Evidence (5539 claims)
Adoption
5539 claims
Productivity
4793 claims
Governance
4333 claims
Human-AI Collaboration
3326 claims
Labor Markets
2657 claims
Innovation
2510 claims
Org Design
2469 claims
Skills & Training
2017 claims
Inequality
1378 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 402 | 112 | 67 | 480 | 1076 |
| Governance & Regulation | 402 | 192 | 122 | 62 | 790 |
| Research Productivity | 249 | 98 | 34 | 311 | 697 |
| Organizational Efficiency | 395 | 95 | 70 | 40 | 603 |
| Technology Adoption Rate | 321 | 126 | 73 | 39 | 564 |
| Firm Productivity | 306 | 39 | 70 | 12 | 432 |
| Output Quality | 256 | 66 | 25 | 28 | 375 |
| AI Safety & Ethics | 116 | 177 | 44 | 24 | 363 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 76 | 38 | 20 | 315 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 77 | 34 | 80 | 9 | 202 |
| Skill Acquisition | 92 | 33 | 40 | 9 | 174 |
| Innovation Output | 120 | 12 | 23 | 12 | 168 |
| Firm Revenue | 98 | 34 | 22 | — | 154 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 84 | 16 | 33 | 7 | 140 |
| Inequality Measures | 25 | 77 | 32 | 5 | 139 |
| Regulatory Compliance | 54 | 63 | 13 | 3 | 133 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Task Completion Time | 88 | 5 | 4 | 3 | 100 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 32 | 11 | 7 | 97 |
| Wages & Compensation | 53 | 15 | 20 | 5 | 93 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 24 | 22 | 9 | 6 | 62 |
| Job Displacement | 6 | 38 | 13 | — | 57 |
| Hiring & Recruitment | 41 | 4 | 6 | 3 | 54 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 10 | 6 | 2 | 40 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 5 | 9 | — | 26 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
Adoption
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Algorithmic credit systems are positively associated with repayment behavior.
Multiple regression results reported in the study indicate a positive association between use of algorithmic credit systems and repayment behavior based on cross-sectional survey of 400 users.
Measurement reliability and validity were established through Cronbach's alpha and principal component analysis.
Paper states that Cronbach’s alpha and principal component analysis (PCA) were used to establish measurement reliability and validity.
The study used a quantitative, explanatory, cross-sectional design and employed multiple regression and moderation analyses to assess relationships among algorithmic credit systems, human capability, institutional design, and financial-wellbeing outcomes.
Methods described explicitly: quantitative explanatory cross-sectional design; analytical methods named as multiple regression and moderation analyses.
Data were collected from 400 users of algorithmic and digitally mediated credit platforms.
Study reports a quantitative, explanatory, cross-sectional survey of users; sample size explicitly stated as 400.
Empirical simulations of five game scenarios (ranging from repeated prisoner's dilemma to stylized repeated marketing promotion games) validate the theoretical predictions: AI agents naturally exhibit the proposed reasoning patterns and attain stable equilibrium behaviors intrinsically.
Simulation experiments reported in the paper across five distinct game scenarios; these simulations are presented as empirical validation of the theoretical results.
Relaxing the common-knowledge payoff assumption—allowing stage payoffs to be unknown and each agent to observe only its own privately realized stochastic payoffs—still yields the same on-path Nash convergence guarantee.
Theoretical extension/proof in the paper showing convergence results hold under private, stochastic stage payoffs (no common-knowledge of payoffs).
We prove that 'reasonably reasoning' agents—agents capable of forming beliefs about others' strategies from previous observation and learning to best respond to these beliefs—eventually behave along almost every realized play path in a way that is weakly close to a Nash equilibrium of the continuation game.
Formal theoretical proof provided in the paper (mathematical analysis of agent belief-formation and best-response learning leading to on-path closeness to Nash equilibria).
Off-the-shelf reasoning AI agents can achieve Nash-like play zero-shot, without explicit post-training.
Stated claim in the paper supported by a combination of theoretical results (formal proofs about convergence properties of 'reasonably reasoning' agents) and empirical simulations across five game scenarios (including repeated prisoner's dilemma and stylized repeated marketing promotion games).
End-to-end verified pipelines can produce provably correct code from informal specifications.
The paper surveys early research demonstrating pipelines that go from informal specifications to formally verified code; the provided text does not include experimental sample sizes or benchmarks.
AI-generated postconditions catch real-world bugs missed by prior methods.
Surveyed early research asserted by the paper indicating empirical instances where AI-generated postconditions found bugs that other methods missed; no numeric details provided in the excerpt.
Interactive test-driven formalization improves program correctness.
Paper surveys early research that reportedly demonstrates this effect (described as 'interactive test-driven formalization that improves program correctness'); the excerpt does not include specific study details or sample sizes.
The central bottleneck is validating specifications: since there is no oracle for specification correctness other than the user, we need semi-automated metrics that can assess specification quality with or without code, through lightweight user interaction and proxy artifacts such as tests.
Analytical claim and research agenda item in the paper; motivates need for new metrics and interaction designs. No empirical validation or sample size reported in the excerpt.
Intent formalization offers a tradeoff spectrum suitable to the reliability needs of different contexts: from lightweight tests that disambiguate likely misinterpretations, through full functional specifications for formal verification, to domain-specific languages from which correct code is synthesized automatically.
Conceptual framework proposed in the paper describing a spectrum of specification formality; presented as an argument rather than an empirical finding, with no sample sizes provided in the excerpt.
Intent formalization — translating informal user intent into checkable formal specifications — is the key challenge that will determine whether AI makes software more reliable or merely more abundant.
Normative argument presented by the authors as the central thesis of the paper; no empirical study or sample size cited in the provided text.
Agentic AI systems can now generate code with remarkable fluency.
Authoritative assertion in the paper based on contemporary observations of large code-generating models; no empirical sample size or benchmark numbers reported in the text provided.
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).
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.
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).
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 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.
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.
To mitigate risks and realize benefits, AI systems in finance/tax should combine AI with human-in-the-loop controls and clear escalation paths.
Prescriptive recommendation grounded in case lessons and literature on safe AI deployment; presented as a best-practice guideline rather than tested intervention.