Evidence (7156 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 |
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 net educational value of AI-generated feedback depends on alignment with pedagogical goals, quality evaluation, integration with human teaching, and governance to manage equity, privacy, and incentives.
Synthesis statement from the meeting report produced by 50 interdisciplinary scholars; conceptual judgment rather than empirical proof.
Convergence after exemplar exposure occurred by both tightening of estimates within a measure family and by agents switching measure families.
Agent-level tracking across stages showed two patterns following exemplar exposure: (1) reduced within-family dispersion (tighter estimates) and (2) categorical switches in measure selection by some agents, as recorded across the 150-agent sample.
LLMs excel at extracting and generating arguments from unstructured text but are opaque and hard to evaluate or trust.
Synthesis of recent LLM literature and observed properties (generation capability vs. opacity); no empirical evaluation within this paper.
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.
HindSight has limitations: it depends on citation and venue proxies for impact, uses a finite forward window (30 months), and may undercount delayed-impact research and be domain-specific to AI/ML.
Authors' stated limitations in the paper noting reliance on observable downstream signals (citations/venues), the finite forward window, field heterogeneity, and measurement noise.
Practical caveats: benefits depend on accelerators supporting MXFP formats; despite up to 96% recovery, residual quality gaps may remain for some task-specific or safety-critical cases; integration and tuning cost is required to apply BATQuant.
Discussion/limitation section in the paper outlining hardware dependency, remaining quality gaps despite high recovery percentages, and engineering effort for integration and tuning; these are argumentative caveats rather than results of controlled experiments.
The sign of the Largest Lyapunov Exponent (LLE) gives a precise criterion: negative LLE (contracting dynamics) permits fast convergence and real speedups for parallel Newton methods, whereas positive LLE (expanding/chaotic dynamics) prevents generally achieving fast convergence.
Theoretical derivation relating Lyapunov exponents to the stability of parallel-in-time linearizations and convergence of the parallel Newton iterations; supported by empirical observations reported on representative tasks.
Many fixed-point and iterative schemes (e.g., Picard, Jacobi) are unified as special cases within the parallel Newton framework.
Theoretical analysis and derivations in the thesis that show these classical iterative methods arise from particular choices/approximations in the parallel Newton formulation.
The core problem is a trade-off between computational latency/resource cost and decision correctness: invoking more LLM reasoning improves correctness but increases latency; invoking less reduces latency but can increase failures.
Paper frames the research problem explicitly as this trade-off in the Introduction/Problem framing sections and motivates the need for adaptive orchestration.
There are potential measurement gaps in the data, particularly in capturing informal employment and rapid technology diffusion.
Authors' stated limitations noting data coverage issues: official statistics and surveys may not fully capture informal sector dynamics or fast-moving tech adoption. Specific metrics of missingness not provided.
The evidence presented in the study is largely correlational, with limited causal identification of AI causing job changes.
Study design and methods statement: reliance on descriptive analyses, occupation-vulnerability mapping, employer surveys, and case studies without quasi-experimental causal identification strategies.
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.
Teamwork partner type moderates the effect of service empathy on collaboration proficiency (i.e., the impact of service empathy on proficiency differs by human vs AI partner).
Reported interaction/moderated-mediation analyses from the online experiment (n = 861) indicating a significant partner-type × service-empathy interaction predicting collaboration proficiency.
Employees' emotional state significantly moderates the relationship between partner type (human vs AI) and collaboration proficiency.
Moderation analyses reported from the same online experimental dataset (n = 861), testing interaction terms between partner type and measured employee emotion on collaboration proficiency; authors report a significant moderating effect.
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.
Net gains from AI are not automatic nor evenly distributed; benefits depend on translation rates to clinical success and on addressing non-technical enablers.
Synthesis and conditional argument informed by sector observations; not backed by empirical distributional analysis in the paper.
Alignment with evolving regulatory expectations (evidence standards, auditing, liability) is necessary to translate AI capabilities into products and reduce adoption risk.
Policy-focused argument referencing regulatory uncertainty; no empirical measures of regulatory impact included.
Realized, sustained impact ('democratized discovery') from AI depends on non-technological enablers: high-quality interoperable data, rigorous validation, transparency/auditability, workforce upskilling, ethical oversight, and regulatory alignment.
Synthesis and prescriptive argument in editorial grounded in observed constraints; no empirical testing of causal dependence provided.
Reward mechanisms reviewed include up-front token sales, milestone-triggered payouts, bounties, and royalties/licensing revenue distribution.
Synthesis of literature and case-study descriptions documenting available reward/payment mechanisms used by DAOs in decentralized science contexts.
Decision models in DAO governance include token-weighted voting, quadratic voting, reputation/stake-based delegation, and multisig/DAO councils for off-chain execution.
Theoretical review of governance mechanisms and survey of existing DAO practices as reported in secondary sources and project documentation.
Token overhead varies from modest savings to a 451% increase while pass rates remain unchanged.
Measured token usage for agent runs with and without skills, reporting a range from modest token savings up to a 451% token increase with no corresponding change in pass rates.
The review synthesizes cross-domain evidence on the use of AI across the continuum from target identification to regulatory integration and critically evaluates existing limitations including data bias, interpretability discrepancy, and regulatory ambiguity.
Statement about the scope and content of the review (literature synthesis and critical evaluation). This is a description of the paper's methods/content rather than an empirical finding; the excerpt indicates these topics are discussed.
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 impact of Generative AI on labor markets is heterogeneous across occupations and tasks.
Synthesis of recent empirical studies drawing on population-level data, online job postings, and systematic reviews as described in the paper.
The study investigates the benefits and drawbacks associated with the incorporation of innovative artificial intelligence technologies into industrial policies.
Author-stated research objective reported in the text; evidence claimed to come from literature review (novel studies and existing literature), but no specific studies, sample sizes, or empirical measures are provided in the excerpt.
Model output can be treated as evidence for studying human behavior, but there are important epistemic limits to interpreting model-generated text as direct evidence of human beliefs or social facts.
Epistemic analysis and methodological critique in the paper (discussion of limits of treating model outputs as evidence); no single empirical test cited in the provided text.
The paper constructs three policy-contingent labor market scenarios for 2025–2035: (1) an Augmented Services Economy with inclusive productivity gains, (2) a Dual-Speed Labor Market characterized by polarization and uneven adjustment, and (3) a Disruptive Automation Shock involving significant displacement and social strain.
Prognostic, scenario-based approach integrating the three evidence bases (task-level capability mapping, occupational exposure/complementarity analysis, and firm- and worker-level adoption evidence). The scenarios are developed and described in the paper for the 2025–2035 horizon.
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.)
Helicoid dynamics is a specific failure regime: a system engages competently, drifts into error, accurately names what went wrong, then reproduces the same pattern at a higher level of sophistication, recognizing it is looping and continuing nonetheless.
Definition introduced in the paper and illustrated by the reported case series; the claim is conceptual/phenomenological rather than a statistical result.
Only one study reported a modest improvement in predicting endoscopic intervention needs (AUC: 0.68).
Single-study result cited in the review reporting AUC = 0.68 for prediction of need for endoscopic intervention.
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).
The review synthesizes findings across five thematic areas: AI‑driven task automation and decision support; digital literacy and capacity building; gender‑sensitive employment patterns; infrastructural and policy challenges; and sustainable development outcomes.
Thematic synthesis of the 55 included articles as described in the paper; themes explicitly listed by the authors.
Prevalence and risk factors for poverty differ by gender, as does the nature of vulnerability.
Stated as a general empirical claim in the introduction, drawing on broader literature (no specific study, method, or sample size provided in the excerpt).
Major actors such as the United States, China, and the European Union pursue distinct models of AI development and regulation.
Comparative policy analysis and qualitative document review of national/regional AI strategies and regulatory proposals for the United States, China, and the EU (specific documents and sample size not specified).
The study identifies the emergence of three competing governance paradigms: the innovation-driven liberal model, the ethics-oriented regulatory model, and the state-controlled authoritarian model.
Finding from the paper's comparative policy analysis and qualitative review of policy documents across major actors (United States, European Union, China); underlying document sources referenced qualitatively but not enumerated as a quantitative sample.
There is substantial heterogeneity in worker experiences within platform-mediated gig work.
Observed variation in roles (primary vs. supplementary), earnings distribution (median below traditional but top-decile premiums), and access to benefits across the 24-country dataset from surveys, administrative records, and platform transaction data.
About 65% of gig workers engage in platform work as supplementary income alongside traditional employment or education.
Self-reported employment status and activity overlap from labor force surveys and administrative linkages in the 24-country dataset.
Distinct AI features (recommendation engines, chatbots, and comparison tools) influence consumer outcomes when modeled as latent constructs.
Methodological claim: the study modeled three AI features as latent constructs and analyzed their relationships with dependent variables using SEM (quantitative questionnaire data).
Artificial intelligence (AI) has a positive and statistically significant effect on growth at lower conditional quantiles (τ = 0.10–0.25) but is insignificant at higher quantiles.
MMQR estimation results reported in the paper showing significant positive AI coefficients at τ = 0.10–0.25 and insignificant coefficients at higher quantiles.
Both time constraints and LLM use significantly alter the characteristics of decision-makers' mental representations.
Results from the 2 × 2 experiment (N = 348) comparing representation-related measures across manipulated conditions; reported statistically significant differences associated with time constraints and with LLM use.
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.
Institutional factors (education systems, active labor market policies, mobility, industrial policy, social protection) shape net employment outcomes from AI.
Theoretical and policy-focused synthesis; cross-country comparisons in literature highlight institutional mediation though no single new cross-country empirical estimate is provided.
Net employment effects depend on the balance of substitution and complementarity, sectoral exposure, and institutional responses.
Conceptual labor-economics framework (task-based, skill-biased change) and comparative review of cross-country/sectoral evidence emphasizing institutional mediation.