Evidence (8625 claims)
Adoption
8625 claims
Productivity
7686 claims
Governance
6917 claims
Human-AI Collaboration
6574 claims
Org Design
4189 claims
Innovation
4131 claims
Labor Markets
3588 claims
Skills & Training
2985 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 761 | 200 | 101 | 904 | 2020 |
| Governance & Regulation | 829 | 400 | 191 | 122 | 1566 |
| Organizational Efficiency | 784 | 193 | 125 | 84 | 1197 |
| Technology Adoption Rate | 637 | 236 | 124 | 97 | 1103 |
| Research Productivity | 431 | 131 | 58 | 340 | 972 |
| Output Quality | 481 | 183 | 59 | 47 | 770 |
| Decision Quality | 332 | 177 | 82 | 49 | 647 |
| Firm Productivity | 439 | 57 | 88 | 20 | 610 |
| AI Safety & Ethics | 218 | 279 | 66 | 33 | 602 |
| Market Structure | 181 | 170 | 123 | 24 | 503 |
| Task Allocation | 214 | 64 | 72 | 33 | 388 |
| Skill Acquisition | 174 | 62 | 62 | 17 | 315 |
| Innovation Output | 204 | 27 | 45 | 18 | 295 |
| Employment Level | 105 | 54 | 108 | 13 | 282 |
| Fiscal & Macroeconomic | 132 | 69 | 43 | 26 | 277 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 154 | 48 | 26 | 3 | 231 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 123 | 50 | 6 | 223 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 71 | 92 | 10 | 2 | 175 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 58 | 56 | 26 | 13 | 156 |
| Training Effectiveness | 96 | 21 | 14 | 19 | 152 |
| Wages & Compensation | 77 | 37 | 25 | 6 | 145 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 81 | 21 | 1 | 115 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 47 | 6 | 1 | 59 |
| Social Protection | 28 | 16 | 8 | 2 | 54 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Adoption
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We propose Shallow-RHS, an asymmetric link-prediction architecture in which the left-hand side (LHS) device tower leverages temporally valid watch-history message passing to capture collaborative signals, while the right-hand side (RHS) content tower is intentionally shallow and encodes content solely from intrinsic features.
Model architecture description in paper (design specification; no numeric evaluation included in excerpt).
We formulate cold-start recommendation as an inductive graph-completion problem on a temporal bipartite device-content graph.
Methodological framing presented in the paper (problem formulation).
In Tubi's production retrieval system, new content must be assigned a standalone embedding immediately, and the model must also produce device embeddings suitable for approximate nearest-neighbor retrieval.
Description of production serving constraints in Tubi stated in paper (system design / operational constraint).
In neither unit did internal control mechanisms identify any information-security incident, sensitive-data leakage, or formal compliance challenge from external oversight bodies during the period examined.
Author reports absence of recorded incidents in internal control mechanisms and no external oversight challenges for both units over the study period; based on internal records and SEI-GDF auditable indicators.
The aggregate Stanford HAI AI Vibrancy Score shows no significant within-country effect on tourism’s direct GDP share after controlling for macroeconomic factors.
Fixed-effects estimation with clustered standard errors on panel data from 33 countries (2017–2023); reported coefficient β = 0.061, p = 0.622, with macroeconomic controls.
The study integrates ICT4D, socio-technical systems theory, and the capability approach as its theoretical framing.
Methodological/theoretical statement in the paper describing the integrative framework used for analysis.
While grounded in the DRC, the findings offer broader insights into AI adoption dynamics across informal economies in Sub-Saharan Africa and beyond.
Authors' claim of broader relevance/generalizability based on the DRC case study and theoretical framing.
AI adoption in the DRC emerges through hybrid socio-technical interactions between bottom-up youth innovation and weakly coordinated institutional frameworks, rather than following policy-led or infrastructure-first trajectories.
Theoretical integration (ICT4D, socio-technical systems, capability approach) and qualitative interview evidence used to characterize observed adoption pathways.
The article introduces 'compressed professionalization', defined as the accelerated acquisition and immediate market enactment of professional-level digital capabilities outside formal institutional pathways.
Conceptual/theoretical contribution presented and defined in the paper, supported by illustrative field observations from the interviews.
The study drew on 125 semi-structured interviews conducted in Kinshasa, Lubumbashi, and Goma.
Primary qualitative fieldwork reported in the paper: 125 semi-structured interviews across three DRC cities (Kinshasa, Lubumbashi, Goma).
We scored over 2.1 million twin responses on 500 participants and 183 held-out questions.
Reported evaluation counts in the paper: 2.1M responses, 500 participants, 183 held-out questions.
The construction-method grid covers three open-weight LLMs, five cumulative information depths ranked by normalized Shannon entropy, two embedding methods, and two reasoning modes.
Paper's experimental design specification (methods section).
We construct detailed individual-level twins from the German Socio-Economic Panel (SOEP) and evaluate them across a 3 × 5 × 2 × 2 construction-method grid.
Methodological description of the study: experimental construction and evaluation on SOEP data.
There is no evidence of improved win rates for AI-flagged complaints; AI-flagged complaints are more likely to be dismissed and to terminate at earlier procedural phases.
Outcome analysis linking AI-flag status to litigation outcomes (win rates, dismissal rates, termination phase) using case metadata.
The empirical analysis is based on panel data of new energy vehicle firms in the Yangtze River Delta from 2001 to 2023.
Dataset description provided in the paper's abstract/introduction indicating the time span and regional coverage.
R&D expenditure does not constitute a significant mediating channel between artificial intelligence and firms' new quality productive forces.
Mediation analysis using the panel data and constructed indicators; reported nonsignificant mediation effect of R&D expenditure (no sample size or statistics reported in excerpt).
The study developed a manufacturing value chain resilience (MVCR) index system based on three dimensions: Readiness, Response, and Recovery, using the CSMAR database.
Methodological description: construction of MVCR index using CSMAR microdata and a three-dimension framework (Readiness, Response, Recovery).
The study constructed indices of industrial robot application at the enterprise-industry-year level by matching industry-level industrial robot data published by the IFR with microdata from Chinese A-share listed companies.
Methodological description in the paper: matching IFR industry-level industrial robot data to microdata from Chinese A-share listed firms to build enterprise-industry-year robot-application indices.
The study uses listed companies in China's manufacturing industry from 2010 to 2023 as the research sample.
Authors explicitly state the empirical sample: listed manufacturing firms in China covering 2010–2023.
The positive relationship between BDTA and CEE remains robust after a series of robustness tests and endogeneity tests.
Authors state they conducted robustness checks and endogeneity tests (unspecified in the summary) and report that the main regression results remain robust.
Brain privacy has both personal and social attributes; its protection therefore implicates individual interests and technological development.
Normative/legal argumentation and conceptual analysis presented in the paper (no empirical data reported).
Greater frontier-level compute does not consistently translate to better performance.
Empirical observation in the paper's findings: increasing compute capacity at the Pareto frontier did not uniformly improve task performance across evaluated tasks.
The audit samples 2,000 runs over a design space of 10 personas x 8 prompts x 3 model configurations x N=10 reps, with the two OpenAI cells at full 8-prompt coverage and the Anthropic sonnet-4.6 / low cell at 4-prompt coverage.
Stated audit design and sample counts in paper (method section describing factorial design and coverage of model/prompt cells).
The paper evaluates the proposed architecture using the outcome metric 'time-to-insight'.
Methodological statement in the paper listing evaluation metrics.
The paper evaluates the proposed architecture using the outcome metric 'time-to-find'.
Methodological statement in the paper listing evaluation metrics.
The paper evaluates the proposed architecture using the outcome metric 'data product adoption'.
Methodological statement in the paper listing evaluation metrics.
We ran 24 matches pairing 23 expert humans with 16 AI agents, capturing 387 delegation and 1440 adoption decisions.
Author-reported experimental setup and counts from the study (24 matches; 23 human experts; 16 AI agents; counts of delegation and adoption decisions).
Because all observations come from a single practitioner, the inferential statistics are exploratory and hypothesis-generating rather than confirmatory; portability across the full portfolio awaits multi-practitioner replication.
Explicit limitation stated in the paper about the single-practitioner design and its implications for inference.
The framework is illustrated with an accounts-payable simulation and a companion spreadsheet.
Empirical illustration: the paper includes (or accompanies) an accounts-payable simulation and a spreadsheet to demonstrate the model and estimation approach.
The note starts from a compact dashboard expression, expands it into a fuller structural model, defines all variables and parameters, and shows how each cost category can be estimated from operational data.
Methodological description in the paper: construction of dashboard, expansion to structural model, full variable/parameter definitions, and stated procedures for estimating cost categories from operational data; accompanied by worked examples.
Agentic Technical Debt is a stock of accumulated design and governance liability.
Definition provided in the paper as part of the conceptual framework that labels Agentic Technical Debt as a stock (accumulated) liability tied to design and governance.
This note develops a formal and managerially usable model that distinguishes Agentic Technical Debt from Stochastic Tax.
Author states development of a formal, managerially usable model and explicit distinction between the two constructs; supported by model construction in the paper (structural model and dashboard).
Agentic AI systems combine probabilistic reasoning with delegated action through tools, context, memory, orchestration, and external workflow integration.
Conceptual/definitional statement in the paper; presented as the working characterization of 'Agentic AI systems' within the model specification.
The paper proposes a policy framework consisting of six groups of solutions for Vietnam to both promote AI development and control risks in the digital age.
Declared in abstract: the paper presents a six-group policy framework for Vietnam; the framework itself is the paper's output (proposal), not empirically tested in the paper.
This study employs document synthesis and comparative analysis of international policies.
Methodological statement in the paper abstract describing the research approach; no sample size specified beyond document sources.
The rise of artificial intelligence (AI) is shaping a new Agent Economy (AE), in which autonomous AI agents represent humans in performing a wide range of complex tasks.
Statement in paper abstract/intro (conceptual definition); no empirical data or sample reported.
The study contributes a taxonomy of AI workforce impact, a Workforce Resilience Readiness Score (WRRS), an AI Workforce Trust Index (AWTI), an Ethical Automation Boundary concept, and a pilot empirical validation design.
Declared methodological and conceptual contributions in the paper (these are presented as deliverables of the study; no validated results reported in the excerpt).
The International Labour Organization's 2025 update highlights the need to assess the exposure of generative AI at the task level using task data, expert input, and AI model predictions.
Reference to ILO 2025 update recommendation described in the paper (policy/technical guidance rather than primary empirical data in the excerpt).
A path analysis was used to trace structural relationships between HR quality, effectiveness perceptions, and AI readiness.
Paper reports a path analysis linking composite HR quality indices, perceived HR effectiveness, and AI readiness measures; uses same survey sample.
A binary logistic regression modelling active AI adoption was estimated with McFadden R² = 0.032.
Reported logistic regression model fit (McFadden R² = 0.032) for AI adoption outcome using the survey data.
An OLS regression was estimated explaining perceived HR effectiveness with R² = 0.446.
Reported OLS model fit statistics in the paper (R-squared = 0.446); model explains perceived HR effectiveness using survey data.
Constructed and validated a composite index of external HR quality factors with Cronbach's α = 0.959.
Measurement validation reported in the paper; Cronbach's alpha reported for external HR factors.
Constructed and validated a composite index of internal HR quality factors with Cronbach's α = 0.924.
Measurement validation reported in the paper; Cronbach's alpha reported for internal HR factors.
A large-scale empirical survey of 12,562 public servants was conducted in June 2025 in Kazakhstan.
Statement in paper specifying survey sample and date; sample of public servants N = 12,562, June 2025.
Identification limits prevent a strict causal claim; the paper outlines an agenda for cleaner tests.
Authors' explicit caveat in the abstract noting limits to identification and stating they outline future cleaner tests.
The analysis exploits the staggered rollout of Claude Code across GitHub between May 2025 and January 2026, using a panel of 5,838 developers observed monthly over 28 months, with treatment defined by a developer's first Claude-co-authored commit and not-yet-treated developers as controls, and estimates obtained via the doubly robust Callaway and Sant'Anna (2021) estimator.
Methods and data description as stated in the abstract: staggered rollout timing, sample size (5,838), observation window (28 months), treatment definition (first Claude-co-authored commit), estimator (Callaway & Sant'Anna 2021).
Results are robust to two stricter activity filters.
Robustness checks reported in the paper applying two stricter activity filters to the sample; claim refers to consistency of estimated effects under these alternate sample definitions.
The analysis is structured across past, present, and future phases using an integrative socio-technical political economy framework and validated secondary sources (OECD, ILO, UNDP, WTO, WEF) alongside official Indian statistics and sector evidence.
Methodological claim stated in abstract describing the approach and data sources used in the paper (OECD, ILO, UNDP, WTO, WEF, MoSPI/NSO, PLFS, HCES, Reuters, Nasscom).
We analyzed over 1.5M assets and 128K agents in EvoMap.
Descriptive dataset statement in the paper reporting the scope of the empirical analysis (assets and agents counts).
We conducted a global large-scale randomized field experiment, delivering customized LLM-generated feedback for over 31,000 arXiv preprints across 150 fields and more than 45,000 researchers from 133 geographic regions.
Statement in paper describing experimental design and scale: randomized field experiment; sample described as >31,000 preprints, >45,000 researchers, 150 fields, 133 regions.