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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The repertoire of available positions is being augmented to include roles such as AI translator, AI editor, AI designer, AI content manager, and AI trainer.
Identification of novel job titles that explicitly include 'AI' in the title within the >200-job vacancy dataset collected through 2025.
By 2025, roles encompassing AI expertise include copywriter, social media manager, public relations specialist, and designer, whereas in 2023 these roles were confined to editors and journalists.
Year-by-year breakdown of job titles in the >200 vacancy sample showing the presence of new role types (copywriter, social media manager, PR specialist, designer) with AI requirements in 2025 compared to predominantly editors/journalists in 2023.
The scope of positions requiring AI competence expanded significantly between 2023 and 2025.
Longitudinal comparison of job titles and required skills across the >200 vacancies showing an increase in the variety of roles listing AI competence from 2023 to 2025.
The demand for AI-competent roles is predominantly for full-time employment.
Classification of contract type (full-time vs. part-time/contract) in the >200 job vacancies, with majority labeled as full-time.
Employers increasingly prioritize practical experience with primary tools used for content creation and management.
Content analysis of job-ad required skills showing growing mentions of specific content-creation/management tools across the >200 vacancies sampled (2023–2025).
Employers now prioritize candidates who possess foundational knowledge of AI functionalities and an active interest in the technology.
Thematic coding of job-ad text (requirements sections) from the same sample of >200 vacancies showing recurring language requesting foundational AI knowledge and interest.
Proficiency in AI has transitioned from a supplementary skill to a fundamental competency essential for media professionals.
Content analysis of over 200 media-industry job vacancies referencing AI skills collected across 2023–2025; comparison of job-ad requirement language across years.
LLM-based screening is most vulnerable when manipulation is rare and candidate quality differences are small.
Synthesis of experimental results across conditions varying prevalence of manipulation and magnitude of candidate quality differences; sample size not specified in the abstract.
Prompt injection reliably improves applicant rankings when résumé quality is homogeneous and few candidates inject.
Controlled experiments reported in the paper that vary résumé quality homogeneity and fraction of candidates using prompt injection; exact sample size not stated in the abstract.
Sustainable AI-driven recruitment requires the integration of bias auditing frameworks, explainability mechanisms, human-in-the-loop governance, and continuous regulatory compliance monitoring.
Recommendation synthesized from the systematic review of 34 studies and the paper's analysis of risks and mitigation strategies reported in the literature.
Advanced machine learning techniques, including XGBoost and Random Forest algorithms, can achieve predictive accuracies of up to 96% in employee attrition forecasting and workforce optimization tasks.
Aggregate finding reported in the review, citing empirical results from one or more of the 34 studies that evaluated ML models (XGBoost, Random Forest) for attrition forecasting and workforce optimization.
Organizations increasingly adopt AI-driven recruitment systems to reduce hiring costs, accelerate decision-making processes, and enhance workforce planning capabilities.
Claim reported in the paper based on the systematic review of 34 peer-reviewed studies and domain literature surveyed.
AI improves recruitment efficiency through automated candidate screening, intelligent job matching, workforce analytics, and predictive hiring strategies.
Synthesis claim based on a systematic review of 34 peer-reviewed studies spanning computer science, organizational psychology, human resource management, and legal scholarship.
AI-driven outcomes depend less on the technology itself and more on complementary conditions—human capital formation, digital and data infrastructure, institutional coordination, and governance capacity—that enable effective diffusion.
Thematic synthesis of reviewed literature (2015–2025) highlighting repeated findings that complementarities (human capital, infrastructure, institutions) mediate AI diffusion and impacts.
Adopting the paper's proposed Data PROOFS (provenance, resource awareness, ownership, openness, frugality, standards) could mitigate the environmental, social, and economic costs of large-scale data for AI.
Authors' recommendations/proposals presented in the Discussion/Conclusions as mitigation strategies; normative argument rather than direct empirical test.
The CAR model offers a new theoretical perspective on protecting labor in the digital age and is applicable at both normative and political-legal levels.
Paper's concluding normative argument based on conceptual development and comparative analysis (no empirical implementation or evaluation provided).
A five-dimensional CAR model — consisting of collective data access, algorithmic transparency, collective algorithmic oversight committees, algorithmic collective bargaining agreement (CBA) clauses, and Collective Algorithmic Impact Assessment (CAIA) — can reestablish accountability and the institutional power of collective bargaining in digital work regimes.
Theoretical/model claim based on the paper's conceptual model development and normative argumentation (no empirical validation reported).
This article systematically constructs the concept of Collective Algorithmic Rights (CAR) as a comprehensive theoretical framework capable of capturing the collective outcomes of algorithmic governance.
Methodological claim: conceptual model development approach described in the paper (theoretical construction; no empirical testing reported).
AI could increase the value of human-in-the-loop supervision and strategic planning (i.e., 'soar the worth' of these roles).
Paper argues as a potential outcome that reduced junior–expert productivity gaps would raise the relative value of supervisory and strategic roles; framed as a potential/expected labor-market reallocation rather than a measured fact in the abstract.
The study provides policymakers with a solid empirical foundation for assessing how the diffusion of AI supports inclusive growth and sustainability goals.
Authors' interpretation of their comparative, multi-method cross-sectional findings (factor analysis, GLM, cluster analysis) across EU countries linking AI adoption to economic performance, S&T workforce share, employment, and SDGI.
AI adoption has a weaker but present positive association with sustainability indicators as measured by the Sustainable Development Goals Index (SDGI).
Cross-sectional analysis (factor analysis, general linear models) connecting enterprise-level AI adoption measures to country-level SDGI scores across EU countries; the relationship is described as weaker than for GDP per capita or S&T workforce share; no numeric effect sizes reported in the summary.
AI adoption shows weaker but still present positive relationships with overall employment (total employment) across EU countries.
Cross-sectional general linear model estimations and factor analysis relating enterprise-level AI adoption indicators to total employment across EU countries; the summary states the association is weaker yet present; exact sample size and statistical magnitudes not reported.
AI adoption is associated with a larger share/proportion of highly educated science and technology workers in countries.
Cross-sectional comparative analysis of EU countries using factor analysis and general linear models linking enterprise-level AI adoption measures to the proportion of highly educated science & technology professionals; sample consists of EU countries (exact count not reported).
AI adoption is consistently linked with higher economic performance (GDP per capita) across EU countries.
Cross-sectional analysis across EU countries using exploratory factor analysis and general linear model estimations relating enterprise-level AI adoption indicators to GDP per capita (SD variables: AI adoption, GDP per capita). Sample described as EU countries; exact N not reported in the summary.
Participants who completed an implicit association test (IAT) before resume screening were significantly more likely to evaluate candidates of different races for the same amount of time.
Experimental comparison between participants who completed an IAT prior to screening and those who did not; analysis of viewing time across candidate races showed more equal evaluation time for the IAT group. Statistical significance is asserted in the text; sample size not provided in the excerpt.
People may spend up to 55.6% longer viewing resumes when no AI recommendations are given.
Comparison of resume viewing times between conditions with AI recommendations versus no AI recommendations in the experimental resume-screening task (text reports up to 55.6% longer viewing time when no recommendation is given). Sample size not stated in the provided excerpt.
Spending more time viewing resumes corresponds to candidates' selection chance increasing by 3-4% if they are not recommended.
Experimental analysis of participants' resume-viewing time and selection decisions in a biased AI resume-screening study; comparison conditional on whether AI recommendation was present (text reports a 3–4% increase for non-recommended candidates). Sample size not stated in the provided excerpt.
Mechanism analysis provides suggestive evidence that AI improves skill utilization and promotion expectations.
Mechanism/auxiliary analyses in the paper (described as 'suggestive evidence') linking AI diffusion to proxies for skill utilization and promotion expectations within the CLDS framework.
Effects of AI on the wage consequences of educational mismatch vary by occupation: AI mainly benefits overeducated workers in non-manual jobs, where surplus schooling can be effectively absorbed.
Subsample/heterogeneity analysis by occupation (manual vs. non-manual) in CLDS-linked fixed-effects models showing stronger attenuation of overeducation penalty in non-manual occupations under higher AI diffusion.
AI diffusion reduces the wage penalty for overeducated workers.
Interaction models using CLDS data and city-level AI diffusion in fixed-effects specifications showing that greater AI diffusion attenuates the negative wage effect for overeducated workers.
Undereducation is associated with a wage premium.
Same CLDS 2014–2018 microdata and cohort-based educational mismatch measure; estimated via extensive fixed-effects models showing higher wages for undereducated workers.
This study contributes to the accounting literature by positioning AI as a measurable financial determinant rather than a pure technological innovation.
Author's stated contribution in the paper's conclusions/discussion; conceptual claim referencing the study's empirical measurement of AI's association with income.
AI had a significant effect on illustrators' income (b = 0.330, p < 0.05; R² = 7.4%).
Simple linear regression analysis on survey/observational data from 385 illustrators drawn from the Artist's Base community (simple random sampling); reported regression coefficient b = 0.330, p < 0.05, model R² = 7.4%.
Comprehensive regulation is needed that combines competition/access measures, algorithmic explainability, social protection for couriers and measures to prevent platform dependence in remote markets and northern cities of Russia.
Policy recommendation derived from the study's comparative analysis, statistical review, and the regional empirical findings for the Sakha Republic; normative conclusion rather than an experimentally tested intervention.
Russia is characterized by rapid growth of eGrocery and O2O services, an ecosystem role of major digital players, and the formation of a legal framework for the platform economy.
Analysis of Russian statistical data, case analysis of ecosystem players, and review of emerging Russian legal/regulatory acts included in the study.
China has more developed antitrust and algorithmic regulation relative to Russia.
Analysis of regulatory legal acts governing online trade, competition and algorithms in China and Russia; comparative legal/regulatory review presented in the paper.
China is characterized by a larger user base and a higher density of instant retail compared to Russia.
Analysis of statistical data from China and Russia as reported in the comparative section of the study.
The employment-enhancing mechanisms encompass productivity, real income growth, complementary jobs, new jobs and sectors, market expansion and commodification.
Mechanisms listed by the author as explanatory pathways, drawn from the paper's comprehensive theoretical and empirical literature review (no empirical quantification provided in the excerpt).
In countries undergoing intensive automation, there has been a rapid increase in the number and proportion of workers, rather than a decline.
Asserted as an empirical finding derived from the paper's review of studies of countries with intensive automation; the excerpt does not list specific countries, datasets, or sample sizes.
The employment-enhancing effects of new technologies are demonstrated.
Stated as a conclusion based on a 'comprehensive review of theoretical and empirical studies' (no specific studies, sample sizes, or quantitative meta-analytic statistics reported in the excerpt).
Digitalization enables service-sector expansion through fintech and e-commerce.
Empirical sectoral data and comparative case studies highlighting fintech and e-commerce impacts in services; policy analysis situates enabling conditions. No numeric sample size or quantified effect in summary.
Digitalization enhances competitiveness in manufacturing.
Empirical sectoral data and comparative case studies focused on manufacturing; China emphasized as a central case. No explicit sample size or quantified effect reported in the summary.
AI, the Internet of Things (IoT), and platform economies contribute to productivity gains across manufacturing, services, and (to a lesser extent) agriculture in emerging markets, with China as a central case.
Mixed-methods approach combining empirical sectoral data, policy analysis, and comparative case studies; China used as a central case. Sample size/quantitative scope not specified in summary.
Welfare analysis finds the AI shock welfare-improving under complementarity between labor and AI capital.
Model welfare calculations (household utility/welfare measures) under parameterizations that assume complementarity between labor and AI capital; numerical comparisons of welfare before and after the AI shock.
A longevity shock acts as a saving-supply disturbance: it deepens the aggregate capital stock.
Model simulation of an exogenous longevity shock (longer lifespans) in the overlapping-generations GE model, producing higher aggregate capital accumulation.
The AI shock produces a front-loaded output expansion that decays monotonically.
Model-implied output dynamics following an AI technology shock shown in numerical simulations.
An AI technology shock acts as a capital-demand disturbance: it raises all rates of return, most sharply the return to AI capital.
Theoretical dynamic overlapping-generations general equilibrium model with endogenous fertility; numerical simulation of an exogenous AI technology shock that increases returns to capital, with model-implied trajectories of rates of return reported.
The most valuable asset a university can offer students in a post-AI economy is credible endorsement—the capacity of a trusted faculty member, advisor, or other mentor to vouch with specificity for a student's character, competence, and potential.
Normative/analytical claim in the essay based on social capital and mentoring research; presented as the author's recommended institutional response rather than empirically validated evidence.
Demand reallocates toward lower-priced workers in more AI-exposed job categories.
Empirical evidence from Upwork showing post-ChatGPT changes in hiring/demand patterns, with more AI-exposed categories shifting demand toward workers who charge lower prices; inferred via predictive models and difference-in-differences comparisons. (Sample size not reported in abstract.)
In more AI-exposed job categories, the importance of price in predicting labor demand rises.
Same empirical approach as above: Upwork data, text embeddings for worker profiles, computation of the predictive importance of price, and a difference-in-differences design around ChatGPT release comparing more- vs. less-AI-exposed categories. (Sample size not reported in abstract.)