Evidence (6507 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Productivity
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The resulting loss from excess automation harms both workers and firm owners.
Welfare comparisons from the model showing negative payoff changes for workers (lower wages/less employment) and reduced owner returns when automation is excessive; theoretical analysis, no empirical data.
In a competitive task-based model, demand externalities trap rational firms in an automation arms race, displacing workers well beyond what is collectively optimal.
Formal equilibrium analysis in the paper's theoretical competitive task-based model; comparative statics and welfare analysis (no empirical sample).
Knowing that AI-driven displacement can erode demand is not enough for firms to stop automating.
Analytical result from the paper's competitive task-based model showing firms' incentives do not internalize demand externalities; no empirical sample.
If AI displaces human workers faster than the economy can reabsorb them, it risks eroding the very consumer demand firms depend on.
Theoretical statement in the paper's motivating premise; no empirical sample reported (conceptual argument about aggregate demand effects when displacement outpaces reabsorption).
Fukui is Japan's least-visited prefecture.
Descriptive claim in the paper specifying the study site (Fukui) as the country's least-visited prefecture; no supporting national rankings provided in the excerpt.
We quantify an annual opportunity gap of 865,917 unrealized visits, equivalent to approximately 11.96 billion yen (USD 76.2 million) in lost revenue.
Model-based estimate produced by the DSS using the analyzed datasets and the DHDE-informed optimization; figure reported directly in the paper.
For regions experiencing demographic decline and structural stagnation, the primary risk is 'under-vibrancy', a condition where low visitor density suppresses economic activity and diminishes satisfaction.
Conceptual claim and problem framing provided by the authors (theoretical/qualitative argument in the paper).
Most research in urban informatics and tourism focuses on mitigating overtourism in dense global cities.
Author statement in introduction positioning the paper relative to existing literature; no quantitative literature review or citation counts reported in the excerpt.
Developers and experts still lack a shared view, resulting in repeated coordination, clarification rounds, and error-prone handoffs.
Observational/qualitative claim in paper describing current MSD practice (no numeric sample reported).
Even with AI coding assistants like GitHub Copilot, individual coding tasks are semi-automated, but the workflow connecting domain knowledge to implementation is not.
Qualitative observation/comparative statement in paper (no empirical sample reported).
Multidisciplinary Software Development (MSD) requires domain experts and developers to collaborate across incompatible formalisms and separate artifact sets.
Conceptual/argument in paper framing the problem (no empirical sample reported).
Only 12% of AI market value is used in physical activities.
Descriptive aggregate: authors categorize and report that 12% of estimated AI market value maps to physical activities.
Off-the-shelf implementations of DRL have seen mixed success, often plagued by high sensitivity to the hyperparameters used during training.
Statement in the paper's abstract describing observed/prior performance issues with standard DRL implementations; implies literature/empirical experience but no specific experiment/sample given in the abstract.
Coal-based energy consumption structure and a secondary-industry-dominated industrial structure significantly inhibit regional TFCP and have strong negative spatial spillovers.
Control-variable coefficients from Spatial Durbin Model on panel data (30 provinces, 2010–2023) showing statistically significant negative direct and indirect effects for coal-dominant energy structure and secondary-industry share.
Applying them to hardware-in-the-loop (HIL) embedded and Internet-of-Things (IoT) systems remains challenging due to the tight coupling between software logic and physical hardware behavior; code that compiles successfully may still fail when deployed on real devices because of timing constraints, peripheral initialization requirements, or hardware-specific behaviors.
Conceptual/engineering reasoning stated in the paper describing known HIL/IoT failure modes (no experimental quantification provided in this excerpt).
Compositional spatial reasoning remains a formidable challenge for state-of-the-art VLMs (as revealed by our evaluation).
Empirical results from the evaluation of the 37 VLMs on the MultihopSpatial benchmark showing poor performance on multi-hop/compositional queries.
Existing benchmarks predominantly focus on elementary, single-hop relations and neglect multi-hop compositional spatial reasoning and precise visual grounding needed for real-world scenarios.
Literature/benchmark survey and motivation presented by the authors comparing characteristics of prior benchmarks vs. the proposed needs.
Adoption barriers exist, particularly for small and medium-sized enterprises and firms in emerging economies, where capability and data constraints limit impact.
Findings reported from the systematic review and mixed-methods assessment (abstract references barriers observed across reviewed studies); number of studies reported in abstract is 104 for the systematic review.
Significant limitations emerged in case law citations, with most cited cases being non-existent or incorrectly referenced.
Authors' review of the case citations produced by the four AI engines for the single transcript, finding many citations were fabricated or misreferenced.
GDP growth is initially negatively affected by the ageing population.
Estimated negative association reported in panel threshold regressions using provincial panel data (31 provinces, 2000–2022); ageing operationalized (primary specification) as an ageing measure (paper also tests old-age dependency ratio).
Initial adaptation challenges to AI integration were identified among employees.
Participants in semi-structured interviews (n=12) reported initial difficulties adapting to AI tools; themes relating to early adaptation challenges were coded.
Past machine learning applications to pricing have produced models that adapt slowly to real-time changes, depend heavily on historical data, and struggle to handle multi-agent scenarios.
Stated as literature/related-work critique in paper; no new empirical evidence or sample size provided in the excerpt.
Traditional methods, such as rule-based algorithms and statistical scale forecasting, struggle to adapt to rapidly changing market conditions, competitive maneuvers, and evolving consumer strategies, leading to sub-optimal pricing and decreased profitability.
Paper asserts this as background/motivation; no detailed empirical study or sample size provided in the excerpt.
In the short term, big data may inhibit welfare growth.
Theoretical comparative-static/dynamic analysis reported in the model showing that initial or short-run effects of increased data sharing can reduce welfare growth (no empirical/sample data).
There is a measurement asymmetry in standard LLM evaluation: unconstrained prompts can inflate constraint-adherence scores and mask the practical value of structured prompting.
Analysis of evaluation results from the controlled study showing that unconstrained (simple) prompts sometimes achieve high constraint-adherence scores, leading to misleading evaluation of structured prompts' benefits.
There is a central design tension in human-AI systems: maximizing short-term hybrid capability does not necessarily preserve long-term human cognitive competence.
Conceptual/theoretical claim derived from the framework and discussion in the paper (argument and mathematical framing), no empirical sample or longitudinal data presented in the excerpt.
The interaction of artificial intelligence and environmental regulation produces a '1 + 1 < 2' crowding-out effect (their combined effect is less than the sum of individual effects).
Spatial Durbin model with interaction term between AI and environmental regulation as summarized in the abstract; reported as a crowding-out interaction.
Environmental regulation significantly inhibits local UCEE.
Spatial Durbin model results reported in the abstract indicating a significant negative local coefficient for environmental regulation.
Artificial intelligence significantly inhibits local UCEE.
Spatial Durbin model results reported in the abstract indicating a significant negative local coefficient for artificial intelligence.
Progress in agentic AI systems that generate and optimize GPU kernels is constrained by benchmarks that reward speedup over software baselines rather than proximity to hardware-efficient execution.
Author argument/observation in paper (conceptual claim about limitations of existing benchmarks); no empirical sample or experiment reported in the provided text.
Rather than broad job losses, evidence points to a reallocation at the entry level: AI automates tasks typically assigned to junior staff, shifting the nature of entry-level roles.
Synthesis of firm- and task-level empirical studies reported in the brief documenting automation of routine/junior tasks and changes in job-task composition; specific sample sizes vary by cited study and are not provided in the brief.
AI-only baselines perform near or below the median of competition participants.
Comparison of AI-only baseline performance to the distribution of competition participant results reported in the paper (competition with 29 teams / 80 participants).
Our results show that current AI agents struggle with domain-specific reasoning.
Outcome of the competition reported in the paper comparing AI-only baselines to participant submissions across the AgentDS tasks (competition data from 29 teams / 80 participants); reported aggregate performance indicating AI weakness on domain-specific tasks.
The gap between informal natural language requirements and precise program behavior (the 'intent gap') has always plagued software engineering, but AI-generated code amplifies it to an unprecedented scale.
Conceptual claim and argumentation in the paper; presented as an observed escalation in the scale of the existing 'intent gap' due to AI code generation. No quantitative evidence or sample size given in the excerpt.
The capital-output elasticity dropped significantly, from 0.42 in 2010–2015 to 0.35 in 2016–2022.
Estimated from an extended Cobb–Douglas production function applied to China's economy over 2010–2022, with period split 2010–2015 vs 2016–2022 (as reported in the study summary).
Limitations include possible limited organizational generalizability due to a single Fortune 500 lab context; ABS results depend on model specification/calibration; and operational definitions of 'resilience' and 'planning cycle' require careful reading.
Authors' reported limitations based on study design: single lab context (n = 23), dependence of ABS on model choices, and nontrivial operational definitions.
Some declines (in self-efficacy and meaningfulness) from passive AI use persist after participants return to manual work.
Within-experiment assessment of outcomes after participants returned to manual (no-AI) tasks following the AI-use manipulation in the pre-registered experiment (N = 269); reported persistent reductions in self-efficacy and meaningfulness for the passive condition.
Passive use of AI reduces perceived meaningfulness of work.
Pre-registered experiment (N = 269) with self-reported measure of work meaningfulness; passive-copy condition showed lower meaningfulness ratings than No-AI and Active-collaboration conditions.
Passive use of AI reduces psychological ownership of the produced outputs.
Same pre-registered experiment (N = 269). Participants in the passive-copy AI condition reported lower psychological ownership of their outputs (self-report scales) relative to No-AI and Active-collaboration conditions.
Passive use of AI (copying AI-generated output) reduces workers' self-efficacy.
Pre-registered between-subjects experiment (N = 269) using occupation-specific writing tasks. Participants assigned to a passive-copy AI condition reported lower self-efficacy (self-reported confidence to complete tasks without AI) compared to the No-AI (manual) and Active-collaboration conditions.
Large-scale AI models have significant energy and resource costs, creating a notable environmental footprint that must be addressed.
Narrative integration of prior empirical studies measuring compute, energy consumption, and embodied emissions of large models (cited literature); the review does not present new quantitative measurements itself.
As AI is deployed in safety-critical domains, reliability, regulation, and human-oriented system design become essential to avoid harms.
Review of literature on safety-critical systems, human–machine interaction studies, and regulatory policy discussions; the paper reports this as a consensus implication rather than presenting new empirical tests.
The current literature is skewed toward descriptive and engineering work; there is a lack of causal, field‑experimental evidence on NLP interventions' effects on customer behavior and firm profits.
Review coding of study types in the sample (engineering/descriptive vs. experimental/causal) showing few field experiments or causal designs.
Important gaps include customer acquisition, personalization at scale, use of external text sources (social media, news, reviews), operational process improvement, and cross‑channel integration.
Gap detection via low‑density regions in the UMAP thematic map of sentence‑transformer embeddings and manual review showing low article counts for these topics within the 109‑article sample.
Existing literature on NLP in marketing is concentrated around customer retention tasks (e.g., churn prediction, complaint handling, relationship management).
Thematic clustering from sentence‑transformer embeddings of article text combined with UMAP visualization, and manual review of article topics and keywords identifying frequent retention‑related themes.
NLP applications in bank marketing are severely under‑studied.
Descriptive result from the PRISMA review showing only 8/109 articles focused on NLP in bank marketing (≈7%), plus thematic mapping showing sparse coverage in bank‑marketing/NLP intersection.
Jurisdictions are taking divergent policy approaches (e.g., U.S. emphasis on innovation/competition, EU emphasis on rights/standards like GDPR), producing fragmented digital trade rules.
Comparative legal and policy analysis of existing national/regional rules and international instruments (examples cited include GDPR and U.S. policy orientations); descriptive, with specific regulatory texts analyzed.
AI creates novel non-tariff frictions, e.g., pressures toward data localization and regulatory requirements for algorithmic transparency.
Comparative legal and policy analysis of emerging regulations (e.g., data localization laws, algorithmic regulation initiatives) and illustrative jurisdictional examples.
Vietnam's civil-law features—statutory specificity, formal procedures, and constitutional principles like legal certainty and fairness—make straightforward AI deployment legally fraught.
Close textual analysis of Vietnam's statutes, constitutional provisions, and administrative procedures (doctrinal legal analysis); no quantitative sample.
Automated decisions complicate assigning responsibility and hinder judicial and administrative reviewability.
Doctrinal examination of accountability and review mechanisms in administrative law plus comparative institutional analysis of automated decision-making governance.