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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Self-efficacy negatively moderates the relationship between AI application and employees' job insecurity by strengthening the insecurity-reducing effect of moderate AI application and weakening the insecurity-enhancing effect of excessive application.
Moderation analysis on the same cross-sectional survey data (411 valid employee questionnaires), reporting a statistically significant negative (buffering) interaction of self-efficacy with AI application intensity on job insecurity.
In that same limiting case, the social rate of profit tends to zero.
Limit-case implication from the theoretical model linking capital composition and absence of living labor to vanishing profit rate (model analysis; no empirical data).
In that same limiting case, surplus value tends to zero.
Limit-case implication of the model under the value-transfer assumption (theoretical derivation; no empirical backing).
In the limiting case where actual AGI adoption approaches complete substitution and new labor fields fail to compensate, living labor tends to zero.
Limit-case analysis within the theoretical model (as adoption → complete substitution and no compensatory new labor fields, the model's variables imply living labor → 0; no empirical data).
Deeper AGI adoption places downward pressure on the social rate of profit.
Analytical result from the political-economy model linking higher organic composition of capital and reduced living labor to a fall in the social rate of profit (theoretical derivation; no empirical sample).
Deeper AGI adoption compresses the source of surplus value.
Theoretical implication derived in the model under the value-transfer assumption: as living labor falls, the base generating surplus value narrows (model argument; no empirical data).
If AGI adoption outpaces the creation of new labor fields, deeper AGI adoption reduces the quantity of living labor.
Model-based theoretical result: comparative statics of adoption vs creation of new labor fields in the paper's framework (no empirical sample).
Interviews provide expanded analysis on existing skill gaps and lifelong learning needs among wind-energy professionals.
Qualitative interview data are reported to highlight skill gaps and lifelong learning needs; specific counts of interviewees not provided in the summary.
The framework reframes the education–employer gap as a structural failure in the pathway and outlines implications for universities, employers, accreditors, and policymakers.
Conceptual claim and implications drawn by the author(s) in the paper (stated in the abstract).
The architecture of the undergraduate degree is structurally incapable of replacing the informal post-degree apprenticeship system through curricular revision alone.
Argument presented in the paper, supported by the systematic review of eighteen peer-reviewed studies and labor-market analyses cited in the abstract.
The informal post-degree apprenticeship system that historically completed graduate formation no longer reliably exists.
Claim based on the paper's systematic review of eighteen peer-reviewed studies and current labor-market analyses (as described in the abstract).
Higher education has misdiagnosed the resulting challenge as curriculum misalignment—a content problem assumed to be solvable through revised syllabi, AI electives, and marginal expansions of experiential learning.
Argument presented in the paper, supported by the paper's systematic review of eighteen peer-reviewed studies and labor-market analyses (as described in the abstract).
Artificial intelligence and automation are restructuring early-career knowledge-work roles by compressing the entry-level functions through which graduates historically built portfolios, developed professional judgment, and earned professional credibility.
Statement supported in the paper by a systematic review of eighteen peer-reviewed studies and current labor-market analyses (as described in the abstract).
Without intentional, gender‑aware interventions in policy and design, the AI‑driven gig economy is more likely to entrench existing social and economic inequalities than to alleviate them.
Conclusion and social implications in the paper based on thematic synthesis across 48 studies and the feminist political economy analysis.
AI‑mediated platforms generate structural precarity and digital marginalization that disproportionately affect women.
The paper's thematic synthesis of 48 studies highlights structural precarity and digital marginalization as mechanisms that reproduce disadvantage for women.
Wage gaps are present in AI‑mediated platform work and contribute to unequal outcomes for women.
Reviewed literature synthesized in the paper repeatedly cites wage gaps as one mechanism producing gendered disadvantage; reported in Findings.
Algorithmic bias on AI‑mediated platforms contributes to gendered disadvantage in platform work.
The paper identifies algorithmic bias as a key mechanism in the thematic synthesis of the 48 studies; cited as reproducing or amplifying gender inequality.
AI‑enabled platforms reproduce and risk amplifying gender inequality through algorithmic bias, wage gaps, structural precarity, and digital marginalization.
Synthesis across the 48 reviewed studies identifying recurring mechanisms (algorithmic bias, wage gaps, precarity, digital marginalization) that disadvantage women; presented in Findings.
Apart from earnings adequacy, occupations characterized by dimensions of precarity were associated with lower LLM exposure (i.e., higher precarity on those dimensions corresponded to lower LLM exposure).
Abstract statement summarizing regression results across separate models for each precarity dimension (exact coefficients not provided in abstract).
Occupations most likely to be exposed to LLM are those where precariousness is lowest.
Summary conclusion based on the reported comparisons of mean LLM exposure across precarity categories using the Labour Force Survey and regression analyses described in methods.
Apart from earnings adequacy, LLM exposure was lower among occupations exhibiting each separate dimension of precarity (contractual instability, schedule unpredictability, working-time mismatch).
Separate multivariate linear regression models (one per precarity dimension) estimated associations between occupational LLM exposure and each dimension using Canada's Labour Force Survey; results reported in abstract (no per-dimension effect sizes provided in abstract).
Using the multidimensional precarity index, occupations characterized by low exposure to precarity had a significantly higher mean LLM exposure (mean 0.386, 95% confidence interval 0.356-0.417) compared to occupations with medium (mean 0.258, 95% CI 0.221-0.295), high (mean 0.260, 95% CI 0.194-0.328) or very high precarity (mean 0.205, 95% CI 0.136-0.275).
Analysis of Canada's Labour Force Survey; constructed multidimensional precarity index; multivariate linear regression models with cluster-robust standard errors; model coefficients used to produce mean estimates of occupational LLM exposure. (Sample size not reported in abstract.)
Existing research has significant shortcomings in terms of local empirical evidence, micro task mechanisms, and the impact of cutting-edge AI.
Critical appraisal in the paper's discussion of gaps identified through the systematic literature review; no single-study sample size.
Skill mismatch constitutes the core contradiction of labor force transformation.
Interpretive conclusion from the literature review asserting that mismatches between worker skills and job/task requirements are central to the labor-market effects of AI.
The condition 'prompt anxiety' describes a key feature of how stochastic systems organise cognitive labour under 'vector capitalism.'
Conceptual/theoretical framing introduced by the author to label and analyze user experience and labour organization; no empirical quantification provided in the abstract.
AI platforms transform this uncertainty into extractable value through subscription models, token-based pricing, and prompt marketplaces.
Political-economic / theoretical tracing in the paper citing platform business models (subscription, token pricing, prompt marketplaces) as mechanisms that monetize user uncertainty; no quantitative revenue or case-study sample sizes given in the abstract.
Analysis through LLMbench demonstrates that the uncertainty users experience corresponds to measurable variation in model confidence across the generated text.
Empirical demonstration using LLMbench visualisations (token probability distributions, entropy curves) to link user-reported uncertainty to measurable changes in model confidence; specific datasets, models, or sample sizes not provided in the abstract.
Users of large language models have to work with a measurably aleatory process: identical inputs produce different outputs and minor wording changes cascade through the probability field of the generated text.
Empirical analysis using the author's research instrument (LLMbench) for comparative close reading of LLM outputs; specific sample size or number of models/runs not reported in the abstract.
Prompt engineering resembles the psychological and temporal structures that Walter Benjamin identified in gambling behaviour.
Conceptual/theoretical argument presented in the paper drawing an analogy between prompt engineering practices and Walter Benjamin's analysis of gambling; no empirical sample size reported in the abstract.
STARA may widen inequalities across occupational groups and cohorts—particularly affecting low- and medium-skill occupations—by fragmenting or limiting career paths and reducing institutional supports.
Concerns and literature synthesis in the editorial citing prior work on inequalities and occupational differences (e.g. Zajko, 2022 and other cited studies).
AI-based career planning platforms and digital portfolio/performance trackers can embed biases, amplify pressures for self-optimisation, provide only generic recommendations, and risk promoting a narrow view of what constitutes a desirable career.
Conceptual concerns and literature cited in the editorial (Bankins et al., 2024a and other referenced works); argued as potential unintended consequences rather than direct evidence from a single large empirical study.
Algorithmic gatekeeping in promotion and evaluation processes can privilege certain behaviours or skill sets while limiting transparency and equity in career advancement.
Editorial synthesis referencing recent work (e.g. Hillebrand et al., 2025) and conceptual concerns raised in the literature.
STARA is displacing routine tasks and potentially entire roles, particularly in occupations where automation and robotics can substitute standardized work processes.
Synthesis of existing literature cited in the editorial (e.g. Bahadure et al., 2024; Oosthuizen, 2019, 2022; Singh and Chandra, 2026; Singh et al., 2026).
AI adoption intensifies existing sustainability challenges for the newsroom, as journalistic content and labour increasingly support AI systems without corresponding financial return.
Qualitative interview data and organisational analysis from Al-Masry Al-Youm indicating increased use of journalistic outputs for AI purposes and lack of matched revenue; sample size not reported in the excerpt.
Reliance on global technology providers embeds forms of platform dependency within newsroom operations at Al-Masry Al-Youm.
Qualitative case study based on in-depth interviews with journalists, editors, and technical staff at Al-Masry Al-Youm (Egypt); analysis of newsroom practices and integration of third-party/global AI tools. Sample size not reported in the excerpt.
Across most risks, experts identified information, finance, and national security as the most vulnerable sectors.
Sector vulnerability ratings from the Delphi study (n=272); paper reports that information, finance, and national security sectors were most frequently judged vulnerable across risks.
AI users and the general public were judged the most vulnerable to these risks.
Delphi panel rated actor vulnerability; results reported in paper indicate AI users and general public received highest vulnerability ratings (n=272).
All 24 risks were judged as being more than 5% likely to cause catastrophic outcomes.
Aggregate Delphi judgments reported in paper: for each of the 24 risks, experts judged the probability of catastrophic outcomes to exceed 5% (n=272).
In a scenario where pragmatic mitigations are implemented, experts still judged five risks as having a more than 10% probability of catastrophic outcomes: dangerous capabilities, weapons & cyberattacks, environmental harm, inequality & unemployment, and power centralization.
Delphi responses under an alternative (pragmatic mitigations) scenario from the same expert panel (n=272); paper lists five specific risks still judged >10% catastrophic probability.
In a business-as-usual scenario, experts judged 18 of 24 risks as having a more than 10% probability of catastrophic outcomes (e.g., more than 1 million deaths or more than USD 100B in financial loss) in the next 5 years (2025-2030).
Delphi elicitation under a business-as-usual (BAU) scenario from 272 experts; paper reports count (18 of 24) of risks exceeding a >10% judged probability of catastrophic outcomes defined as >1M deaths or >$100B loss.
Experts estimated the five most severe harms in the next 5 years were likely to come from dangerous capabilities, competitive dynamics, weapons & cyberattacks (including CBRNE), power centralization, and false information.
Delphi panel rankings/ratings of risk severity across 24 risks collected from 272 experts; paper reports these top five as the most severe for the 5-year horizon.
Unemployment among highly educated workers consistently impedes sustainable development across both short- and long-run horizons.
Skill-disaggregated unemployment coefficients from ARDL short- and long-run estimates reported in the paper showing negative effects of highly educated workers' unemployment on development.
In the short run, AI adoption negatively impacts sustainable development due to adjustment costs from routine-task substitution, labour market rigidities, and skill mismatches.
Short-run ARDL coefficient estimates reported in the paper showing a negative short-run effect of AI adoption on development; interpretive explanation attributing causes to adjustment costs, rigidities, and mismatches.
An analysis of LL144 audit reports reveals demographic missingness ranging from under 3% to over 50%, which reduces the applicant pool used for fairness calculation and undermines the metrics.
Empirical analysis of LL144 audit reports reported in the paper (specific sample size not given in the excerpt); quantitative range for missingness reported as 'under 3% to over 50%'.
Total compensation declines persistently in the short and medium run following AI adoption.
Panel local projections indicating persistent declines in total compensation associated with higher establishment-level shares of AI-skill job postings (13 industries, 2017-2025).
Employment declines persistently in the short and medium run following AI adoption.
Panel local projection results showing persistent negative responses of employment to increases in the share of AI-skill job postings (13 industries, 2017-2025).
AI may influence society broadly via ethical issues, economic inequality, and social adaptation challenges.
Paper lists ethics, economic inequality, and social adaptation as societal-level areas affected by AI (abstract). Presented as thematic concerns reviewed in the paper; no empirical estimates included in the provided text.
AI-driven automation is associated with job loss.
The paper lists automation and job loss among the areas it examines (abstract). The provided text frames job loss as a potential negative ramification but does not report primary empirical estimates or sample sizes.
Translators have functioned as 'invisible teachers' of AI—through the construction of translation memories, post-editing, and quality assessment—without recognition as teachers of models.
Conceptual framing and synthesis of workflow practices (TM construction, post-editing, QA) and their role as supervision for ML; qualitative argument and illustrative examples in the paper. No quantitative sample reported.
Translators' renditions have been bought as deliverables under contract, segmented as technical objects, and processed as 'information analysis' data under copyright law—resulting in the loss of moral, creative, and economic attribution to the translators who produced them.
Comparative reading of contract practices and copyright treatment (legal/contractual analysis across jurisdictions), descriptive examples of how translations are delivered, segmented, and processed; qualitative argumentation in the paper. No quantitative sample reported.