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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This paper uses panel data of China's Shanghai and Shenzhen A-share non-financial listed companies from 2010 to 2022 to study AI's effects.
Explicit data description in the paper (sample frame and period stated).
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.
The research methodology is based on the envelope model ("input" orientation) to assess the level of transformation of labor resources and labor markets due to the spread of artificial intelligence.
Methodological statement in the paper specifying the use of an input-oriented envelope model applied to a sample of European Union countries.
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).
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.)
Potential risks of deploying such models include fairness/bias, privacy concerns from employee-level predictions, and adverse morale effects if interventions are unevenly applied.
Authors' discussion of risks and ethical considerations when applying predictive XAI models to employee data; this is a stated limitation/risk discussion rather than an empirical finding.
Generalizability is limited: results based on the IBM dataset may differ for real green-workforce populations, industries, or countries.
Authors' stated limitation regarding external validity and representativeness of the IBM HR Analytics dataset as a proxy for sustainability roles.
Counterfactual simulations reported are predictive rather than causal; estimated effects require causal validation (e.g., randomized trials) before large-scale policy rollout.
Authors' methodological caveat noting that simulation-based changes in model-predicted probabilities do not establish causality and recommending causal evaluation methods for policy adoption.
The IBM HR Analytics dataset was used as a proxy for sustainability-focused (green) roles, relying on objective HR records rather than self-report surveys.
Data statement in the paper: model trained and evaluated on the IBM HR Analytics dataset; authors explicitly treat it as a proxy for sustainability-oriented roles for purposes of demonstration.
The study shifts retention analysis from descriptive correlations and surveys toward actionable, employee-level predictions and policy evaluation.
Combination of objective HR records (IBM dataset), predictive modeling (logistic regression), calibration, XAI tools (SHAP, LIME), and counterfactual policy simulations to evaluate intervention effects at individual and aggregate levels.
Local explainability (SHAP and LIME) can identify employee-specific intervention levers for targeted retention actions.
Use of SHAP and LIME for local explanations of individual predictions; counterfactual simulations applied at the employee level to estimate impact of feature changes on that employee's calibrated attrition probability.
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.
The paper recommends a research agenda for AI economists: causal microeconometric studies (DiD, IVs, RCTs), structural models with hybrid human–AI agents, measurement work on GenAI use, distributional analysis and policy evaluation.
Explicit recommendations listed in the implications and research agenda sections; logical follow‑on from bibliometric findings about gaps in causal and measurement evidence.
Bibliometric mapping profiles the intellectual structure and evolution of the field but does not establish causal effects of GenAI on organisational outcomes.
Methodological limitation explicitly stated in the paper; bibliometric approach (co‑word, citation, thematic mapping) is descriptive and historical in scope.
Co‑word and thematic analyses reveal six coherent conceptual clusters that bridge technical AI topics (e.g., LLMs, GANs) with managerial themes (e.g., autonomy, coordination, decision‑making).
Thematic mapping and co‑word network analysis performed on the 212‑paper corpus; identification of six clusters reported in results.
Bibliometric and conceptual tools (VOSviewer, Bibliometrix) were used to identify performance trends, co‑word structures, thematic maps, and conceptual evolution in the GenAI–organisation literature.
Methods section: use of VOSviewer for network visualization and Bibliometrix for bibliometric statistics, co‑word analysis, thematic mapping and Sankey thematic evolution.
The study analysed a corpus of 212 Scopus‑indexed publications covering 2018–2025 to map emergent literature on Generative AI and organisational change.
Bibliometric dataset constructed from Scopus; sample size = 212 peer‑reviewed articles; time window 2018–2025; analyses performed with Bibliometrix and VOSviewer.
Research agenda: causal studies (panel data, quasi-experiments) are needed to estimate effects of AI exposure on employment outcomes and to evaluate retraining/income-support interventions for pre-retirement populations.
Authors’ stated recommendation based on limits of cross-sectional regression results from the n=889 survey and the identified need to move from association to causation.
Study limitations: cross-sectional design, self-reported intentions, potential unobserved confounders, and limited generalizability to only three cities (Beijing, Guangzhou, Lanzhou).
Explicit methodological statements in the paper describing data and design: cross-sectional survey of 889 respondents from three cities and reliance on self-reported employment intentions.
The analysis used sentence‑transformer models to produce dense vector representations of article text and UMAP to project those embeddings into a low‑dimensional thematic map for cluster identification and gap detection.
Methods section specifying use of sentence‑transformer embeddings and UMAP for dimensionality reduction/visualization of article text.
The study followed a PRISMA protocol for literature selection and included peer‑reviewed journal articles published between 2014 and 2024, with a final sample size of n = 109.
Explicit methodological statement in the paper describing the literature search, inclusion/exclusion criteria, and final sample.
Twenty‑seven papers study marketing in banking without using NLP methods.
PRISMA systematic review; categorization of the 109 selected articles into the three coverage groups (8, 74, 27).
Seventy‑four papers study NLP in marketing more broadly (not specifically banking).
Same PRISMA‑based systematic review and manual categorization of the final sample n = 109 into topical buckets (NLP in marketing vs. NLP in bank marketing vs. marketing in banking without NLP).
Only 8 peer‑reviewed papers directly examine NLP in bank marketing (out of a final sample of 109 articles published 2014–2024).
Systematic review following PRISMA protocol; final sample n = 109 peer‑reviewed journal articles published 2014–2024; manual screening and categorization yielding counts by topic.
The study's findings are qualitative and case-driven (Xiaomi and Deloitte); generalizability is limited by case selection and the absence of standardized quantitative metrics.
Methods section explicitly states case analysis and literature review as primary methods and notes lack of large-scale quantitative measurement.
The study is qualitative and law-focused and uses Vietnam as a focused case study without collecting primary quantitative field data.
Explicit Data & Methods statement in the paper indicating doctrinal legal analysis, comparative institutional analysis, and normative framework development; no primary quantitative sample.
The study recommends empirical metrics for future evaluation of reforms, including processing time per case, reversal rates on appeal, administrative litigation frequency, compliance and procurement costs, investment flows into public-sector AI, and changes in labor composition and wages in administrative agencies.
Methodological recommendation arising from the paper's normative and comparative analysis.
The work is qualitative and exploratory — presenting naturalistic phenomena rather than causal empirical estimates, and is intended to be hypothesis-generating rather than definitive.
Methodology explicitly stated: naturalistic, qualitative daily observations over one month across multiple platforms; comparative observational documentation without experimental manipulation or causal identification.
CoMAI is a modular, four-agent interview-assessment framework coordinated by a centralized finite-state machine.
System design and implementation described in the paper: a pipeline of four specialized agents (question generation, security/validation, scoring by rubric, summarization/reporting) with a centralized finite-state machine enforcing workflow and information flow constraints.
Field experiments (A/B testing) and willingness-to-pay experiments are necessary to quantify monetary benefits, adoption curves, and optimal pricing for alignment capabilities.
Paper explicitly recommends these empirical approaches in the recommendations for economists and product teams; this is a methodological recommendation rather than an empirical finding.
Recommended evaluation directions include automatic metrics (embedding similarity, task success, turn counts), human evaluation (satisfaction, perceived collaboration), and A/B testing in deployed settings (latency, compute, retention).
Paper's explicit evaluation proposals and recommended metrics listed in the Data & Methods and Evaluation Directions sections; these are prescriptive recommendations rather than executed experiments.
The paper focuses on architecture and conceptual arguments rather than reporting large-scale empirical datasets or results.
Data & Methods section and overall document framing emphasize architecture description and proposed evaluations; explicitly notes absence of large-scale empirical results in the provided summary.
Alignment verification can be implemented using semantic embeddings (cosine similarity) or learned classifiers with threshold-based decision branching.
Paper describes these as recommended implementation approaches for the alignment verification component; no empirical benchmark comparing methods is reported.
Temporal decay in the retrieval component can be modeled with functions such as exponential decay and a tunable half-life parameter applied to dialogue-turn embeddings.
Methodological description in the paper specifying temporal decay modeling options (exponential decay example) and tunable parameters; descriptive claim about intended implementation (no empirical comparison of decay functions provided).
Research agenda items for economists include: quantifying willingness-to-pay for verifiable reasoning, studying labor-market impacts for validators, designing contracts/mechanisms to incentivize truthful argument provision, and evaluating regulatory interventions.
Paper's stated research and policy agenda; prescriptive rather than empirical.
Evaluation currently lacks metrics and benchmarks for argument quality, fidelity, contestability, and human trust; developing these is necessary.
Paper notes the gap and proposes evaluation metrics and experimental designs; no new benchmarks introduced.
Evaluation metrics for the architecture should include sample efficiency, generalization across tasks, robustness to distribution shift, autonomy (fraction of learning decisions made internally), transfer speed, lifelong retention, and safety/constraint adherence.
Explicit recommendations for evaluation metrics in the paper.
This paper is a conceptual/theoretical architecture proposal rather than an empirical study; empirical validation should follow via suggested experiments.
Explicit statement in the paper about nature of contribution.
Suggested empirical research directions for AI economists include: comparing LLM performance and economic outcomes on rule‑encodable vs tacit tasks; quantifying performance decline when forcing LLMs into interpretable rule representations; studying contracting/pricing where buyers cannot verify internal rules; and measuring returns to scale attributable to tacit capabilities.
Explicitly enumerated recommended research agenda items in the paper; these are proposed studies rather than executed work.
New metrics are needed to value tacit capabilities — e.g., measures of transfer, generalization under distribution shifts, ease of integrating with human workflows, and irreducibility to compressed rule representations.
Methodological recommendation in the paper listing specific metric categories for future empirical work.
Suggested empirical validations (not performed) include benchmarking LLMs versus rule systems on allegedly rule‑encodable tasks, attempting rule extraction and measuring fidelity loss, and compression/distillation studies to quantify irreducible task performance.
Recommendations and proposed experimental directions listed in the paper; these are proposals, not executed studies.
The paper contains mostly qualitative and historically grounded empirical content and reports no primary datasets or large‑scale experimental results in support of the formal thesis.
Explicit declaration in the Data & Methods section that empirical content is qualitative/historical and no new datasets were collected.
The paper's core methodological approach is conceptual and theoretical argumentation (formal/logical proof, historical examples, and philosophical framing), not empirical experimentation.
Stated Data & Methods description indicating reliance on formal logic, historical case analysis, and philosophical argument; absence of primary datasets.
The LEAFE algorithmic procedure: summarize environment feedback into compact experience items; backtrack to earlier decision points causally linked to failures and re-explore corrective action branches; distill corrected trajectories into the policy via supervised fine-tuning.
Method section / algorithm description in paper specifying the reflective/backtracking and distillation pipeline as the core of LEAFE.
Evaluation used seven benchmarks spanning online computer-use, offline computer-use, and multimodal tool-use reasoning tasks.
Benchmarks section in the summary states seven benchmarks covering those categories; no benchmark names or dataset sizes provided in the summary.
Objectives combine trajectory-level rewards (for global consistency) with stepwise grounded rewards derived from execution outcomes.
Method summary explicitly lists these objectives as part of the TraceR1 training procedure.
TraceR1 focuses on short-horizon trajectory forecasting to keep predictions tractable while capturing near-term consequences of actions.
Framework description in summary that emphasizes 'short-horizon trajectory forecasting' as a design choice.
During grounded fine-tuning, tools are treated as frozen agents and only the policy is adjusted using execution feedback (tools are not modified).
Explicit statement in Data & Methods section of the summary describing tool handling during grounded fine-tuning.