Evidence (2340 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 |
Org Design
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Leader emotional intelligence (EI) moderates decision quality, delegation, and managerial communication when generative AI tools (Copilot/ChatGPT) are used in corporate management.
Theoretical EI-moderated human–AI model described in the paper and proposal to test it using a randomized online experiment.
The four-variable account (produced output, underlying understanding, calibration accuracy, self-assessed ability) better explains phenomena like overconfidence, over- and under-reliance on AI, 'crutch' effects, and weak transfer than the simpler claim that generative AI merely amplifies the Dunning–Kruger effect.
Argumentative synthesis in the paper comparing explanatory power of the proposed four-variable framework against the more general Dunning–Kruger metaphor; draws on examples and empirical patterns from the reviewed literature rather than a single empirical test.
A useful working model is 'AI-mediated metacognitive decoupling': LLM use widens the gap among produced output, underlying understanding, calibration accuracy, and self-assessed ability.
Conceptual synthesis and theoretical proposal grounded in reviewed empirical findings from multiple literatures (human–AI interaction, learning research, model evaluation); presented as the paper's working model rather than as a single empirical estimate.
There is a fundamental trade-off between operational stability and theoretical deliberation across multi-agent coordination frameworks.
Empirical results from controlled benchmarks comparing agent architectures under fixed computational time budgets, as reported in the paper (no numeric sample size or statistical details provided in the abstract).
These patterns are consistent with a reorganization of the scientific production process rather than immediate efficiency gains, in line with theories of general-purpose technologies.
Interpretation linking observed changes in budget allocation, team size, and task breadth (from the proposal dataset and task-level analyses) to theoretical predictions about general-purpose technologies (GPTs); empirical findings show organizational change rather than large average short-run productivity gains.
This paper offers a forward-looking framework that emphasizes the decentralizing potential of AI on labor markets, moving beyond the traditional displacement-versus-creation dichotomy.
Paper's stated contribution; based on conceptual framework and synthesis of historical and contemporary analyses (no empirical validation presented in the abstract).
The emergence of artificial intelligence and robotics is catalyzing a profound transformation in the nature of human labor.
Stated as a central premise in the paper's abstract; supported by the paper's synthesis of economic history, contemporary labor market data, and analysis of digital platform growth (no specific datasets or sample sizes reported in the abstract).
AI agents are approaching an inflection point where the binding constraint shifts from raw capability to how work is delegated, verified, and rewarded at scale.
Conceptual argument presented in the paper's introduction/positioning; no empirical data, experiments, or sample reported.
The resulting AI safety profile is asymmetric: AI is bottlenecked on frontier research (novel tasks) but unbottlenecked on exploiting existing knowledge.
Theoretical implication of the novelty-bottleneck model distinguishing novel (human-judgment) vs. routine (covered by agent prior) components of tasks.
Wall-clock time can be reduced to O(√E) through team parallelism, but total human effort remains O(E).
Model-derived result showing parallelism across humans can speed wall-clock completion time while aggregate human effort does not drop asymptotically.
Better agents improve the coefficient on human effort but not the exponent (i.e., they reduce the constant factor but do not change the asymptotic scaling class).
Analytic result from the stylized model under the paper's assumptions about task decomposition and novelty fraction ν.
Behavioral factors — specifically trust calibration, cognitive load, and affective reactions — shape the transition of corporate AI initiatives from pilot deployments to scalable, sustained use.
Synthesis of human-AI interaction literature integrated with adoption frameworks (TAM and TOE); conceptual linkage rather than new empirical testing in this paper.
AI accelerates value-chain maturation while creating distinct risks — including professional responsibility tensions and potential system-level externalities.
Conceptual argument and risk analysis in the Article (theoretical reasoning and synthesis of management/ethics literature). No empirical causal estimate reported in the excerpt.
The legal profession is at a crossroads, caught between intensifying fears of AI-driven displacement and a generational opportunity for transformation.
Author's synthesis and framing in the Article (conceptual assessment; literature/contextual synthesis). No empirical sample or experiment reported in the excerpt.
This advantage is contingent upon robust AI governance, ethical frameworks, and the transition from 'pilot-lite' projects to integrated, data-driven 'AI-first' business models.
Conditional claim in the paper linking success to governance, ethics, and organizational integration; appears to be normative/analytical rather than empirical in the abstract.
Machine-readable metrics and open scholarly infrastructure are reshaping scholarly profiles and incentives.
Conceptual and historical discussion referring to platforms and metrics (e.g., arXiv, Google Scholar, ORCID) as mechanisms changing incentives; no new empirical estimates provided.
That interconnected ecosystem is fundamentally restructuring who can do science (access), how fast discoveries propagate, and what counts as a valid scientific contribution.
Argumentative claim linking infrastructural and tool changes to changes in access, dissemination speed, and norms of contribution. The paper presents examples and narrative but no systematic empirical evaluation or sample.
The most consequential development is not any single tool but the emergence of an interconnected ecosystem—AI agents, preprint platforms, open source codebases, and citation infrastructure—that forms a feedback loop.
Synthesis/argument based on multiple examples (LLM agents, preprint servers like arXiv, open-source code repositories, citation indices). No quantitative measurement or causal identification reported.
The central tension in AI for science is between automation (building systems that replace human researchers) and augmentation (tools that amplify human creativity and judgement).
Analytical claim based on the paper's review of historical examples and conceptual discussion; no primary data or experimental design reported.
Science has repeatedly delegated its bottlenecks to machines—first inference, then search, then measurement, then the full workflow—and each delegation solves one problem while exposing a harder one underneath.
Interpretive historical argument drawing on examples across AI-for-science milestones (e.g., DENDRAL, search and inference systems, measurement automation, and contemporary end-to-end workflows). No quantitative sample or experimental method reported.
AI assistance in safety engineering is fundamentally a collaboration design problem rather than merely a software procurement decision: the same tool can either degrade or improve analysis quality depending entirely on how it is used.
Synthesis of the formal framework and analytic results in the paper (theoretical argument; no empirical sample reported).
Organizational culture and technological readiness moderate the effectiveness of generative AI integration in decision-making processes.
The paper reports moderation effects tested in the SEM framework using survey data from senior managers, decision-makers, and AI adoption specialists (SmartPLS). No numeric moderator effect sizes or sample size provided in the excerpt.
Small language models offer privacy-preserving alternatives to frontier models, but their specialization is hindered by fragmented development pipelines that separate tool integration, data generation, and training.
Background claim stated in paper/abstract; no experimental data provided for this statement within the abstract.
Governmental structures, labor supply and demand, and incorporation of financial measures act as key intervening variables affecting achieved ROI from GenAI implementations.
Qualitative synthesis and theoretical analysis reported in the paper identifying contextual/intervening variables.
Generative AI serves as an effective 'wingman' for employment lawyers, capable of replacing substantial junior associate work while requiring continued human expertise for client counseling, supervision, and final legal advice preparation.
Authors' synthesis of experimental results showing AI-produced substantive analysis plus discussion about remaining limitations (e.g., citation errors) and required human oversight; qualitative assertion about substitutability for junior associate tasks.
AI usage has dual effects on employees: it can both enhance innovative behavior and predict disengagement, as revealed by a dual-path (SOR-based) model.
Interpretation/synthesis from the four-stage longitudinal study of 285 finance professionals using a dual-path model based on SOR theory (combining the mediation and moderation results).
Artificial intelligence embedded in human decision-making can either enhance human reasoning or induce excessive cognitive dependence.
Stated as a conceptual claim in the paper's introduction/abstract; supported by the paper's conceptual framing (theoretical argument), no empirical sample or experimental data reported here.
These productivity gains are most pronounced for lower-skilled workers, producing a pattern the authors call “skill compression.”
Cross-study pattern reported in the literature review: comparative evidence across worker-skill strata in multiple empirical papers showing larger relative gains for lower-skilled/junior workers; specific underlying studies and sample sizes are not enumerated in the brief.
Lightweight safeguards can reduce risk in some settings but do not consistently prevent severe failures.
Analysis of simulated interventions/safeguards within governance simulations showing reductions in certain risk metrics in some scenarios, but persistence of severe failures in others; assessment based on rubric-judged transcript segments.
There are large differences in corruption-related outcomes across governance regimes and specific model–governance pairings.
Observed heterogeneity in outcomes across different authority structures and model–governance pairings within the multi-agent simulations, evaluated via rubric-based scoring over 28,112 transcript segments.
The paper formalizes the distinction using a signal-aggregation model in which an organization maintains an anchor belief and achieves agreement through two exclusion channels: (1) report shrinkage toward the anchor and (2) a tolerance rule that discards reports deviating beyond a threshold.
Analytical formal model presented in the paper specifying an anchor belief and two exclusion mechanisms; model assumptions and mechanisms are explicit in the theoretical development. No empirical sample.
Organizational cohesion is observationally ambiguous: it can arise either from genuine information integration (debate and synthesis of heterogeneous inputs) or from exclusionary processes (conformity pressure, gatekeeping, intolerance of dissent).
Conceptual argument and formal definition in the paper framing; supported by the analytic distinction introduced in the paper between integration and exclusion as alternative generative mechanisms for observed agreement. No empirical sample—argument is theoretical and illustrated by model construction.
The authors identify ten evaluation practices that teams use, ranging from lightweight interpretive checks to formal organizational processes (examples: qualitative user reviews, red-team testing, A/B experiments, telemetry/log analysis, structured annotation, governance/meta-evaluation).
Thematic coding of 19 interview transcripts produced a taxonomy enumerating ten practices (paper reports the taxonomy as an outcome).
Safeguards such as audit trails, explainability, and human oversight impose additional implementation costs that must be weighed against efficiency benefits.
Normative and economic reasoning based on requirements for compliance and system design; no empirical cost estimates provided.
There is a fundamental tension between AI-driven efficiency and core administrative-law principles—discretion, due process, and accountability.
Doctrinal legal analysis of administrative-law principles in Vietnam and comparative institutional analysis of AI adoption in other systems.
The paper is primarily theoretical and historical; empirical validation is needed to quantify the irreducible component of LLM value, and practical degrees of rule‑extractability may exist even if some capabilities remain tacit.
Stated limitations section acknowledging the theoretical nature of the work and the need for empirical follow‑up.
If an LLM's full capability were reducible to an explicit rule set, that rule set would be an expert system; because expert systems are empirically and historically weaker than LLMs, this leads to a contradiction (supporting non‑rule‑encodability).
Logical proof‑by‑contradiction presented in the paper, supported by conceptual mapping between rule sets and expert systems and qualitative historical comparisons.
The paper's proposed ISB+NDMS approach is tailored to the Russian institutional context (leveraging historical planning experience) and its transferability to other political-economic systems is uncertain.
Comparative/transferability claim based on institutional analysis and normative reasoning in the paper; no cross-country empirical comparisons provided.
AI adoption has an inverted U-shaped effect on employee-related corporate social responsibility (ECSR).
Panel regression with quadratic specification (AI and AI^2) showing statistically significant positive coefficient on AI and statistically significant negative coefficient on AI^2; sample of 2,575 Chinese listed firms observed 2013–2023; controls, firm and/or year fixed effects and robustness checks reported.
Demand for labor will shift toward data scientists, ML engineers, and interdisciplinary scientists, while wet-lab expertise and translational teams remain crucial.
Workforce trend analysis and employer hiring patterns summarized in the paper; interviews/case studies indicating changes in team composition.
AI excels at hypothesis generation but cannot replace scientific reasoning and experimental validation; human expertise remains essential.
Argument and case examples in the paper showing AI-generated hypotheses requiring human-led experimental design, interpretation, and validation.
The research methodology combines systemic analysis, comparative assessment of international practices, and analytical generalization of organizational learning models, enabling capture of both structural trends and concrete institutional responses to technological changes.
Methodological statement from the paper describing its approach; this is a factual claim about methods used rather than an empirical finding.
The validity of human–AI decision-making studies hinges on participants' behaviours; effective incentives can potentially affect these behaviours.
Conclusion from the authors' thematic review and theoretical rationale linking incentive design to participant behaviour and study validity (no quantitative effect sizes provided in excerpt).
The study's counterfactual analytical model links HR indicators (training intensity, absenteeism, labor productivity, turnover rates, workforce allocation) to organizational performance outcomes using regression-based simulations and predictive estimation.
Methodological claim explicitly stated: model construction from an industrial firm dataset using regression-based simulations and predictive techniques. (Specific sample size, variable operationalizations, and time frame not reported in the description.)
A minimal linear specification (linearized model) demonstrates how coupling strength, persistence, and dissipation determine local stability and oscillatory regimes through spectral conditions on the Jacobian.
Analytic linear model and local stability analysis in the paper: computation of Jacobian, derivation of spectral conditions (eigenvalue locations) that separate stable/oscillatory regimes; illustrative examples within the paper (no empirical data).
We develop a theoretical framework - the productivity funnel - that traces how technological potential narrows through successive stages, from access and digital infrastructure, through organizational absorption and human capital adaptation, to ultimate value capture.
Conceptual/theoretical development presented in the paper; no empirical sample needed (framework-building).
Effects of curated Skills are highly heterogeneous across domains (e.g., +4.5 pp in Software Engineering vs. +51.9 pp in Healthcare).
Per-domain pass-rate deltas reported in the paper (SkillsBench per-domain analysis). The example domain deltas (+4.5 pp and +51.9 pp) are taken from the reported per-domain results.
Scholarly production, institutional incentives, funding, and the Cold War geopolitical context shaped which economic theories became prominent.
Historical institutional case study drawing on archives, correspondence, publication records, and contemporaneous debates to link institutional and funding environments to intellectual trajectories.
Whether AI increases or decreases overall inequality depends on AI’s technology structure (proprietary vs. commodity) and on labor-market institutions (rent‑sharing elasticity ξ and asset concentration).
Comparative statics and regime analysis within the calibrated model that varies the technological-form parameter (η1 vs. η0) and the rent‑sharing elasticity ξ, as well as measures of asset concentration.
AI can equalize individual task performance while increasing aggregate inequality because rents accrue to owners of complementary assets rather than to workers.
Analytical model and calibrated simulations demonstrating that within-task compression (reduced worker dispersion) can coexist with rising aggregate inequality (ΔGini) owing to rent concentration at the firm/asset-owner level.