Evidence (2432 claims)
Adoption
5126 claims
Productivity
4409 claims
Governance
4049 claims
Human-AI Collaboration
2954 claims
Labor Markets
2432 claims
Org Design
2273 claims
Innovation
2215 claims
Skills & Training
1902 claims
Inequality
1286 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 369 | 105 | 58 | 432 | 972 |
| Governance & Regulation | 365 | 171 | 113 | 54 | 713 |
| Research Productivity | 229 | 95 | 33 | 294 | 655 |
| Organizational Efficiency | 354 | 82 | 58 | 34 | 531 |
| Technology Adoption Rate | 277 | 115 | 63 | 27 | 486 |
| Firm Productivity | 273 | 33 | 68 | 10 | 389 |
| AI Safety & Ethics | 112 | 177 | 43 | 24 | 358 |
| Output Quality | 228 | 61 | 23 | 25 | 337 |
| Market Structure | 105 | 118 | 81 | 14 | 323 |
| Decision Quality | 154 | 68 | 33 | 17 | 275 |
| Employment Level | 68 | 32 | 74 | 8 | 184 |
| Fiscal & Macroeconomic | 74 | 52 | 32 | 21 | 183 |
| Skill Acquisition | 85 | 31 | 38 | 9 | 163 |
| Firm Revenue | 96 | 30 | 22 | — | 148 |
| Innovation Output | 100 | 11 | 20 | 11 | 143 |
| Consumer Welfare | 66 | 29 | 35 | 7 | 137 |
| Regulatory Compliance | 51 | 61 | 13 | 3 | 128 |
| Inequality Measures | 24 | 66 | 31 | 4 | 125 |
| Task Allocation | 64 | 6 | 28 | 6 | 104 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 10 | 16 | 93 |
| Worker Satisfaction | 42 | 32 | 11 | 6 | 91 |
| Task Completion Time | 71 | 5 | 3 | 1 | 80 |
| Wages & Compensation | 38 | 13 | 19 | 4 | 74 |
| Team Performance | 41 | 8 | 15 | 7 | 72 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 17 | 15 | 9 | 5 | 46 |
| Job Displacement | 5 | 28 | 12 | — | 45 |
| Social Protection | 18 | 8 | 6 | 1 | 33 |
| Developer Productivity | 25 | 1 | 2 | 1 | 29 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 7 | 4 | 9 | — | 20 |
Labor Markets
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Mandatory pre-departure training in South–South labour corridors (examined via the Myanmar–Malaysia corridor) is a highly implementable, cross-level lever for improving regularity and rights-protecting mobility in contexts with limited enforcement and coordination capacity.
Conceptual analysis anchored in the Myanmar–Malaysia corridor using a structured desk review of policy/program materials, corridor process mapping, and governance gap analysis. No new causal field experiments or quantitative impact estimates reported.
The rapid rise of AI-enhanced robotics since the 2010s signals a shift toward increased embedding of AI into hardware systems, accelerating cross-sector spillovers.
Interpretation based on observed acceleration in AI-enhanced robotics patents (patent filings 1980–2019) and the convergence patterns reported in the paper. This is an inference drawn from patenting trends rather than a directly measured measure of cross-sector spillovers.
Nonlinear adoption/diffusion models that allow for thresholds, complementarities, and endogenous firm investment responses will better capture tipping points and adoption dynamics than linear models.
Modeling proposal arguing theoretical need for nonlinear specifications and endogenous adoption; no empirical fit comparisons or simulated sample evidence are presented in the paper.
Estimating micro-level gross flows at occupation × industry × geography × demographic granularity (and at higher frequency) will better capture transitions such as reemployment paths, upskilling, and churn.
Proposal to use CPS, LEHD/LODES, JOLTS, administrative unemployment records and firm panels to estimate high-resolution flows. No empirical estimates or sample-size specifics provided.
Nowcasting and real-time analytics (including LLM re-scoring and streaming signals like job postings/platform activity) can update OAIES and short-term projections to improve monitoring.
Proposal to ingest real-time/near-real-time inputs (job-posting APIs, platform data, administrative records) and re-score tasks via LLM embeddings. No implemented nowcast results or sample-based evaluation are presented.
Incorporating causal identification methods (DiD, event-study, synthetic controls, IV) with task-based exposure will yield more credible causal estimates of AI’s effects on employment, wages, and mobility than correlational risk scores.
Methodological claim supported by standard econometric approaches proposed for use with the OAIES and staggered adoption/panel data. No empirical demonstration is provided; evidence is methodological rationale.
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.
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).
AI-enabled upskilling and AI-guided procedures weaken the negative effect of workplace stress on employee retention (AI moderates the stress→retention link).
Moderation test in PLS-SEM on N = 350. Reported moderator effect (AI × Stress → Retention): β = 0.078, p < 0.005 (interpreted as a buffering/weakening effect of AI interventions on the stress→retention relationship).
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.
GenAI models enable personalization (tailored care pathways and risk predictions) by integrating multimodal data (notes, imaging, labs).
Technical capability demonstrated in model development literature and small-scale studies using multimodal inputs; the paper notes limited real-world longitudinal evidence of clinical outcome improvements from such personalization.
GenAI CDS can extend access to expertise in low-resource settings by supporting non-specialists or overburdened clinicians.
The paper cites the potential based on the capability of decision-support systems and early pilot evaluations; empirical real-world evidence and large-scale trials in low-resource settings are limited or not cited.
GenAI CDS can save clinician time (faster charting, literature summarization, guideline retrieval), potentially increasing capacity and access.
Reported process findings from early studies and human-AI interaction evaluations (qualitative and quantitative) and retrospective workflow analyses; specific sample sizes and effect magnitudes are not provided in the paper.
Generative AI clinical decision support (GenAI CDS) can improve diagnostic and treatment suggestions through synthesis of patient data and medical knowledge, reducing missed diagnoses and standardizing care where evidence is clear.
Early evaluations reported in the paper: controlled tasks, simulated patient vignettes, retrospective validation comparing model outputs to historical chart-verified diagnoses or guideline-concordant actions; no large-scale RCTs cited and sample sizes for cited studies are not specified in the paper.
Recommendation: support capacity building—digital literacy, agronomic knowledge, and extension systems—to increase adoption and equitable benefits.
Authors' recommendation derived from recurring findings on human-capacity constraints in the reviewed studies.
AI interventions supported economic transformation in some contexts by improving market access and enabling reallocation toward higher-value tasks.
Findings from selected studies and institutional reports documenting improved market linkages, price discovery, and shifts in farm household activities.
AI applications contributed to environmental resilience via water and fertiliser savings and earlier pest detection in some studies.
Reported resource-use metrics and earlier detection outcomes in several reviewed studies and case reports synthesized thematically.
AI-enabled interventions produced technical efficiency gains through better input targeting and reduced waste.
Studies in the review reporting improvements in input targeting (e.g., fertiliser/pesticide application) and reductions in waste; aggregated in thematic synthesis.
AI deployment has produced measurable supply-chain efficiency improvements and better market integration in reviewed cases.
Synthesis of studies and institutional reports reporting metrics/qualitative evidence on logistics, aggregation, price discovery, and market linkages.
AI interventions are associated with input cost reductions up to ~25%.
Comparative effect-size synthesis across reviewed studies reporting input cost outcomes (2020–2025).
Across reviewed studies (2020–2025), AI interventions are associated with yield gains of roughly 12–45%.
Comparative effect-size synthesis of reported impacts across the reviewed studies (>60 articles/reports) that reported yield outcomes.
AI-powered digital agriculture in developing contexts—especially Sub-Saharan Africa—can materially improve productivity, sustainability, and rural livelihoods.
Structured literature review and thematic synthesis of >60 peer-reviewed articles and institutional reports (timeframe 2020–2025) focused primarily on Sub-Saharan Africa and other developing contexts.
Proprietary experimental datasets and curated metagenomic sequences become valuable intellectual assets that can differentiate commercial offerings.
Paper lists 'Data as an economic asset' and highlights the value of proprietary datasets and curated metagenomes; no market valuation data are included.
Faster, cheaper access to structural hypotheses can shorten drug and enzyme discovery cycles, raising R&D productivity and lowering marginal costs of early‑stage screening.
Paper argues this as an implication under 'Productivity and R&D acceleration'; it is presented as an economic consequence rather than demonstrated with empirical cost‑or time‑saving data in the text.
Practical applications are already emerging, including accelerating target structure availability for small‑molecule and biologics design, guiding enzyme redesign, and interpreting disease mutations.
Paper lists these application areas as emerging uses of AI‑predicted structures; evidence is presented as examples and implications rather than empirical case studies within the text.
Template‑and‑MSA informed architectures (e.g., RoseTTAFold and AlphaFold family) deliver near‑experimental accuracy for many proteins.
Paper names these architectures and links their inputs (MSAs, templates) to high accuracy against experimental structures (PDB); specific evaluation datasets, protein counts, or error metrics are not enumerated in the text.
Modern AI systems (e.g., AlphaFold variants, RoseTTAFold, single‑sequence models like ESMFold) can approach or reach near‑experimental accuracy while greatly increasing speed and scalability.
Paper cites specific models (AlphaFold family, RoseTTAFold, ESMFold) and describes benchmarking against structural ground truth (PDB / curated experimental structures) and large‑scale pretraining; exact benchmark values or sample sizes are not specified in the text.
Policy levers such as requiring third-party audits, setting interoperability/data standards, subsidizing vetted tools, and investing in formative/performance assessment can align AI-enabled tools with public-interest goals in education.
Policy analysis and recommendations synthesized from assessment theory, comparative case studies, and literature on algorithmic governance; prescriptive (not empirically validated within the paper).
AI supports new forms of formative feedback and personalization that could be used to improve learning measurement.
Synthesis of literature on adaptive learning systems and formative assessment; examples discussed in country case studies based on policy and secondary sources.
New economic metrics are needed for VR (value of behavioral data streams, cost per reduction in harm, ROI on security investments, welfare metrics capturing trust and adoption).
Authors' recommendations based on identified gaps in the literature and the comparative review of 31 studies; proposed as agenda items rather than empirically developed metrics.
VR generates high‑value behavioral and biometric datasets for AI personalization, training, and analytics; firms that extract this data can gain competitive advantages, creating incentives to centralize collection unless counteracted by policy or market forces.
Economic implications inferred by the authors from the literature synthesis and standard industrial‑organization logic; not supported by original empirical market data in the paper.
There is a need for regulatory standards, industry best practices, and ethics‑by‑design approaches; interoperable policy frameworks are recommended to govern VR security and privacy.
Policy and governance recommendations synthesized from multiple reviewed studies and the authors' integration; presented as prescriptive guidance rather than empirically tested interventions.
An effective defense mix for VR combines technical controls (secure boot, attestation, encrypted communications), AI tools for anomaly detection and policy enforcement, and human‑centered design (transparency, consent, usable controls).
Cross‑study synthesis showing these categories recur as recommended controls in the 31 reviewed papers; authors propose combining them in TVR‑Sec. No deployment or performance metrics provided.
Socio‑Behavioral Safety measures (moderation, design constraints, psycho‑social safeguards) are necessary to prevent harassment, persuasion, addictive interfaces, and other psychological harms in shared virtual spaces.
Qualitative synthesis of social‑behavioral harms and proposed mitigations reported across the literature review (31 studies); comparative evaluation of socio‑technical controls.