Evidence (8501 claims)
Adoption
5831 claims
Productivity
5043 claims
Governance
4561 claims
Human-AI Collaboration
3605 claims
Labor Markets
2749 claims
Innovation
2697 claims
Org Design
2653 claims
Skills & Training
2112 claims
Inequality
1429 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 440 | 117 | 68 | 507 | 1148 |
| Governance & Regulation | 458 | 216 | 125 | 67 | 883 |
| Research Productivity | 270 | 101 | 34 | 303 | 713 |
| Organizational Efficiency | 441 | 106 | 76 | 43 | 670 |
| Technology Adoption Rate | 347 | 130 | 76 | 45 | 603 |
| Firm Productivity | 324 | 39 | 73 | 13 | 454 |
| Output Quality | 272 | 75 | 27 | 30 | 404 |
| AI Safety & Ethics | 122 | 188 | 46 | 27 | 385 |
| Market Structure | 119 | 134 | 86 | 14 | 358 |
| Decision Quality | 182 | 79 | 41 | 20 | 326 |
| Fiscal & Macroeconomic | 95 | 58 | 34 | 22 | 216 |
| Employment Level | 78 | 37 | 80 | 9 | 206 |
| Skill Acquisition | 104 | 37 | 41 | 9 | 191 |
| Innovation Output | 124 | 12 | 26 | 13 | 176 |
| Firm Revenue | 101 | 38 | 24 | — | 163 |
| Consumer Welfare | 77 | 38 | 37 | 7 | 159 |
| Task Allocation | 93 | 17 | 36 | 8 | 156 |
| Inequality Measures | 29 | 81 | 33 | 6 | 149 |
| Regulatory Compliance | 54 | 61 | 13 | 3 | 131 |
| Task Completion Time | 92 | 8 | 4 | 3 | 107 |
| Error Rate | 45 | 53 | 6 | — | 104 |
| Worker Satisfaction | 48 | 36 | 12 | 8 | 104 |
| Training Effectiveness | 60 | 13 | 12 | 16 | 102 |
| Wages & Compensation | 56 | 16 | 20 | 5 | 97 |
| Team Performance | 50 | 13 | 15 | 8 | 87 |
| Automation Exposure | 28 | 29 | 12 | 7 | 79 |
| Job Displacement | 7 | 45 | 13 | — | 65 |
| Hiring & Recruitment | 42 | 4 | 7 | 3 | 56 |
| Developer Productivity | 38 | 4 | 4 | 3 | 49 |
| Social Protection | 22 | 12 | 7 | 2 | 43 |
| Creative Output | 17 | 8 | 6 | 1 | 32 |
| Skill Obsolescence | 3 | 26 | 2 | — | 31 |
| Labor Share of Income | 12 | 7 | 10 | — | 29 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
The contribution rate of total factor productivity (TFP) rose from 18% to 26% between the earlier and later periods.
Decomposition of growth using the extended Cobb–Douglas production function for China over 2010–2022, reporting TFP contribution rates for the two periods.
The initially selected candidates determine both the benchmark of success and the direction of improvement.
Theoretical result asserted by the authors based on analysis of the closed-loop system (paper's analytical finding).
Rejected individuals exert effort to improve actionable features along directions implied by the decision rule.
Model assumption and dynamic behavior encoded in the proposed framework (assumption/behavioral mechanism in the model).
The paper proposes design principles for effective, accountable, and adaptive sandboxes to contribute to debates on experimentalism in AI governance.
Stated contribution of the paper (descriptive claim about content; abstract does not list the principles or empirical testing).
Regulatory sandboxes (RSs) have emerged as a potential solution to AI regulatory challenges.
Descriptive observation and normative framing within the paper; contextual reference to the EU AI Act's treatment of sandboxes (no empirical sample reported in the abstract).
External inputs that bypass internal filtering shorten recognition delays (i.e., speed up detection of regime shifts).
Model extensions/analysis showing that when some inputs are allowed to bypass internal exclusion mechanisms, the dynamics of anchor updating detect regime changes faster; result comes from theoretical model manipulations, not empirical testing.
In a preregistered mediation model, perceived accountability mediated the AI-over-questionnaire effect on goal progress (indirect effect = 0.15, 95% CI [0.04, 0.31]).
Mediation analysis preregistered and reported in the paper using data from the RCT (N = 517); indirect effect estimate 0.15 with 95% confidence interval [0.04, 0.31].
The AI chatbot produced significantly higher goal progress than the no-support control at two-week follow-up.
Between-groups comparison in the preregistered RCT (N = 517); reported effect size d = 0.33 and p = .016 for AI vs control on goal progress measured at two-week follow-up.
The authors provide a demo video, a hosted website, and an installable package demonstrating JobMatchAI.
Paper explicitly states availability of a demo video, a hosted website, and an installable package. No links, access dates, or artifact verification details are provided in the excerpt.
The authors provide a hybrid retrieval stack combining BM25, a skill knowledge graph, and semantic components to evaluate skill generalization.
Paper describes a hybrid retrieval stack composed of BM25, a knowledge graph, and semantic retrieval components intended for evaluation of skill generalization. No evaluation metrics or comparisons are included in the excerpt.
The authors release JobSearch-XS benchmark.
Paper explicitly states release of the JobSearch-XS benchmark. No dataset size, annotation protocol, or access URL provided in the excerpt.
JobMatchAI integrates Transformer embeddings, skill knowledge graphs, and interpretable reranking.
Statement in paper describing system architecture and components (implementation claim). No quantitative implementation details or component-level ablation results provided in the supplied excerpt.
TDAD (Test-Driven Agentic Development) combines abstract-syntax-tree (AST) based code-test graph construction with weighted impact analysis to surface the tests most likely affected by a proposed change.
Description of the tool/methodology and its implementation (TDAD is presented as an open-source tool in the paper).
PIER is an offline reinforcement learning framework that learns fuel‑efficient, safety‑aware routing policies from physics‑calibrated environments grounded in historical vessel tracking data and ocean reanalysis products, requiring no online simulator.
Methodological description of PIER in the paper: offline RL trained on environments constructed from AIS and reanalysis data; no online simulator used for policy learning (implementation details provided).
Bootstrap 95% confidence interval for PIER mean CO2 savings relative to great-circle routing is [2.9%, 15.7%].
Bootstrap analysis applied to the 2023 AIS validation results (840 episodes per method) producing the stated 95% CI for mean percent savings.
PIER reduces per‑voyage fuel consumption variance by a factor of 3.5 (p < 0.001).
Statistical comparison of per-voyage fuel variance between PIER and baseline routing on 840 episodes per method from 2023 AIS data; significance reported with p < 0.001.
On the LoCoMo benchmark, the architecture achieves 74.8% overall accuracy.
Benchmark evaluation reported in the paper using the LoCoMo benchmark with a reported overall accuracy of 74.8%.
Adversarial governance compliance was 100%.
Adversarial compliance testing reported in the paper (linked to the adversarial query experiments); reported compliance = 100%.
There was zero cross-entity leakage across 500 adversarial queries.
Adversarial testing reported in the paper: 500 adversarial queries used to test cross-entity leakage; result = zero leakage.
Progressive context delivery yielded a 50% token reduction.
Reported experimental result in the controlled experiments indicating token usage reduction from progressive delivery = 50%.
Governance routing precision was 92% in the experiments.
Reported experimental metric from the controlled experiments (N=250, five content types) showing governance routing precision = 92%.
The system achieved 99.6% fact recall (with complementary dual-modality coverage) in the controlled experiments.
Reported experimental result from the controlled experiments (N=250, five content types) as stated in the paper.
Immediate practical steps include improved documentation, stakeholder audits, and multi‑metric evaluation; medium‑term steps include standards for participatory evaluation and tooling for transparency and monitoring; long‑term steps include institutional governance, interoperable safety APIs, and public‑interest evaluation infrastructure.
Prescriptive roadmap in the paper based on conceptual analysis and prior literature; these are recommended policy/program milestones rather than empirically validated interventions.
Transparency (detailed documentation of data, objectives, evaluation processes, and deployment constraints; audit and contest mechanisms) is a necessary mechanism for accountable alignment.
Normative and practical argumentation supported by prior work on model cards, documentation standards, and auditing; no new audits are presented in the paper.
Pluralistic evaluation—using multiple, diverse evaluation criteria and stakeholder‑informed metrics rather than single aggregated alignment scores—will better capture the values and harms at stake.
Argumentative rationale and literature synthesis advocating multi‑metric evaluation approaches; examples from prior evaluation critiques are referenced rather than new empirical comparison.
The Flourishing–Justice–Autonomy (FJA) framework should guide alignment efforts, emphasizing (1) Flourishing (human well‑being and meaningful opportunities), (2) Justice (distributional fairness and protection of vulnerable groups), and (3) Autonomy (informed choice and user control).
Prescriptive proposal grounded in conceptual analysis and synthesis of ethical and technical literature; the paper defines and motivates the three principles as its core normative contribution.
The positive spillover effects of CAFTA on third‑country agricultural imports are concentrated in medium and large firms.
Heterogeneity analysis using firm‑size subgroup DID estimates derived from the China Industrial Enterprise Database (2000–2014) showing stronger effects for medium and large enterprises.
CAFTA induced spillovers that significantly increased China's agricultural imports from non‑ASEAN (third) countries.
Difference‑in‑differences (DID) estimation exploiting CAFTA as an exogenous shock; import outcomes drawn from China Customs Database 2000–2014; robustness checks reported (mediator tests and subgroup analyses).
The report issues seven policy recommendations grouped into three goals: (1) improve understanding of the emerging threat, (2) strengthen defenses, and (3) ensure responsible development and deployment.
Policy synthesis based on threat analysis and governance review (report-authored recommendations; descriptive).
Total effect of trust on brand loyalty is approximately 0.800 (total β ≈ 0.800 = direct β 0.410 + indirect β ≈ 0.390), all reported as statistically significant (p < .001 for direct effects; p = .001 for indirect).
Path coefficients reported from SEM (n = 450) and arithmetic combination of direct and indirect standardized effects as reported in the paper.
Adoption intention for AI marketing strongly predicts brand loyalty (Adoption Intention → Brand Loyalty: standardized β = 0.717, p < .001).
Cross-sectional survey (n = 450 Gen Z); SEM (SPSS AMOS); reported standardized path coefficient β = 0.717 with p < .001.
Trust in AI-driven marketing directly increases Generation Z consumers' brand loyalty (Trust → Brand Loyalty: standardized β = 0.410, p < .001).
Cross-sectional survey (n = 450 Gen Z); SEM (SPSS AMOS); reported standardized path coefficient β = 0.410 with p < .001.
Trust in AI-driven marketing has a strong positive effect on Generation Z consumers' intention to adopt AI marketing (Trust → Adoption Intention: standardized β = 0.718, p < .001).
Cross-sectional survey (n = 450 Generation Z respondents); analysis via Structural Equation Modeling (SPSS AMOS); reported standardized path coefficient β = 0.718 with p < .001.
The study's strengths include multimethod triangulation, a very large behavioral dataset (150 million interactions), and controlled simulation experiments informed by empirical observation.
Methods reported: mixed‑methods sequential design with (1) 6‑month lab ethnography (n = 23), (2) computational analysis of 150 million customer interactions, and (3) empirically grounded agent‑based simulation experiments.
The Algorithmic Canvas is an operational medium where segmentation, targeting, and positioning parameters co‑evolve through iterative human–AI collaboration.
Design and implementation described in the study; observation of Canvas‑mediated interactions during a 6‑month lab ethnography inside a Fortune 500 company (n = 23).
Autopoietic STP + Algorithmic Canvas approach is 44% more resilient to market shocks than traditional, process‑based STP (p < 0.01).
Agent‑based simulations and comparative analyses informed by empirical calibration; supported by large‑scale behavioral data (150 million customer interactions) and simulation experiments. Statistical test reported with p < 0.01. Exact number of simulation runs and full test details not specified in the summary.
The main results are robust to inclusion of firm, industry, and year fixed effects, DID identification using the 2018 SCD pilot, and multiple robustness checks addressing potential confounders and endogeneity.
Authors report baseline regressions with firm/industry/year fixed effects, DID specifications exploiting the 2018 Supply Chain Innovation and Application Pilot Program as a quasi-natural experiment, and a battery of robustness tests (alternative specifications, controls, and checks).
The positive effect of SCD on green innovation is stronger for substantive green innovation (actual environmentally beneficial R&D and technologies) than for strategic green innovation (symbolic/labeling or reputation‑oriented activities).
Heterogeneous outcome analysis splitting green innovation into 'substantive' (e.g., green patents, technological R&D outputs) versus 'strategic' (signaling/compliance indicators); regression and DID estimates show larger and statistically significant coefficients for substantive measures compared to smaller or weaker effects on strategic measures.
Supply chain digitalization (SCD) significantly increases corporate green innovation among Chinese A-share listed firms (2012–2022).
Panel analysis of Chinese A-share listed firms over 2012–2022 using regression models with firm, industry, and year fixed effects; difference-in-differences (DID) identification exploiting the 2018 Supply Chain Innovation and Application Pilot Program as an exogenous shock to SCD; firm-level controls included; multiple robustness checks reported.
Algorithmic transparency and interpretability are important so investors and regulators can understand how ESG inputs affect automated decision systems.
Normative recommendation grounded in literature on model risk, accountability, and regulatory needs; not an empirical finding but a consensus implication of reviewed work.
Research priorities include empirically quantifying AI's effects on productivity, wages, inequality, and environmental costs; developing standardized sustainability and governance metrics; and evaluating regulatory impacts on innovation and welfare.
Stated research agenda based on gaps identified in the narrative review; identifies directions for future empirical work rather than presenting new empirical findings.
AI has progressed from symbolic systems to data-driven, generative architectures and large-scale computational infrastructures, becoming a foundational technology across sectors.
Narrative synthesis of historical and technical literature across AI research and innovation studies; qualitative tracing of architectural shifts (symbolic → statistical → deep learning/generative models) and increased deployment across industries. No original empirical measurement or sample size reported in this paper.
MYRIAD-EU synthesizes progress and remaining challenges and proposes concrete directions for continued research and practice in multi-hazard, multi-risk DRR.
Overall project scope: synthesis and reflection on interdisciplinary research and practice conducted across MYRIAD-EU (2021–2025), as reported in the paper.
MYRIAD-EU conducted in-depth, place-based case studies co-produced with local stakeholders to test methods and tools for multi-risk assessment.
Reported methods include in-depth place-based case studies co-produced with local stakeholders as part of MYRIAD-EU activities (2021–2025).
The main results are robust to inclusion of controls and a range of heterogeneity and moderation checks, supporting that findings are not driven by simple time trends or obvious confounders.
Reported robustness checks in the staggered-DID framework (control variables, alternative specifications, subgroup tests) and discussion of parallel-trends assumption.
Implementation of urban green data center pilot policies leads to measurable improvements in firms' energy utilization efficiency.
Staggered-adoption difference-in-differences (DID) using an unbalanced firm–year panel of Chinese A-share listed firms linked to prefecture-level cities (2012–2024); treatment is timing/location of urban green data center pilot designation; results reported as statistically significant and robust to controls and alternative specifications.
Mechanisms linking digital services to export performance include reduced transaction and search costs, platform network and scale effects, data as an input improving service quality and customization, and task‑level specialization changing comparative advantage.
Conceptual/theoretical synthesis drawing on multiple strands of literature and illustrative case studies presented in the review (no new causal identification).
Digital services trade is shifting from traditional cross‑border delivery toward online, platform‑based models, with cross‑border data flows a core input and determinant of competitiveness.
Integrative literature and policy review synthesizing domestic and international studies; theoretical/conceptual synthesis and cited case examples (no new econometric analysis or primary microdata).
Policy recommendations include standards on explainability, audit trails, certification for finance/tax AI systems, stronger data governance, and public–private coordination to update regulatory guidance.
Paper's policy and governance recommendations drawn from case findings and literature synthesis; prescriptive content rather than evaluated interventions.
Deployments should build governance, explainability, and auditability into systems and start with pilots on high-volume, well-structured tasks before scaling.
Paper recommendations based on case experience and analytic framing; advocated strategy rather than empirically validated at scale within the paper.