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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AI should be understood as a productive rentier asset whose returns derive from constructed scarcity and access control rather than from commodity exchange.
Conceptual/theoretical framing based on political economy and sociological analysis (argumentative, no empirical sample reported).
Experts in the study assign a 14% probability to 'rapid-progress' scenarios characterized by substantial GDP growth, declining labor force participation, and accelerating wealth inequality.
Result from the 2025 forecasting study of experts (69 economists + 52 AI experts), reporting a probability estimate (14%) for a named scenario with specified macroeconomic and labor-market features.
Developed economies leverage educational capital to mitigate the adverse inequality effects of AI adoption.
Reported interaction/moderation findings from OLS and Random Forest analyses on the World Bank/OECD dataset showing weaker or offset association between AI adoption and Gini in higher-education / higher-development country groups.
There is a substantial lag in the adoption of state-of-the-art AI techniques in RERS research.
Synthesis of methodological findings from the 59-study review, including low deep-learning usage (15%) and absence of state-of-the-art XAI/TL implementations.
Fairness auditing in RERS research is limited despite documented discrimination risks in housing markets.
Assessment of evaluation and ethical practices across the 59 reviewed studies; authors note few studies perform fairness auditing and cite broader literature on discrimination risks in housing.
The literature is dominated by residential property studies (91% of reviewed works).
Domain/topic classification of the 59 studies in the review; authors report 91% focus on residential properties.
Research is geographically concentrated in Asia (56% of reviewed studies).
Geographic coding of study origins in the systematic review; authors report 56% of studies originate from Asia (n=59 total studies).
A reliance on proprietary datasets is pervasive: 80% of reviewed studies use proprietary data.
Reported percentage (80%) derived from categorization of dataset types across the 59 reviewed studies.
No reviewed work implements state-of-the-art post hoc explainable AI (XAI) or transfer learning (TL) frameworks.
Systematic review of methods reported across 59 studies; authors state absence of implementations of state-of-the-art post hoc XAI or TL.
Deep learning is employed in 15% of reviewed RERS studies.
Count/percentage reported from the systematic review of 59 studies; percentage (15%) directly reported in paper.
The effectiveness of prompt injection rapidly diminishes as more candidates inject, collapsing when manipulation becomes widespread.
Controlled experiments that vary the share of candidates performing prompt injection and observe changes in manipulation effectiveness; exact sample size not provided in the abstract.
There is a global disparity in data centre infrastructure (concentrations favouring some regions over others).
Analysis drawing on external data sources cited in the paper illustrating geographic distribution of data centre infrastructure.
Data workers in Kenya report direct employment by big tech corporations and exposure to graphic content.
Qualitative interviews / responses from data workers in Kenya collected and reported in the paper.
Hyper-datafication systematically redistributes labour risks and representational harms toward the Global South.
Qualitative responses from data workers in Kenya describing labour conditions and exposure; analysis of language data representation; external data on global data centre infrastructure and geography.
Hyper-datafication drives substantial and growing environmental costs.
Quantitative analysis of dataset growth and estimated storage-related energy consumption and carbon footprint across the analysed Hugging Face datasets (≈550k); modelled storage and emissions impacts.
These results demonstrate how people's decision-making processes can be insufficient for overseeing AI in high-stakes domains.
Synthesis/interpretation of experimental findings (longer viewing when no AI, small increases in selection probability with more time for non-recommended candidates, IAT effects) to argue that human decision processes may not adequately supervise biased AI in high-stakes settings. This is an interpretive/concluding claim based on the experiment; not a direct empirical measure. Sample size not stated in the excerpt.
Policy asymmetries, digital literacy gaps, and regional inequalities deepen digital divides and impede inclusive development.
Policy analysis and comparative case studies documenting how policy differences, literacy, and regional disparities affect digital inclusion; China used as a focal example. No quantitative sample sizes or causal estimates given in summary.
Agriculture remains digitally marginalized due to infrastructural and institutional deficits.
Comparative case studies and sectoral data showing lower digital adoption in agriculture; qualitative policy analysis identifies infrastructure and institutional shortcomings. No sample size or quantified adoption metrics provided in summary.
The essay introduces the concept of a 'vouching gap' to describe a growing divide between students who graduate with credible advocates willing to stake their reputations on their behalf and those who do not.
Conceptual contribution defined in the essay and motivated by social capital theory and mentoring research; no empirical quantification or sample provided.
Automation of student work and candidate screening will widen existing inequalities between students.
Theoretical claim in the essay linking AI-driven automation to differential outcomes across students, motivated by social capital and mentoring literature; no empirical data or sample reported.
This automation threatens to hollow out the value of a university degree.
Argument presented in the essay, grounded in social capital theory and mentoring research; no empirical test or sample size reported.
A 'critical transmission path' can occur in which AI-induced productivity gains are weakly transmitted to households and may generate absorption tension.
Conceptual framework / theoretical argument in the review (no empirical sample reported).
Productivity gains from AI do not automatically translate into broadly distributed welfare or into output fully absorbed by market demand.
Conceptual review / theoretical argument and literature synthesis presented in the paper (no empirical sample reported).
The root causes of these problems include the disruption of labor relations boundaries by the transformation of the means of production, the exclusion of implicit data labor from distribution rules, the concentration of capital driven by high industry barriers, and social structural constraints on technological dissemination.
Synthesis and causal argumentation grounded in Marx's theory of reproduction; conceptual reasoning rather than empirical testing.
In the consumption phase, high costs lead to service stratification, making it difficult for technological dividends to benefit the general public.
Theoretical/qualitative argument about cost barriers and unequal access to AI-enabled services; no empirical evidence or sample sizes reported.
In the exchange phase, high barriers to entry for technology and capital foster market monopolies.
Analytical claim based on structural characteristics of AI/embodied intelligence industries; no empirical sample or quantitative measures provided in the paper.
In the distribution phase, behavioral data unconsciously generated by workers drives algorithmic iteration yet remains excluded from the distribution system, resulting in hidden data exploitation.
Theoretical argument that worker-generated behavioral data fuels algorithmic development but is not accounted for in value distribution; no empirical data or sample reported.
In the production stage, workers are alienated into becoming data producers.
Conceptual claim based on Marxian analysis of labor and data extraction; no empirical sample or quantitative evidence presented.
In the production stage, workers are disciplined by algorithms.
Theoretical/qualitative argument in the paper describing algorithmic management and control; no empirical measures or sample provided.
In the production stage, workers lose decision-making power.
Theoretical analysis of production relations using Marxist reproduction framework; qualitative claim without reported empirical data.
Income inequality has the opposite effect (it works against poverty reduction).
Negative and statistically significant coefficient on an income inequality measure in the CS-ARDL estimates for BRICS (2008–2023).
Automation reduces employment-based tax revenue and increases public financial pressure.
Explicit finding reported in paper; derived from the scoping review of existing literature (method: qualitative scoping review following Arksey & O'Malley). No quantitative sample or meta-analysis size reported in the abstract.
Automation often displaces workers without adequate retraining, leading to unemployment and reduced income tax contributions, which worsens income inequality.
Statement in paper's purpose/intro; synthesized from the literature via a qualitative scoping review (framework of Arksey & O'Malley). No primary empirical sample size reported in the abstract.
These findings highlight the risk of bias propagation in AI-assisted writing, calling for fairness-aware design in educational AI tools.
Authors' conclusion and recommendation based on the experimental results described above (N = 123 study showing bias transfer).
Comparing a six-group score spread against a two-run noise difference overstates disparity by approximately 2.4X through statistic arity alone.
Methodological comparison reported in pilot analysis showing that using a six-group spread versus a two-run noise baseline produces ~2.4X larger apparent disparity attributable to arity; based on pilot dataset and statistical comparisons.
We should not train AIs to share those specific value systems (i.e., we should not align AI to aggregated or particular human value sets that may be oppressive or unhealthy).
Normative recommendation offered by the authors as part of their argumentation; presented without empirical quantification in the abstract.
Aligning AI to aggregated human preferences is the wrong target.
Normative/argumentative claim stated in the paper; no empirical sample, presented as the authors' thesis.
These mechanisms produce ethical harms such as accountability deficits, epistemic injustice, labour precarity, and constrained sovereignty.
Reported synthesis finding drawing on the reviewed literature (50 articles) linking the named mechanisms to listed ethical harms.
These dynamics operate through four mechanisms: epistemic templating, governance transfer, infrastructural lock-in, and labour opacity.
The paper reports these four mechanisms as the pathways identified via the critical synthesis of the 50 articles.
The analysis identifies four interrelated dynamics—algorithmic colonialism, data colonialism, platform imperialism, and platform sub-imperialism—through which dependency and domination are reproduced across global and intra-South contexts.
Synthesis of the 50 reviewed peer-reviewed articles; these dynamics are reported as the paper's analytical findings.
AI adoption may reproduce entrenched inequalities in postcolonial contexts.
Critical synthesis (literature review) of 50 peer-reviewed articles from 2019–2025 reported by the paper.
The study identifies specific retention issues including rigid work practices, a predominantly masculine culture, and occurrences of bullying and harassment.
Findings from thematic analysis of 23 interviews using NVivo 13; participants' accounts raised these specific themes as retention-related issues.
Women in UK construction continue to face major retention challenges driven by structural biases that lead to feelings of disrespect, insufficient support, and being undervalued.
Thematic analysis of 23 qualitative interviews with women involved in digitally enabled projects; participants reported experiences and perceptions related to retention and workplace culture.
Women make up less than 15% of the UK construction workforce.
Statement in the paper likely citing national labour/industry statistics or prior literature (not primary data from this study).
One in three Scheduled Tribe (ST) graduates work in farm or elementary occupations untouched by AI.
Occupational distribution from PLFS 2025 after mapping AI-exposure indices; reported share of ST graduates in farm/elementary (AI-unexposed) occupations in the 83,000-employed-graduate sample.
One in four Scheduled Caste (SC) graduates work in farm or elementary occupations untouched by AI.
Occupational distribution from PLFS 2025 after mapping AI-exposure indices; reported share of SC graduates in farm/elementary (AI-unexposed) occupations in the 83,000-employed-graduate sample.
Graduates from the Scheduled Castes and the Scheduled Tribes are 0.24--0.37 standard deviations less exposed than upper-caste graduates within the same district.
Within-district comparisons using three occupational AI-exposure indices mapped to PLFS 2025; reported standardized exposure differences for SC and ST graduates relative to upper-caste graduates in the 83,000-employed-graduate sample.
Reporting on ethics, transparency and governance was inconsistent.
Reported synthesis result from the scoping review noting variability in reporting practices across included studies.
Formal theory use was limited, with only a small minority of studies explicitly drawing on established frameworks.
Authors' assessment of methodological/theoretical characteristics of the included empirical studies in the scoping review.
Fewer studies evaluated individual-facing developmental support, and sustained career outcomes were rarely measured.
Reported gap identified in the scoping review findings summarised in the abstract.