Evidence (4560 claims)
Adoption
5267 claims
Productivity
4560 claims
Governance
4137 claims
Human-AI Collaboration
3103 claims
Labor Markets
2506 claims
Innovation
2354 claims
Org Design
2340 claims
Skills & Training
1945 claims
Inequality
1322 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 378 | 106 | 59 | 455 | 1007 |
| Governance & Regulation | 379 | 176 | 116 | 58 | 739 |
| Research Productivity | 240 | 96 | 34 | 294 | 668 |
| Organizational Efficiency | 370 | 82 | 63 | 35 | 553 |
| Technology Adoption Rate | 296 | 118 | 66 | 29 | 513 |
| Firm Productivity | 277 | 34 | 68 | 10 | 394 |
| AI Safety & Ethics | 117 | 177 | 44 | 24 | 364 |
| Output Quality | 244 | 61 | 23 | 26 | 354 |
| Market Structure | 107 | 123 | 85 | 14 | 334 |
| Decision Quality | 168 | 74 | 37 | 19 | 301 |
| Fiscal & Macroeconomic | 75 | 52 | 32 | 21 | 187 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Skill Acquisition | 89 | 32 | 39 | 9 | 169 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 106 | 12 | 21 | 11 | 151 |
| Consumer Welfare | 70 | 30 | 37 | 7 | 144 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 68 | 31 | 4 | 127 |
| Task Allocation | 75 | 11 | 29 | 6 | 121 |
| Training Effectiveness | 55 | 12 | 12 | 16 | 96 |
| Error Rate | 42 | 48 | 6 | — | 96 |
| Worker Satisfaction | 45 | 32 | 11 | 6 | 94 |
| Task Completion Time | 78 | 5 | 4 | 2 | 89 |
| Wages & Compensation | 46 | 13 | 19 | 5 | 83 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 17 | 9 | 5 | 50 |
| Job Displacement | 5 | 31 | 12 | — | 48 |
| Social Protection | 21 | 10 | 6 | 2 | 39 |
| Developer Productivity | 29 | 3 | 3 | 1 | 36 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Skill Obsolescence | 3 | 19 | 2 | — | 24 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Labor Share of Income | 10 | 4 | 9 | — | 23 |
Productivity
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Indirect employment effects will arise from new industries and platform ecosystems enabled by AI.
Theoretical/qualitative argument and sectoral examples (synthesis); the paper does not report empirical measurement of the magnitude or sample-based evidence of such industry creation.
AI complements labor by raising productivity and increasing demand for high-skill, technology-intensive roles (developers, data scientists, AI specialists, etc.).
Complementarity arguments within labor economics theory and sectoral analysis; no new empirical counts or representative labor market sample described in the paper.
Policy interventions (lifelong learning, reskilling programs, active labor-market policies, social protection) are necessary to manage transitional unemployment and distributional effects.
Policy prescriptions based on theoretical framework and synthesis of prior policy evaluations; the paper recommends these approaches but does not present new impact estimates.
AI indirectly creates employment via platform ecosystems, new industries, and productivity-induced demand expansion.
Economic theory on demand-driven employment effects and literature synthesis of platform and productivity spillovers; cross-sectoral discussion rather than a new empirical estimate.
AI directly creates new occupations and tasks related to AI development, deployment, maintenance, and oversight.
Empirical and conceptual synthesis noting observed emergence of AI-specific roles in labor markets and task-based theory of job creation; no single quantified sample provided.
AI complements high-skill, technology-intensive roles, increasing demand for advanced cognitive, creative, and supervisory skills.
Task-complementarity argument from theory and empirical patterns in literature where technology raises demand for skilled workers; cross-sectoral examples cited conceptually.
Adoption of AI in accounting can raise firm-level productivity via faster close cycles, better control, and improved forecasting, potentially affecting profitability and investment decisions.
Theoretical and literature-based claim; the paper suggests mechanisms but does not present a specified empirical estimation in the abstract.
The paper advocates a complementary (augmenting) view of AI in accounting instead of a pure substitution view.
Argumentative conclusion based on synthesis of reviewed studies and theoretical considerations presented in the paper.
AI adoption changes accountants' roles from data entry and routine processing to analysis, interpretation, and strategic decision support.
Inferred from qualitative literature, surveys, and case studies discussed in the paper rather than from a specified empirical identification strategy.
Documented benefits of AI in accounting include increased efficiency, fewer manual errors, faster close cycles, improved report accuracy, and better fraud/irregularity detection.
Reported from literature and industry reports/case examples cited by the paper; the paper does not provide detailed sample sizes or econometric estimates in the abstract.
AI complements accountants rather than substituting them, raising productivity and shifting accountants' focus toward strategic financial management.
Argument based on literature review and qualitative interpretation of workflow changes (surveys/case studies likely); no randomized or quasi-experimental evidence reported in the abstract.
AI technologies (machine learning, robotic process automation, and advanced analytics) are materially improving accounting by automating repetitive tasks, reducing errors, detecting fraud, and providing predictive insights.
Stated as the paper's main finding and supported by cited literature and industry/case examples; the abstract does not specify an empirical design or sample for causal estimation.
Serious-game DSTs can reduce informational frictions by making model outputs (including AI-based recommendations) more interpretable and actionable, lowering barriers to adoption and improving translation of technical advice into economic behavior.
Conceptual synthesis and illustrative practice examples where visualization and interactivity improved understanding; empirical evidence is limited to qualitative user reports and small demonstrations.
Games can act as front-ends to underlying models and datasets or bridge multiple DSTs, improving interoperability and workflow fit for farmers.
Examples of prototypes and deployed tools that connected game interfaces to models/datasets or multiple DSTs; evidence is case-based and demonstrates feasibility rather than large-scale adoption.
Serious games can explicitly model economic outcomes alongside environmental metrics, showing how mitigation/adaptation actions affect enterprise resilience and income.
Prototype demonstrations and case studies that combined economic models with environmental outputs in game interfaces; economic outcome data in these examples are limited and typically short-term or simulated rather than long-term observed incomes.
Dynamic, scenario-based visual outputs in serious games help users understand trade-offs over time (for example, carbon sequestration versus yields).
Comparative demonstrations and workshop observations where scenario visualization was used to communicate temporal trade-offs; evaluation mostly via self-reported comprehension and qualitative feedback from participants.
Interactive, transparent simulations in games reduce skepticism by letting users explore assumptions and model behavior, thereby building trust in DST recommendations.
Qualitative interviews, user testing in workshops, comparative demonstrations where participants explored model assumptions and reported increased confidence; evidence primarily anecdotal and from small pilots.
Co-design through serious games facilitates participatory design with farmers and stakeholders, producing tools that better match on-farm decision contexts and preferences.
Reports from participatory workshops and co-design sessions, case studies of prototype development with farmer groups; evidence largely qualitative (user feedback, design iterations) and based on small-group engagements.
Serious games—interactive, simulation-based decision support tools—can materially increase farmer uptake of land-use decision support tools (DSTs) needed to meet global net zero targets by enabling co-design, building trust, visualizing outcomes, demonstrating profitability–environment links, and integrating with other tools.
Synthesis of literature and practice examples including case studies and deployed game prototypes used with farmer groups, participatory workshops, and qualitative interviews/surveys. Evidence is primarily from small-scale pilots and demonstrations rather than large randomized trials; sample sizes are heterogeneous and often small or not reported.
AI adoption raises executives' human capital/market value, which contributes to higher compensation.
Mediation tests linking AI application to measures of executive human capital (skills/market value) and linking those measures to higher pay in the reported analyses.
AI adoption increases firm total factor productivity (TFP), and higher TFP is associated with higher executive compensation.
Mechanism analysis reporting that firms with higher AI application have higher estimated TFP, and TFP is positively related to executive pay (mediation tests on the sample).
AI adoption alleviates financing constraints, and this channel contributes to higher executive compensation.
Mediation/mechanism tests in the paper showing AI adoption is associated with reduced financing constraints, and reduced financing constraints are associated with higher executive pay (mediation analysis on the A-share firm panel).
Crises (pandemics, supply shocks) tend to accelerate digital and AI adoption, potentially shortening adjustment time to new technological regimes.
Interpretation of recent historical episodes (e.g., COVID-19) and diffusion literature; qualitative assertion without presented microeconometric identification.
AI and the green transformation function as modern long-wave drivers by improving operational efficiency, enabling new products and services, and reorganizing competitive hierarchies.
Conceptual argument linking general-purpose technology literature to observed/anticipated capabilities of AI and green tech; literature synthesis without original empirical tests.
Schumpeterian cycles are driven by clusters of technological innovations and entrepreneurial activity; AI and green technologies represent contemporary innovation clusters with strong potential for productive disruption.
Application of Schumpeterian theory to contemporary technology trends via literature synthesis and conceptual argument (no empirical quantification provided).
The paper's qualitative framework can be operationalized for economists into measurable constructs such as task-level time use, output quality metrics, billable hours, client satisfaction, wages, and employment composition.
Authors propose next steps and measurement opportunities; suggestion comes from translating interview-derived categories into empirical variables for future work.
Architectural education should integrate AI tool training and algorithmic thinking to align workforce skills with evolving task demands.
Authors' recommendation grounded in interview evidence that students are adopting algorithmic strategies and in the constructed conceptual framework; presented as pedagogical implication.
Algorithmic thinking strategies—procedural, iterative, and prompt-based reasoning—are central to how students engage with GenAI during co-design.
Inductive thematic analysis of student interviews identified recurring descriptions of procedural/iterative prompting and tool orchestration as core practices.
Integrating lived temporality into design and evaluation is necessary to preserve and enhance the qualitative aspects of human life in transhumanist transformation.
Normative/philosophical argument supported by literature synthesis and conceptual reasoning; no empirical demonstration (N/A).
AI/ML methods can reduce reliance on animal models by simulating biology, optimizing experiments, and prioritizing candidate drugs—supporting the 3Rs (Replacement, Reduction, Refinement)—but this is contingent on rigorous validation and ethical oversight.
Conceptual and methodological arguments (Manju V et al.) and cited examples of validated in silico alternatives and experiment‑optimization workflows; no single trial or sample size—recommendation based on synthesis of studies and caveats about validation and regulation.
CDRG‑RSF identified five prognostic genes including UBASH3B, which is associated with reduced NK activation and may mediate drug resistance—making it a candidate therapeutic target.
Feature selection within the CDRG‑RSF model yielded five prognostic genes; UBASH3B shown to correlate with immune suppression (reduced NK activation) and inferred links to drug resistance (associational analyses; functional validation not specified in summary).
PIGRS prognostic model (LASSO + Gradient Boosting Machine ensemble using 15 programmed‑cell‑death immune genes) outperformed most published LUAD prognostic models.
Prognostic modeling using LASSO feature selection followed by GBM ensemble on a 15‑gene panel; comparative benchmarking against published LUAD prognostic models reported superior performance (metrics and external cohort testing referenced).
Multi‑omics integration and consensus clustering (10 methods) in lung adenocarcinoma (LUAD) identified three molecular subtypes (CS1–CS3) with distinct prognoses.
PIGRS study integrated transcriptome, DNA methylation, and somatic mutation data and applied ten clustering algorithms to define molecular subtypes; reported three subtypes with differing survival outcomes (external validation cohorts used).
Data augmentation with Gaussian noise improved DNN performance for small sample cross‑omics training sets.
Cross‑omics study applied Gaussian noise augmentation during DNN training on small paired viral datasets and observed improved model performance and DEA recovery relative to non‑augmented training.
Dynamic Ensemble Selection‑Performance (DES‑P) produced parsimonious, high‑accuracy classifiers within the EPheClass pipeline.
Use of DES‑P for model selection in EPheClass reportedly yielded small, high‑performing ensembles (example: periodontal disease AUC = 0.973 with 13 features).
Applying centred log‑ratio (CLR) transformation and RFE to compositional microbiome data improves model parsimony and supports reproducibility in diagnostic classifiers.
EPheClass preprocessing: CLR to handle compositional 16S data and RFE to reduce feature sets; resulted in small feature panels (e.g., 13 features) with high performance and emphasis on rigorous validation to avoid prior overfitting issues.
The same EPheClass approach produced successful parsimonious classifiers for IBD (26 features) and antibiotic exposure (22 features).
EPheClass applied to additional microbiome outcomes (IBD and antibiotic exposure) with RFE selecting 26 and 22 features respectively; performance described as 'successful' (exact AUCs not provided in summary).
The framework’s emphasis on traceability and IT integration creates rich datasets suitable for econometric evaluation (causal impact on earnings, placement) and for training ML models (curriculum recommendation, skill-gap prediction).
Argument in paper about secondary uses of integrated data (conceptual); no datasets or empirical model training described.
Modelling artefacts (flowcharts/logigrams and algorigrams) can encode repeatable lesson-planning, assessment and audit algorithms.
Paper's modelling artefacts description (conceptual/tools).
Firms and hospitals need differentiated investment and governance strategies by interaction level: integration and workflow redesign for AI-assisted; training and decision-support protocols for AI-augmented; process redesign, liability allocation, and oversight for AI-automated systems.
Prescriptive recommendations derived from cross-case findings (n=4) and the conceptual mapping to innovation management implications.
Different interaction levels produce heterogeneous productivity gains (throughput increases, faster/safer decisions, process cost reductions); economic evaluation should be level-specific.
Theoretical/generalization drawn from observed effects across the four qualitative cases and conceptual analysis linking interaction level to types of productivity gains.
Adoption of healthcare AI is better framed as an evolution toward 'Human+' professionals (complementarity) rather than wholesale replacement of clinicians.
Cross-case interpretive analysis of the four qualitative case studies and theoretical framing with Bolton et al. (2018); presented as the paper's core insight.
AI-automated solutions streamline end-to-end processes (e.g., automated reporting pipelines) while keeping humans in supervisory/exception roles, producing process reconfiguration and efficiency gains and shifting roles toward exception management and governance.
Observed characteristics of the AI-automated case(s) in the qualitative multiple case study (n=4) and synthesized in cross-case comparison.
AI-assisted applications automate highly repetitive tasks (e.g., triage routing, routine image preprocessing), producing increased service availability and throughput while freeing clinician time but requiring oversight and workflow integration.
Empirical observations from one or more of the four qualitative case studies illustrating AI-assisted use-cases; interpreted via the Bolton et al. framework and cross-case comparison.
Policy guidance should target pairing AI diffusion with training, management practices, and organizational reforms to maximize social returns, and evaluations should assess both short-run costs and longer-run productivity trajectories.
Synthesis of evidence that complementarities and contextual factors matter, combined with identified gaps in causal and longitudinal evidence, led to this policy recommendation in the review.
Empirical evidence highlights strong complementarities between AI technologies and human capital (digital skills), organizational practices, and management—models should incorporate these complementarities.
Multiple included studies reported interaction/moderation effects showing higher productivity when AI adoption co-occurs with higher digital skills or supportive management practices; synthesized recommendation follows from findings.
Many digital transformation studies implicate AI and automation as key drivers of observed productivity gains, conditional on complementary factors.
Synthesis of included studies where AI/automation was identified as a contributing technological component correlated with productivity improvements; review notes these effects are conditional on complements like skills and management.
Digital transformation components most consistently tied to productivity gains are technological integration (including automation/AI), process digitization, employee digital skills/training, and analytics/data-driven decision-making.
Synthesis of components extracted from included studies where reported associations between specific digital transformation elements and productivity outcomes were noted across multiple studies.
Phased implementation with middleware/integration layers and hybrid architecture is recommended to balance control, customization, and security.
Paper's implementation recommendation derived from pilot experience and the architecture's trade-offs; recommendation rather than empirically validated strategy in the summary.
AI components (predictive cash-flow analytics, automated compliance checks, risk-scoring) improved automation and decision support within the financial framework.
Paper describes integration of AI for predictive analytics and automation and reports improved automation as a benefit in pilot validation. No quantitative accuracy metrics, model validation details, or sample sizes given in the summary.