Evidence (7278 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).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9047 claims
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Productivity
8066 claims
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Governance
7278 claims
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Human-AI Collaboration
6912 claims
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Org Design
4439 claims
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Innovation
4359 claims
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Labor Markets
3652 claims
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Skills & Training
3018 claims
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Inequality
2160 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 | 795 | 210 | 105 | 955 | 2131 |
| Governance & Regulation | 886 | 414 | 197 | 126 | 1654 |
| Organizational Efficiency | 826 | 204 | 129 | 87 | 1257 |
| Technology Adoption Rate | 681 | 259 | 128 | 110 | 1189 |
| Research Productivity | 464 | 138 | 65 | 349 | 1028 |
| Output Quality | 503 | 196 | 61 | 53 | 813 |
| Decision Quality | 351 | 180 | 84 | 51 | 673 |
| AI Safety & Ethics | 238 | 288 | 71 | 34 | 637 |
| Firm Productivity | 455 | 58 | 92 | 20 | 631 |
| Market Structure | 186 | 172 | 123 | 25 | 511 |
| Task Allocation | 222 | 70 | 76 | 34 | 407 |
| Innovation Output | 238 | 28 | 48 | 18 | 334 |
| Skill Acquisition | 177 | 62 | 62 | 17 | 318 |
| Employment Level | 107 | 57 | 108 | 13 | 287 |
| Fiscal & Macroeconomic | 135 | 72 | 44 | 26 | 284 |
| Firm Revenue | 172 | 50 | 28 | 5 | 256 |
| Consumer Welfare | 121 | 68 | 45 | 12 | 246 |
| Task Completion Time | 183 | 33 | 10 | 13 | 240 |
| Inequality Measures | 45 | 126 | 50 | 6 | 227 |
| Worker Satisfaction | 95 | 74 | 23 | 12 | 204 |
| Error Rate | 77 | 98 | 11 | 4 | 190 |
| Regulatory Compliance | 84 | 73 | 17 | 7 | 181 |
| Automation Exposure | 61 | 61 | 27 | 14 | 166 |
| Training Effectiveness | 98 | 21 | 14 | 19 | 154 |
| Wages & Compensation | 78 | 37 | 25 | 6 | 146 |
| Developer Productivity | 105 | 18 | 14 | 6 | 144 |
| Team Performance | 87 | 17 | 28 | 10 | 143 |
| Job Displacement | 12 | 83 | 23 | 1 | 119 |
| Hiring & Recruitment | 53 | 8 | 8 | 3 | 72 |
| Social Protection | 39 | 17 | 8 | 2 | 66 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 50 | 6 | 1 | 62 |
| Labor Share of Income | 17 | 20 | 17 | — | 54 |
| Worker Turnover | 15 | 15 | — | 3 | 33 |
| Industry | — | — | — | 1 | 1 |
Governance
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Study methodology: Two online experiments were conducted via the crowdsourcing platform Prolific with sample sizes study 1: n = 325 and study 2: n = 371; participant mean age = 35 years; 55% female.
Methodological and sample description provided in the abstract.
Late disclosure of AI involvement did not improve affective engagement for AI-generated content.
Reported experimental result in the abstract from the two online studies manipulating disclosure timing (early vs. late).
The study was conducted by the Mohammed bin Rashid School of Government’s Future of Government Center, in collaboration with global AI pioneers.
Authorship and collaboration statement in the report.
The report highlights the key findings of a field study covering ten Arab countries to explore the realities and challenges of AI governance.
Report statement describing the geographic scope of the field study (explicitly: ten Arab countries).
The recommendations are based on regional research that included hundreds of leaders active in the AI domains, from the public and private sectors.
Report statement claiming participant base of the underlying research (described as 'hundreds of leaders').
The authors construct a mean-reverting jump-diffusion stochastic process model and conduct Monte Carlo simulations to evaluate hedging efficiency of the proposed futures contracts.
Methodological claim: explicit description of the mathematical model (mean-reverting jump-diffusion) and simulation method (Monte Carlo) used in the paper.
Capital income taxes, worker equity participation, universal basic income, upskilling, and Coasian bargaining cannot eliminate the excess automation.
Model-based policy counterfactuals evaluated in the paper showing these interventions fail to achieve the social optimum in the theoretical framework; no empirical sample.
Wage adjustments and free entry cannot eliminate the excess automation.
Analytical result in the model showing endogenous wage changes and free entry do not restore the socially optimal level of employment; theoretical equilibrium analysis, no empirical data.
We analyze a regional standardized sentiment database (97,719 responses).
Dataset description in the paper specifying the size of the standardized sentiment database.
We analyze a raw Fukui spending database (90,350 records).
Dataset description in the paper specifying the size of the raw Fukui spending database.
The analysis relies on partial least squares path modeling (PLS-PM) to test eight predictions linking technological perceptions, organizational factors, and adoption outcomes.
Author-stated analytical method: PLS-PM; eight predictions tested; uses the survey data described above.
The study uses cross-sectional survey data from 523 human resource professionals and hiring managers representing 184 organizations across multiple industries in the United States.
Author-stated sample description in the paper: cross-sectional survey; 523 HR professionals/hiring managers; 184 organizations; multiple industries; U.S.
The study synthesises findings from 36 peer-reviewed articles published between 2015 and 2025.
Systematic literature synthesis / review of peer-reviewed articles; sample = 36 articles (2015–2025) as stated in the paper.
We construct a multidimensional energy justice index to analyze AI’s net effects, pathways, and institutional dependencies.
Methodological statement: authors create an energy justice index (multidimensional) used as dependent variable in empirical analysis.
This study uses a panel dataset for 30 Chinese provinces from 2008 to 2022.
Statement of dataset coverage in the paper: 30 provinces, years 2008–2022 (panel data).
This study uses a mixed-method research design combining quantitative ROI modelling and cost–benefit analysis, qualitative synthesis of secondary enterprise case studies, and architectural analysis of Azure-native GenAI services.
Explicit methodological description in the abstract of the paper.
Ninety-five high-quality studies were analyzed using principal component analysis and k-means clustering.
Paper states screening produced 95 high-quality studies which were subjected to PCA and k-means clustering for analysis.
A systematic literature review of 450 records from major databases was conducted using PRISMA 2020 guidelines.
Statement in the paper describing methods: systematic literature review using PRISMA 2020; initial search returned 450 records from major databases.
Specification and implementation are available at https://github.com/chelof100/acp-framework-en
Repository URL provided in the specification text; points to the stated implementation and documentation artifacts.
The specification defines more than 62 verifiable requirements and 12 prohibited behaviors.
Quantitative claims stated in the specification about requirement and prohibited-behavior counts.
The v1.13 release includes an OpenAPI 3.1.0 specification for all HTTP endpoints.
Specification/repository statement indicating an OpenAPI 3.1.0 specification is provided for HTTP endpoints.
The v1.13 release includes 51 signed conformance test vectors (Ed25519 + SHA-256).
Repository/specification statement listing 51 signed conformance test vectors and the signature/hash algorithms used.
The v1.13 release includes a Go reference implementation of 22 packages covering all L1-L4 capabilities.
Repository statement describing a Go reference implementation comprising 22 packages and coverage claim for L1-L4.
The v1.13 specification comprises 36 technical documents organized into five conformance levels (L1-L5).
Explicit quantitative statement in the specification/repository describing document count and organization.
The paper presents a formal evolutionary taxonomy of generative AI spanning five eras (1943–present) and analyzes frontier lab dynamics, sovereign AI emergence, and post-training alignment evolution from RLHF through GRPO.
Conceptual taxonomy and historical/organizational analysis provided in the paper. No empirical sample size reported in the excerpt.
The framework extends the Sustainability Index of Han et al. (2025) from hardware-level analysis to ecosystem-level analysis.
Conceptual / methodological extension claimed by the authors referencing Han et al. (2025). No empirical sample size reported in the excerpt.
Classical scaling laws model AI performance as monotonically improving with model size.
Statement about prior literature / modeling assumptions (classical scaling laws). No empirical sample size reported in the excerpt.
The paper derives formal conditions under which the inversion (smaller, orchestrated models outperforming frontier models) holds.
Mathematical derivations and stated sufficient/necessary conditions presented in the paper.
We develop the Institutional Fitness Manifold, a mathematical framework that evaluates AI systems along four dimensions: capability, institutional trust, affordability, and sovereign compliance.
Theoretical/model development presented in the paper (formal definition of the manifold and its four dimensions).
There have been five eras of AI development since 1943, and within the current Generative AI Era there are four distinct epochs, each initiated by a discontinuous event.
Descriptive/historical classification within the paper (counts of eras and epochs; named initiating events such as the transformer and the 'DeepSeek Moment').
The study uses panel data for 30 Chinese provinces from 2013–2022 to measure urban circular economy efficiency (UCEE) with a Super-SBM model including undesirable outputs, track dynamics via the Global Malmquist–Luenberger index, and estimate spatial effects with a spatial Durbin model.
Methodological description in the abstract: explicit statement of data (30 provinces, 2013–2022) and the three methods used (Super-SBM with undesirable outputs, GML index, spatial Durbin model).
Despite fears of mass unemployment, aggregate labor-market data through 2025 show limited labor-market disruption from generative AI.
Review of aggregate employment and labor-market studies and macro-level data through 2025 cited in the brief; methods include analyses of employment statistics and macro labor indicators (no single sample size reported).
We scored rule-breaking and abuse outcomes with an independent rubric-based judge across 28,112 transcript segments from multi-agent governance simulations.
Reported methodology: multi-agent governance simulations with agents in formal governmental roles, outcomes evaluated by an independent rubric-based judge; explicit sample count of 28,112 transcript segments.
A GNN graph is constructed from reasoning embeddings and trading decisions are made using a PPO-DSR policy.
Method description: the paper reports embedding agents' reasoning, building a graph neural network (GNN) from those embeddings, and using a PPO-DSR reinforcement learning policy to trade. Specific GNN/PPO-DSR hyperparameters and architecture are not provided in the excerpt.
Four LLM agents output scores along with reasoning.
Method description: the paper states that four LLM agents produce numeric scores and associated textual reasoning. The number of agents is explicitly given as four; no further architecture or model-family details included in the excerpt.
BlindTrade anonymizes tickers and company names (blindfolding agents by anonymizing all identifiers).
Methodological description in the paper: the system design explicitly replaces tickers and company names with anonymized identifiers. Implementation details and examples not provided in the excerpt.
Data ethics, as a central pillar of digital ethics, emphasizes the responsible use and protection of personal information.
Conceptual/definitional statement in the paper situating data ethics within digital ethics and highlighting protection of personal information as a core concern.
Big data usage is proxied by keyword frequency in firms' annual reports.
Operationalization described in the paper: frequency/count of big-data-related keywords in annual reports used as the proxy for firms' big data application.
The empirical analysis uses a fixed-effects regression approach to measure the impact of big data application on firm value.
Methodological statement in the paper specifying fixed-effects regression as the primary econometric approach.
The study analyzes panel data covering Chinese A-share listed companies from 2007 to 2021.
Description of dataset in the paper: panel of Chinese A-share listed companies spanning the years 2007–2021 (sample period stated).
The analysis extends the dynamic taxation setup of Slavik and Yazici (2014).
Methodological claim: the model and solution approach build on and modify the framework from Slavik and Yazici (2014) (reference to prior theoretical framework rather than empirical data).
We characterize the optimal tax policy in an economy with human manual and cognitive labor, physical capital, and artificial intelligence (AI).
Theoretical/analytical work: the paper develops and analyzes a dynamic general-equilibrium model that includes manual and cognitive human labor, physical capital, and AI. (No empirical sample; model-based characterization.)
The field study used a 44-item questionnaire with 45 participants to measure comprehension, reported behavior change/adoption, and perceptions of volunteer legitimacy.
Methodological description provided in the paper: instrument and sample sizes explicitly reported.
No original quantitative dataset or controlled evaluation is reported in this paper.
Methodological description in the paper stating reliance on prior literature, conceptual analysis, and prescriptive recommendations; paper does not present new experiments.
The paper is a position/normative paper (not an empirical study) that uses conceptual analysis, literature synthesis, and prescriptive roadmaping rather than new quantitative experiments or datasets.
Explicit methodological statement in the paper summarizing genre and methods used; absence of reported original data or controlled evaluations.
There is a need for longitudinal and cross‑country empirical research to measure how hybrid work and AI tools affect promotion rates, network centrality, productivity, privacy harms, trust, and long‑term career trajectories.
Statement of research gaps derived from the paper's methodological approach (conceptual synthesis and secondary case studies) and absence of longitudinal/cross‑cultural primary data.
Robustness checks include mediator tests (costs, tariffs, logistics) and firm‑level subgroup analyses to establish heterogeneous responses and support mechanism claims.
Paper reports robustness strategy involving mediation analysis and subgroup DID estimations across multiple mediator variables and firm size groups using the stated databases.
Empirical identification relies on treating CAFTA as an exogenous shock and applying a difference‑in‑differences (DID) design on firm and customs data from 2000–2014.
Methodological description in the paper: DID strategy with treated vs control comparisons; data sources explicitly listed as the China Industrial Enterprise Database and China Customs Database covering 2000–2014.
Highly Autonomous Cyber-Capable Agents (HACCAs) are AI systems able to plan and execute multi-stage cyber campaigns across the full attack lifecycle with minimal or no human direction.
Conceptual definition provided in the report; constructed via literature review and threat-framework formulation (no empirical sample; definitional/analytic).
Practical recommendations for firms and policymakers include investing in training for AI curation/evaluation/coordination, experimenting with decentralised decision rights and governance safeguards, and monitoring competitive dynamics related to model/platform providers.
Policy and practitioner takeaways explicitly presented in the discussion/implications sections, deriving from the conceptual framework and mapped literature.