works).
Adoption of STARA increases the need to upskill and reskill workers across skill levels, with even high-skilled workers expected to integrate new digital competencies into their professional trajectories.
Literature synthesis and cited empirical/conceptual studies (e.g. Hani et al., 2025; Ibrahim and Abiddin, 2024; Singh and Chandra, 2026; Tariq, 2026).
Journalists and editors exercise bounded and situational agency through local adaptation, self-training, and development of ethical guardrails that institutionalise responsible AI use.
Based on in-depth interviews with newsroom staff (journalists, editors, technical personnel) at Al-Masry Al-Youm; qualitative accounts of local practices such as self-training and the creation of internal ethical rules. Sample size not reported in the excerpt.
Experts assigned the highest responsibility for addressing these risks to general-purpose AI developers and governance actors (including governments, regulators, and standards bodies).
Delphi ratings of actor responsibility reported in paper: highest responsibility attributed to general-purpose AI developers and governance actors by 272 experts.
Policymakers in emerging economies should adopt integrated policy frameworks combining AI development incentives, labour market reform, and education strategies to ensure technological progress translates into inclusive and sustainable development.
Policy recommendation derived from the study's empirical findings and interpretation.
The empirical findings validate the core theoretical proposition of Routine-Biased Technological Change that skill-biased technological change operates through heterogeneous channels invisible at the aggregate level.
Synthesis of empirical results (skill-disaggregated effects differ, total unemployment insignificant) used to support the RBTC theoretical proposition.
Unemployment among less-educated workers shows a positive long-run relationship with sustainable development, interpreted as reflecting structural labour reallocation effects consistent with RBTC.
Long-run ARDL coefficient for less-educated workers' unemployment reported as positive in the paper; interpretive link to Routine-Biased Technological Change (RBTC) and labour reallocation.
In the long run, AI adoption contributes positively and significantly to sustainable development through productivity gains and innovation spillovers after structural adjustments are completed.
Long-run ARDL estimates reported in the paper indicating a positive and statistically significant long-run coefficient for AI adoption; theoretical interpretation invoking productivity gains and innovation spillovers.
The audit detects significant engagement premiums for three exploitation-related dimensions: performative labor, emotional bait, and privacy violations.
Reported aggregated analysis across labeled dimensions showing positive associations of these dimensions with views; privacy violations mentioned in summary of findings (specific effect size for privacy violations not reported in provided text).
Within-channel analyses indicate median view boosts of +56.0% for performative content (FDR-corrected p < 0.001), with effects holding in same-year robustness checks (p = 0.030).
Within-channel analyses for performative-content label showing median percent boost, FDR-corrected significance, and robustness check restricting comparisons to same-year videos.
Within-channel analyses indicate median view boosts of +65.6% for emotional bait content (FDR-corrected p < 0.001).
Within-channel (fixed-effects or matched) comparisons of emotional-bait-labeled vs. other videos, with multiple-testing correction (FDR); reported median percent boost and p-value.
A mixed-effects regression controlling for channel-level variation shows that a one-unit increase in exploitation score yields a 4.4× increase in views (p < 0.001).
Mixed-effects regression analysis with channel-level random effects on the full video dataset; reported multiplicative effect and p-value.
Exploitation scores correlate with view counts (Spearman ρ = 0.229, p < 10^{-50}).
Spearman rank correlation computed between exploitation scores and view counts across the study dataset (5,051 videos).
A multi-annotator validation study (N=107) shows strong agreement with human judgment: macro-average F1 = 0.911 and high sensitivity for overall exploitation risk (recall = 0.960, F1 = 0.793).
Multi-annotator validation study with 107 human annotations comparing model/weak-supervision labels to human judgments; reported classification metrics.
The machines are increasingly becoming competent.
Authorial assertion about the trend in AI capability (no metrics or studies provided in the excerpt).
The concept of co-intelligence describes a new cognitive ecology where the human and artificial minds mutually influence one another to come up with ways of comprehending, creating and making choices that neither of them could accomplish individually.
Conceptual claim attributed to Ethan Mollick (2024) and extended by the author — described conceptually rather than demonstrated empirically in the excerpt.
None of the past technologies have spread into so many aspects of human life, so fast.
Author's comparative assertion about the speed and breadth of AI diffusion relative to prior technologies (no empirical comparison provided in the excerpt).
Artificial intelligence has become a partner in our everyday activities: it dictates our emails, diagnoses our diseases, educates our young children, controls our budgets, creates our artworks, and influences the policies made by governments and corporations.
Authorial assertion listing domains of current AI use (no empirical study or quantified data provided in the excerpt).
The internet had to cope with more or less a decade before it could reach one billion users; social media did it in half times.
Comparative historical adoption claim presented by the author (no citation or empirical method given in the excerpt).
Less than a year after its debut, hundreds of millions of individuals on all seven continents were using large language models, in virtually every field of professional activity, and in most languages.
Authorial assertion summarizing global LLM adoption (no specific study, dataset, or methodology provided in the excerpt).
There were now a hundred million ChatGPT users in two months.
Authorial assertion in the text citing a user-count milestone for ChatGPT (no study or data source provided in the excerpt).
Audit outputs can be leveraged as red-teaming inputs to stress-test fairness robustness and strengthen AI governance through improved data quality and oversight (proposed intervention).
Proposed methodological/policy recommendation in the paper (proposal, not evaluated empirically in the excerpt).
AI-enabled hiring systems are widely adopted.
Statement in paper (background claim); no empirical sample or citation provided in the excerpt.
Changes in skill demand in online labour markets are an outcome of introducing platform-embedded GenAI.
Synthesis of the study's empirical findings (difference-in-differences results showing increased skill diversity in logo jobs post-logo-AI and mediation evidence via competition) leading to the broader conclusion that platform-embedded GenAI can change skill demand on online labour platforms.
Stronger competition among freelancers partially mediates the effect of the platform-embedded logo-AI on higher skill diversity in logo jobs.
Mediation analysis within the difference-in-differences framework linking measures of freelancer competition to changes in requested skill diversity after the logo-AI launch. Specific mediation estimation details and sample size not provided in the abstract.
Logo jobs exhibit higher skill diversity than other design jobs after the platform introduced logo-AI.
Difference-in-differences comparison of skill-diversity metrics extracted via the authors' LLM-based skill extraction and embedding framework on EPWK job posts for logo design (treatment) versus other design jobs (control), pre- and post-introduction of the platform-embedded logo-AI tool. Sample size not reported in the abstract.
AI adoption raises real output.
Panel local projections linking establishment-level AI adoption (share of job postings requiring AI skills) to real output across 13 industries over 2017-2025.
AI adoption raises labor productivity.
Panel local projections estimating the effect of establishment-level AI-skill posting share on labor productivity across 13 industries (2017-2025).
AI is not a simple labor replacement but a powerful enabler, pushing the overall labor structure toward higher skills and added value.
Author interpretation based on the paper's empirical findings (DiD results) that show upward movement of labor-skill composition; specific empirical measures not provided in excerpt.
The findings provide strong empirical support for the 'skill-biased technological change' theory, revealing a significant complementary synergy between technological progress and high-skilled labor in the AI era.
Empirical analysis reported in the paper using a Difference-in-Differences design showing complementarity between AI-related technological progress and high-skilled labor; details on coefficients, confidence intervals, or sample not provided in excerpt.
AI innovation exerts a significant positive impact on the labor structure, optimizing the proportion of high-skilled and low-skilled labor.
Paper's empirical result using a Difference-in-Differences (DiD) empirical strategy; specific sample size or data source not reported in provided excerpt.
Although AI creates obstacles, it also has the potential to be an important tool for creating innovative opportunities and continued growth if managed with sound practices.
Concluding statement in the paper's abstract presenting a normative/conditional conclusion based on the paper's evaluation and synthesis of evidence (no primary quantified results provided in the supplied text).
AI leads to the creation of new jobs.
The paper explicitly states it examines the creation of new jobs as a ramification of AI (abstract); claim presented qualitatively without reported sample sizes or quantified effect in the provided text.