Evidence (3231 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5921 claims
Human-AI Collaboration
5192 claims
Org Design
3497 claims
Innovation
3492 claims
Labor Markets
3231 claims
Skills & Training
2608 claims
Inequality
1842 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 738 | 1617 |
| Governance & Regulation | 671 | 334 | 160 | 99 | 1285 |
| Organizational Efficiency | 626 | 147 | 105 | 70 | 955 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 349 | 109 | 48 | 322 | 838 |
| Output Quality | 391 | 121 | 45 | 40 | 597 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 277 | 145 | 63 | 34 | 526 |
| AI Safety & Ethics | 189 | 244 | 59 | 30 | 526 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 106 | 40 | 6 | 188 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 79 | 8 | 1 | 152 |
| Regulatory Compliance | 69 | 66 | 14 | 3 | 152 |
| Training Effectiveness | 82 | 16 | 13 | 18 | 131 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Labor Markets
Remove filter
The paper advances a replicable interdisciplinary synthesis method and provides a simulated dataset and transparent protocols enabling other researchers to adapt the approach.
Methods section detailing systematic literature search protocols (ACM/IEEE/Springer, 2020–2024), inclusion criteria, simulation parameterization for the cross-sectoral dataset (seven industries, 2020–2024), and stated reproducibility materials.
AI adoption is strongly associated with workforce skill transformation (reported correlation r = 0.71).
Correlational analysis reported in the paper using the simulated cross-sectoral dataset that mirrors employment trends across seven industries (Manufacturing, Healthcare, Finance, Education, Transportation, Retail, IT Services) over 2020–2024. This corresponds to sector-year observations (7 sectors × 5 years = 35 observations) and is triangulated with findings from a systematic literature synthesis (ACM, IEEE, Springer publications 2020–2024).
Research priorities include rigorous real-world trials assessing patient outcomes, cost-effectiveness, and labor impacts; comparative studies of integration strategies; measurement of long-run workforce effects; and development of standard metrics and monitoring frameworks.
Explicit recommendations from the narrative review based on identified gaps: scarcity of RCTs, economic analyses, and long-term workforce studies.
Reward shaping at the assignment layer enables an explicit trade-off between diagnostic accuracy and human labor by incorporating penalties for human involvement.
Methodology section describing reward shaping and experimental comparisons showing different accuracy/human-effort trade-offs (results reported in paper; exact experimental details not provided in the summary).
Masked reinforcement learning techniques constrain or mask action spaces, reducing exploration over huge symptom/action spaces.
Paper describes use of masked RL to limit action options during training and execution; used in both assignment and execution layers (methodological claim supported by algorithmic description and experiments).
The upper layer ('master') learns turn-by-turn human–machine assignment using masked reinforcement learning with reward shaping to balance accuracy and human cost.
Methodological description in the paper and empirical results from experiments using masked RL and reward-shaped objectives at the assignment layer (implementation and experimental setup reported; dataset/sample size not specified in summary).
Returns to advanced digital skills vary by firm size/type: the wage return in large Chaebol conglomerates is approximately 18.7%, significantly higher than the ~9.5% return in Small and Medium-sized Enterprises (SMEs), indicating a 'skills–scale' complementarity effect.
Heterogeneity analysis within the extended Mincerian wage regression framework using KLIPS micro-data, comparing estimated returns across firm types (Chaebol vs SMEs). (Sample size and exact model specification not provided in the excerpt.)
Workers with only general digital literacy receive a wage premium of approximately 5.8% (after controlling for education, experience, and demographics).
Same empirical framework: extended Mincerian wage equation on KLIPS micro-data with controls for education, experience, and demographic characteristics. (Sample size not specified in the provided excerpt.)
Workers possessing specialized digital skills (e.g., data analysis, programming, automation control) enjoy a significant wage premium of approximately 14.2% after controlling for years of education, work experience, and demographic characteristics.
Empirical estimation using an extended Mincerian wage equation on micro-data from the Korean Labor and Income Panel Study (KLIPS); models control for years of education, work experience, and demographic covariates. (Sample size not specified in the provided excerpt.)
The model is disciplined using data from the Michigan Survey of Consumers and the Survey of Professional Forecasters, targeting key empirical moments.
Calibration/estimation strategy described in the paper: parameters are chosen to match moments from the Michigan Survey of Consumers and SPF (targeted empirical moments). Specific moments and calibration targets are reported in the paper.
I develop a search-and-matching model with sticky wages and endogenous separations.
Theoretical/model contribution: construction and analysis of a calibrated search-and-matching framework that incorporates wage stickiness and endogenous separation decisions.
Workers and firms face information frictions about the aggregate state of the economy (modeled explicitly).
Assumption and mechanism built into the paper's theoretical framework: a search-and-matching model with information frictions for both sides of the market (model specification).
Households form dispersed, backward-looking expectations about macroeconomic conditions.
Survey evidence from the Michigan Survey of Consumers showing dispersion in individual expectations and patterns consistent with backward-looking (slow/updating) belief formation about macro variables; exact sample sizes and empirical specifications are provided in the paper (not in the summary).
DARE posits that responsible AI deployment requires the simultaneous and integrated development of Digital readiness, Administrative governance, Resilience & ethics, and Economic equity.
Descriptive claim about the framework's components as reported in the abstract (conceptual proposition).
This paper introduces the DARE Framework, a holistic, four-dimensional model for national AI strategy and international cooperation.
Factual description of paper content in abstract — the framework is introduced by the authors (conceptual/model contribution).
AI tools—ranging from machine learning algorithms in inventory management to natural language processing in customer engagement—are applied in micro‑enterprise contexts.
Descriptive synthesis from included articles reporting specific AI applications (ML for inventory management; NLP for customer engagement) across the reviewed literature.
Global efforts toward establishing shared norms and multilateral cooperation are underway through initiatives led by the United Nations, OECD, UNESCO, and G7.
Qualitative document review identifying initiatives and normative efforts by multilateral organizations (organizations named; specific initiatives referenced qualitatively but not enumerated as a dataset).
The study builds and calibrates an integrated system dynamics model that connects demographics, labor supply, economic output, and public finance.
Method: development and calibration of a system dynamics model using official statistics for demographics, labor, output, and fiscal variables (model structure and calibration described in paper).
The paper ends with policy implications and recommends periodic evaluation and the integration of AI-related risks into financial governance.
Policy recommendations section in the paper advocating for periodic evaluation and AI-risk integration into financial governance (method: prescriptive/policy analysis based on review findings).
Specialized SDE services that require further study are grouped and highlighted.
Section of the paper grouping and highlighting specialized services for future research (method: expert-driven identification from review; no quantitative prioritization stated).
We introduce a concise conceptual model of a 'shadow' project for designing SDE products or services, detailing participant roles and project composition.
Presentation of a conceptual model within the paper (method: model construction and descriptive exposition; no empirical testing/sample).
The paper proposes a clear classification of criminally oriented products and services in the SDE.
Taxonomy/classification produced in the paper (method: conceptual taxonomy from literature and analysis; no quantitative validation reported).
We identify a structured set of labor‑market roles within the SDE model.
Analytical identification and description of roles within the paper (method: conceptual modeling and qualitative role-mapping; sample size N/A).
We propose an integrated definition of the shadow digital economy that synthesizes technical and economic definitions.
Conceptual analysis and literature synthesis in the paper that combines technical and economic definitions into a single integrated definition (method: review/synthesis; no numeric sample).
General US employment for prime age workers (age 25–54) is currently high (~80%).
Paper cites a current employment rate of 80% for prime-age workers; likely based on national labor statistics though the exact data source and year are not specified in the excerpt.
The growth effect of AI exhibits industry heterogeneity: high‑tech manufacturing industries benefit more significantly.
Heterogeneity/subgroup regressions on the 2003–2017 Chinese industry panel showing larger estimated AI effects in high‑tech manufacturing sectors.
The positive effect of AI on industry growth increases over time.
Dynamic/DID analysis across the 2003–2017 panel showing that the estimated treatment effect grows larger in later periods.
The industry growth rate of the treatment group (industries with intensive AI application or high AI patent concentration) is significantly higher than that of the control group.
DID comparison between treatment and control industry groups in the China 2003–2017 panel, where treatment is defined by intensive AI application or AI patent concentration.
AI technology innovation has a significant positive impact on economic growth.
Industry panel data for Chinese industries from 2003 to 2017 analyzed using a differences-in-differences (DID) approach; main specification estimates effect of AI-related innovation on economic growth.
The weeder was equipped with a Raspberry Pi microcontroller and a camera module to detect crops and weeds in real-time, enabling autonomous operation.
Design description in the paper: hardware integration of Raspberry Pi and camera module for real-time detection (method: system design and implementation). No sample size or quantitative test data reported for detection accuracy in the provided summary.
Platform work accounts for 12.8% of labor income for participants in the studied sample.
Earnings and income calculations using platform transaction records combined with labor force survey and administrative income data for the 24-country sample (2015–2025).
Platform-mediated gig work has grown to represent 4.2% of total employment across 24 OECD countries (2015–2025).
Aggregate analysis of administrative data, national labor force surveys, and platform transaction records covering 24 OECD countries over the 2015–2025 period.
The study reframes AI as an augmentation mechanism rather than a substitute for managerial judgment and extends organizational decision theory to account for socio-technical decision systems.
Theoretical contribution asserted by the paper based on its literature synthesis and conceptual development (claim about extension of theory rather than empirical test).
The paper develops an integrative conceptual framework that explains how human judgment, algorithmic intelligence, and organizational context interact to shape decision quality and organizational outcomes.
Author-constructed conceptual framework based on synthesized literature across decision sciences, management, and information systems (framework described as output of the meta-analysis; no empirical validation reported in abstract).
The model was prompted to suggest jobs to 24 simulated candidate profiles balanced in terms of gender, age, experience and professional field.
Methods reported in the paper: experimental prompting of GPT-5 with N=24 simulated profiles, balanced across specified attributes.
This study evaluates how a state-of-the-art generative model (GPT-5) suggests occupations based on gender and work experience background for under-35-year-old Italian graduates.
Study design described in the paper: targeted population (under-35 Italian graduates), model used (GPT-5) and evaluation focus (occupation suggestions).
Common AI applications in accounting include transaction automation, invoice processing, reconciliations, fraud detection, anomaly detection, automated financial reporting, and predictive forecasting.
Descriptive listing drawn from academic and industry sources/case studies summarized in the paper.
Two regimes emerge: an inequality-increasing regime when AI is proprietary (concentrated control), rents concentrate because firms capture most gains (low ξ), and complementary assets are concentrated.
Model regime characterization and calibrated simulations showing rising firm profits and aggregate inequality under proprietary-AI assumptions and low rent-sharing elasticity.
Generative AI shifts economic value toward concentrated complementary assets (firm-level capital, proprietary data/algorithms), increasing firm profits and rents captured by asset owners.
Model results from a task-based framework with heterogeneous firms and complementary assets; calibration via MSM to six empirical moments; counterfactuals show increased profit shares when AI confers advantages to firms owning complementary assets.
Structural breaks in patenting dynamics are concentrated after 2010, consistent with an inflection in AI diffusion and commercialization.
Application of structural-break detection methods to patent filing time series (1980–2019) across domains; reported concentration of detected breakpoints after 2010. (Paper reports timing and clustering of breaks; exact statistical tests not enumerated in the summary.)
Patenting in AI-enhanced robotics experienced a sharp acceleration beginning in the early 2010s.
Observed marked upturn in the AI-enhanced robotics patent time series from the early 2010s onward (patent filings 1980–2019). Structural break tests applied to the time series identify an acceleration concentrated after 2010.
A dynamic Occupational AI Exposure Score (OAIES) that uses LLMs plus occupational task data can estimate time-varying, task-level AI exposure for occupations and workers.
Paper describes a concrete construction algorithm (task decomposition from O*NET/task inventories, LLM-based capability mapping, augmentation vs automation weighting, diffusion/adoption dynamics, and calibration to observed employment/wage/gross-flow changes). This is a proposed method rather than an applied/validated implementation.
From interview-based evidence the authors constructed a conceptual framework that integrates empirical insights with existing theories to explain how human–AI interaction alters design cognition.
Synthesis of qualitative interview findings with literature on creative cognition and design thinking; framework presented as an output of the study (framework construction described in paper).
A Random Survival Forest built on curated cancer‑death‑related genes (CDRG‑RSF) achieved the best long‑term prognostic performance among 14 tested ML algorithms for pancreatic cancer, with 3‑ and 5‑year AUCs > 0.7.
Comparison of 14 ML survival algorithms on curated prognostic genes; Random Survival Forest (CDRG‑RSF) reported superior 3‑ and 5‑year AUCs exceeding 0.7 (exact sample sizes/cohort details not provided in summary).
Experimental knockdown of PSME3 reduced proliferation and invasion and increased apoptosis in LUAD cells, implicating the PI3K/AKT/Bcl‑2 pathway as a mediator.
Functional assays (gene knockdown experiments) reported in the PIGRS study showing decreased proliferation/invasion and increased apoptosis after PSME3 knockdown, with pathway analyses implicating PI3K/AKT/Bcl‑2.
Deep neural networks (DNNs) better captured cross‑study differential expression (DEA) signals when predicting miRNA from mRNA than sparse linear models (LASSO); for HIV the cross‑study log2 fold‑change (log2FC) correlation was approximately R ≈ 0.59 for the DNN approach.
Analysis on seven paired viral infection datasets (including WNV and HIV); compared DNNs vs. LASSO for mRNA→miRNA prediction; reported cross‑study log2FC correlation R ≈ 0.59 for HIV for the DNNs. Methods included differential expression signal recovery across studies.
An AI‑powered pipeline (EPheClass) produced a parsimonious saliva microbiome classifier for periodontal disease with AUC = 0.973 using 13 features.
EPheClass pipeline using ensemble ML (kNN, RF, SVM, XGBoost, MLP), centred log‑ratio (CLR) transform and Recursive Feature Elimination (RFE); reported performance AUC = 0.973 for periodontal disease model with 13 features (sample size not specified in summary).
Higher job performance is positively associated with greater employee retention.
PLS-SEM analysis, N = 350. Reported direct path: Performance → Retention, β = 0.348, p < 0.001.
The paper identifies gaps and recommends that economists conduct randomized evaluations and quasi-experimental studies to estimate causal effects of interventions (hands-on labs, instructor training, compute subsidies) on competencies and earnings.
Policy and research agenda section of the paper arguing for randomized/quasi-experimental methods; no such causal interventions were implemented in this study.
The study conducted a cross-sectional online survey of more than 600 higher-education students and educators from multiple world regions.
Cross-sectional online survey; sample size reported as >600 participants; recruitment targeted a mix of disciplines and institution types; survey mapped to UNESCO 2024 AI competency frameworks.