Evidence (14156 claims)
Adoption
8625 claims
Productivity
7686 claims
Governance
6917 claims
Human-AI Collaboration
6574 claims
Org Design
4189 claims
Innovation
4131 claims
Labor Markets
3588 claims
Skills & Training
2985 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 761 | 200 | 101 | 904 | 2020 |
| Governance & Regulation | 829 | 400 | 191 | 122 | 1566 |
| Organizational Efficiency | 784 | 193 | 125 | 84 | 1197 |
| Technology Adoption Rate | 637 | 236 | 124 | 97 | 1103 |
| Research Productivity | 431 | 131 | 58 | 340 | 972 |
| Output Quality | 481 | 183 | 59 | 47 | 770 |
| Decision Quality | 332 | 177 | 82 | 49 | 647 |
| Firm Productivity | 439 | 57 | 88 | 20 | 610 |
| AI Safety & Ethics | 218 | 279 | 66 | 33 | 602 |
| Market Structure | 181 | 170 | 123 | 24 | 503 |
| Task Allocation | 214 | 64 | 72 | 33 | 388 |
| Skill Acquisition | 174 | 62 | 62 | 17 | 315 |
| Innovation Output | 204 | 27 | 45 | 18 | 295 |
| Employment Level | 105 | 54 | 108 | 13 | 282 |
| Fiscal & Macroeconomic | 132 | 69 | 43 | 26 | 277 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 154 | 48 | 26 | 3 | 231 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 123 | 50 | 6 | 223 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 71 | 92 | 10 | 2 | 175 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 58 | 56 | 26 | 13 | 156 |
| Training Effectiveness | 96 | 21 | 14 | 19 | 152 |
| Wages & Compensation | 77 | 37 | 25 | 6 | 145 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 81 | 21 | 1 | 115 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 47 | 6 | 1 | 59 |
| Social Protection | 28 | 16 | 8 | 2 | 54 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Increasing cost and failure rates in the pharmaceutical R&D process have not fundamentally improved over the last decade.
Stated as a contextual observation in the paper's opening paragraph; presented as a summary of industry trends (no specific dataset, sample size, or citation included in the excerpt).
Without support, performance stays stable up to three issues but declines as additional issues increase cognitive load.
Empirical study / human-AI negotiation case study in a property rental scenario that varied the number of negotiated issues; the paper reports observed performance across different numbers of issues (no sample size for this specific comparison stated in the abstract).
Reliance on automated content generation introduces risks of cognitive overreliance, algorithmic bias, and strategic misalignment.
The paper articulates these risks as conceptual/qualitative concerns in its discussion; no quantitative estimates or empirical tests of these specific risks are reported in the provided excerpt.
Wide disagreement among AIs created confusion and undermined appropriate reliance on advice.
Reported experimental finding from the paper: manipulating within-panel disagreement across tasks produced wide disagreement conditions that, according to the abstract, led to confusion and reduced appropriate reliance. No quantitative metrics reported in abstract.
High within-panel consensus fostered overreliance on AI advice.
Experimental manipulation of within-panel consensus across the three tasks; the abstract reports that high consensus increased participants' reliance on AI (interpreted as overreliance). Specific measures and sample size not provided in abstract.
Current (pay-upfront) models impose a financial barrier to entry for developers, limiting innovation and excluding actors from emerging economies.
Analytical argument in the paper based on cost-structure reasoning and literature on barriers to entry; no empirical sample or causal estimate provided.
Improvements in AI ('better' AI) amplify the excess automation as well.
Model comparative statics: increased AI capabilities raise private incentives to automate, leading to more displacement than is socially optimal; theoretical analysis only.
More competition amplifies the excess automation (the automation arms race).
Comparative-statics result in the competitive task-based theoretical model showing increased competition raises firms' incentives to automate; no empirical sample.
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).
Strict data sovereignty laws fragment regional collaboration between African Union member states and hinder AI development.
Stated in the paper as a policy barrier; supported by the authors' policy review of data sovereignty rules and their implications for cross-border data sharing.
Restricted cloud access due to payment system mismatches and volatile exchange rates is a barrier to AI adoption in Africa.
Claim made in the paper as part of the list of barriers; based on the authors' qualitative and quantitative review and reference to policy/financial constraints across African countries.
Important barriers include limited access to high-performance computing (HPC).
Paper identifies limited HPC access as a key barrier; supported by the authors' collection and consolidation of HPC availability data via the Africa AI Compute Tracker (ACT).
Africa's participation in modern AI development is constrained by severe infrastructural and policy gaps.
Stated as a central argument in the paper; supported by the paper's synthesis of qualitative and quantitative evidence and reference to official declarations on AI adoption across the continent.
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).
The most vulnerable occupational groups to AI-driven transformation are office workers, data entry operators, call center workers, accountants, and administrative staff with routine analytical and administrative tasks.
Results of the envelope-model assessment for the sampled European Union countries that identify occupations with high exposure/vulnerability to AI-driven change; occupations are listed explicitly in the paper.
AI appears to be a diffusing technology, not an emerging occupation.
Synthesis of empirical findings: presence of a shared vocabulary but lack of a coherent practitioner population in resume data, interpreted as diffusion of AI skills/vocabulary across existing roles.
Across heterogeneous learners, a common broadcast curriculum can be slower than personalized instruction by a factor linear in the number of learner types.
Theoretical comparative result in the model (analysis of broadcast vs personalized curricula across heterogeneous learner types; abstract states factor linear in number of types).
The findings provide evidence against cue-based accounts of lie detection more generally.
Authors' interpretation: because lie-detection accuracy did not decrease despite changes to visual cues (retouching, backgrounds, avatars), the results challenge theories that rely on superficial cues for lie detection.
Participants' confidence in their judgments declined in AI-mediated videos, particularly when some participants used avatars while others did not.
Experimental comparisons across conditions with varying levels of AI mediation; subgroup/condition contrast highlighting larger declines in mixed-avatar settings.
Perceived trust in speakers declined in AI-mediated videos.
Experimental results from the two preregistered online experiments comparing perceived trust across varying levels of AI mediation (retouching, background replacement, avatars).
AI-based tools that mediate, enhance or generate parts of video communication may interfere with how people evaluate trustworthiness and credibility.
Motivating claim stated in the paper's introduction/abstract; not an empirical finding but a hypothesis motivating the experiments.
AI adoption faces critical obstacles originating from digital illiteracy, poor Internet access, excessive application costs, and the rural-to-urban divide.
Survey findings and interview themes from the mixed-methods study (survey n=293; interviews n=12) identifying barriers to AI adoption.
Users still had concerns about how AI credit assessments and chatbots operate.
Qualitative interview data (n=12) and/or survey responses (n=293) reporting user concerns about AI credit scoring and chatbots.
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.
AI can initially exacerbate distributional injustice.
Dimension-level analysis indicating negative (or initially negative) effects of AI on the distributional component of the energy justice index.
There are few integrated frameworks (bridging ethics and technical controls) in the current AI governance landscape.
Result of the literature review and cluster analysis showing limited coverage of frameworks that integrate ethical principles with auditable technical controls.
Findings reveal a fragmented landscape dominated by ethics/privacy-centric and compliance/risk-focused approaches.
Synthesis of the reviewed literature and results of PCA/k-means clustering indicate thematic dominance of ethics/privacy and compliance/risk orientations across frameworks.
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.
These findings uncover critical threats to judicial integrity and public trust and underscore the urgent need for robust safeguards against non-legal influences in AI legal systems.
Interpretation/conclusion drawn from the empirical results (observed deviations, sentiment amplification, and subgroup vulnerabilities).
These safety risks are compounded for emotionally charged topics.
Subgroup analyses where emotionally charged case topics showed larger deviations and stronger effects from injected sentiment.
These safety risks are compounded (stronger) for low-skilled occupational categories.
Subgroup analyses reported in the paper showing larger model deviations and/or greater sentiment amplification effects for cases involving low-skilled occupations.
The sentiment-induced divergences lead to unstable and often inflated compensation predictions by the models.
Analysis of model-predicted compensation amounts under sentiment perturbations showing increased variability and upward bias compared to CJOL amounts.
Public opinion (social media sentiment) substantially amplifies deviations between LLM outputs and real rulings.
Stress-test experiments in which injected social media sentiment increased the divergence of model outputs from CJOL judgments across the sample.
Models exhibit inherent deviations from real rulings.
Empirical comparison of LLM outputs to CJOL judgments showing systematic differences (based on the paper's reported comparisons across the dataset).
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).