Evidence (2332 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 |
Inequality
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Empirical evidence on applications designed to support women’s career development remains limited.
Conclusion drawn from the scoping review: authors searched seven databases + backward/forward citation searching and synthesised identified empirical studies.
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.)
Algorithmic scenario planning is being used for tax avoidance.
Presented in the abstract as an example of algorithmic technologies applied to international tax purposes (scenario planning for tax avoidance); no empirical details provided in the abstract.
Workers with a higher share of standardized routine tasks face more pronounced downward wage pressure.
Subgroup analysis by share of standardized routine tasks in workers' duties showing larger negative wage effects for those with higher routine-task shares.
The task substitution mechanism is the core channel underlying these effects of automation on wage structure.
Mediation/heterogeneity tests reported in the paper showing stronger automation effects where task substitution (standardized routine tasks) is higher; authors interpret this as the primary channel.
Wage growth for occupational groups with high exposure to automation lags markedly behind that of low-exposure groups.
Heterogeneity analysis across occupational exposure groups using CFPS panel data comparing wage growth trajectories for high- vs low-exposure occupations.
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.
By framing AI risk exclusively in cybersecurity terms, the Order constructs an AI-risk universe in which provenance, labor, education, culture, meaning, and the commons are rendered 'not testable' within the policy regime.
Argumentative/theoretical claim backed by textual analysis and the counted absence of relevant terms in the EO.
The Executive Order frames AI risk overwhelmingly through cybersecurity language.
Textual analysis of the EO; supported by the paper's verified word-count analysis showing high frequency of security/cyber terms relative to other domains.
Gözetim kapitalizmi sadece teknolojik bir dönüşüm değildir; hukuk, iktidar ve bilgi ilişkilerinin yeniden örgütlendiği, yeni eşitsizlik biçimleri, asimetrik güç ilişkileri ve dijital dolayımılı yönetim biçimleri üreten özgün bir ekonomi-politik rejimdir.
Genel sonuç/sonuçlandırma çıkarımı; sentezleyici teorik analiz; argument based on mapping between technology, law, and power (no empirical evidence in abstract).
Foucaultcu perspektiften algoritmik yönetimsellik, bireyi yalnızca denetlenen bir özne haline getirmekle kalmayıp, aynı zamanda davranışsal fazlanın üreticisi olan bir veri-nesnesine dönüştürmektedir.
Foucault teorik çerçevesiyle yapılan kavramsal analiz; literatüre dayalı argüman; no empirical sample provided in abstract.
Kişisel verilerin metalaştırılması, Julie E. Cohen’in 'biyopolitik kamusal alan' kavramsallaştırması üzerinden değerlendirildiğinde, kişisel bilgi ekonomik üretim ve davranışsal öngörünün hammaddesi olarak hukuksal dispozitif tarafından yapılandırılmaktadır.
Teorik değerlendirme ve kavramsal çerçeveleme; atıf yapılan literatüre dayanıyor; no empirical testing reported.
Hukuk sistemi veri üretimi, dolaşımı, mülkiyeti ve ticarileştirilmesini kurumsallaştırarak gözetim kapitalizminin kurucu unsurlarından biri haline gelmiştir.
Hukuk teorik analizine dayanan argüman; çalışmada Julie E. Cohen ve Foucault perspektifleriyle hukuksal dispozitif incelenmektedir. No quantitative/legal-empirical dataset cited in abstract.
Bu rejimde davranışsal veriler algoritmik altyapılar aracılığıyla sürekli biçimde çıkarılmakta, işlenmekte ve metalaştırılmaktadır.
Kavramsal/diskurs analizi ve literatüre atıf (Zuboff); no empirical measurement or sample described in abstract.
The benefits of the digital economy are uneven: urban residents gain more than rural residents, widening the urban–rural income gap.
Heterogeneity analysis (urban vs. rural) in the two-way fixed effects panel on 31 provinces (2011–2021) showing larger estimated income effects for urban areas.
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.
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).
Results across 15 experimental runs reveal that elderly female occupants consistently experience the lowest satisfaction in initial rounds.
Empirical experiment results reported in abstract: 15 experimental runs; observed satisfaction distribution across demographic profiles with elderly females lowest initially.
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.
AI-driven efficiency pressures in IT services may compress billable work and alter hiring and wage structures, raising transition risks even for technical workers.
Abstract cites high-reliability sector evidence (Reuters 2026a; Nasscom) to support this sector-specific claim; no sample size provided in abstract.
Labor-market segmentation and digital capability gaps in India create distributional vulnerabilities.
Abstract cites Indian official statistics and household/labor surveys (PLFS, HCES, MoSPI–NSO) and integrates sector evidence; no specific sample size reported in abstract.
Refined exposure measures imply widespread task transformation rather than uniform job destruction, with accelerated skill change as a central risk for vulnerable workers.
Abstract cites labor-market analyses and ILO (2025) as the basis for refined exposure measures and conclusions; no sample size stated in abstract.
Global frameworks warn that uneven readiness may produce a 'Next Great Divergence' between countries.
Cited global reports in abstract (UNDP 2025, WTO 2025, OECD 2026) which are summarized as issuing this warning; no primary data sample size reported in paper abstract.
Persistent adoption gaps among groups suggest unequal access to AI-enabled productivity.
Abstract references global reports (OECD, WEF, UNDP, WTO) and sector evidence indicating adoption gaps; no numerical sample size given.
AI may widen capability inequality—inequalities in access to knowledge, digital infrastructure, computational resources, and organizational adoption—thereby shaping income opportunities and socio-economic security for low-income groups.
Argument presented using the paper's socio-technical political economy framework and validated secondary sources (OECD, ILO, UNDP, WTO, WEF) and official Indian statistics; no direct empirical sample from this paper reported.
These findings challenge the prevailing theory of skill-biased technological change.
Empirical observation that high-skill, high-exposure neighborhoods experienced wage stagnation post-2023 despite continued inflows of high-skilled workers, interpreted in contrast to predictions of skill-biased technological change.
Since 2023, high-exposure neighborhoods have experienced wage stagnation even as they continue to attract high-skilled workers (a 'high-skill trap').
Temporal analysis of job-posting wage signals in Beijing neighborhoods (2018--2024) using the GenAI Exposure Index to compare wage trajectories before and after 2023 between high- and low-exposure neighborhoods.
GenAI exposure is highly concentrated in the city's core districts, deepening the intra-urban AI divide.
Spatial analysis of a neighborhood-level GenAI Exposure Index constructed from 5 million Beijing job postings (2018--2024), where task-level assessments were aggregated across five leading large language models to measure exposure by neighborhood.
Concentrated digital power may hinder inclusive industrialisation (SDG 9) and exacerbate global inequalities (SDG 10).
Argument linking conceptual analysis of digital power concentration to Sustainable Development Goals based on literature and policy interpretation (literature-based reasoning, no empirical measurement provided).
Industrial data systems generate 'participation without power,' a dynamic that particularly affects workers, small and medium enterprises (SMEs), and developing economies.
Theoretical/conceptual framing introduced by the paper and justified via literature review and examples from recent studies (no quantitative sample reported).
Inequality is increasingly shaped by the capacity to control and leverage digital systems rather than merely by access to digital technologies.
Conceptual claim grounded in synthesis of recent literature arguing a shift from access-based digital divide frameworks to control/power-based frameworks (literature review, no primary data reported).
The potential widening of the gender wage gap would operate through existing patterns of gender-based occupational sorting (i.e., because women are concentrated in occupations more exposed to generative AI).
Mechanistic interpretation supported by the combination of descriptive occupational sorting evidence from Swedish administrative data and results from the partial-equilibrium simulations incorporating predicted AI exposure and task complementarity.