Evidence (6869 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Governance
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These harms increasingly translate into financial loss through litigation, enforcement penalties, brand erosion, and failed deployments.
Paper argues this linkage using conceptual reasoning and illustrative examples/case vignettes; cites regulatory and market incidents but does not provide systematic empirical estimates or a sample size.
AI systems can create material harms: discriminatory outcomes, privacy and security failures, opacity in decision logic, and regulatory noncompliance.
Paper lists these harms as core risks based on prior literature, regulatory developments, and conceptual risk analysis. Presented as well-documented categories rather than as new empirical findings; no sample size reported.
As artificial intelligence assumes cognitive labor, no existing quantitative framework predicts when human capability loss becomes catastrophic.
Introductory/background claim asserted by authors motivating the study (literature gap claim).
Broader AI scope lowers the critical threshold K* (i.e., more general AI reduces the K* value at which capability collapse occurs).
Model sensitivity analysis / simulations showing K* varies with assumed scope of AI (reported in model calibration discussion).
The model identifies a critical threshold K* approximately 0.85 (scope-dependent; broader AI scope lowers K*) beyond which capability collapses abruptly — the 'enrichment paradox.'
Model analysis and simulations calibrated across domains (paper reports computed threshold K* ≈ 0.85 and notes dependence on AI scope).
Fabrication risk is not an anomalous glitch but a foreseeable consequence of the technology's design, with direct implications for the evolving duty of technological competence.
Conclusion drawn from the paper's theoretical/physics-based analysis and the simulated scenario; stated in the abstract as the authors' interpretation and policy/legal implication.
The paper presents the physics-based analysis in a legal-industry setting by walking through a simulated brief-drafting scenario.
Methodological claim explicitly stated in the abstract: use of a simulated brief-drafting scenario to demonstrate the analysis.
Although commonly dismissed as random 'hallucination', recent physics-based analysis of the Transformer's core mechanism reveals a deterministic component: the AI's internal state can cross a calculable threshold, causing its output to flip from reliable legal reasoning to authoritative-sounding fabrication.
Paper cites/relies on 'recent physics-based analysis' of Transformer mechanisms and states that it demonstrates a calculable threshold; the paper also purports to present this science in a legal setting (via simulation). No numeric experimental sample provided in the excerpt.
Courts confront a novel threat to the integrity of the adversarial process due to fabricated authorities produced by generative AI.
Asserted in the abstract as a consequence of fabricated outputs; supported by the paper's conceptual argument and simulation reference rather than empirical court-case analysis.
Attorneys who unknowingly file such fabrications face professional sanctions, malpractice exposure, and reputational harm.
Stated as a legal/consequential claim in the abstract; no empirical evidence, case counts, or legal-statistics provided in the excerpt.
For law in particular, generative AI introduces a perilous failure mode in which the AI fabricates fictitious case law, statutes, and judicial holdings that appear entirely authentic.
Claimed in the paper; supported by the paper's analytic argument and a simulated brief-drafting scenario referenced in the abstract (no numeric sample 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.
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.
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.
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).
The article argues that the idea of a “Pax Silica” is fragile.
Conclusion drawn from the paper's theoretical framework and comparative analysis; presented as an assessment rather than empirical measurement.
Contemporary struggles over semiconductor supply chains represent not a new hegemonic order but a logistical adaptation of Pax Americana.
Stated thesis supported by comparative/historical analysis and theoretical argumentation (comparative analysis of historical Pax orders and U.S. techno-security architecture); no quantitative sample size reported in abstract.
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).
Traditional paradigms, specifically the resource-based view and the dynamic capabilities framework, operate under closed-system, first-order cybernetic assumptions that fail to capture the dissipative nature of algorithmic agents.
Conceptual critique presented in the paper's theoretical argumentation (literature critique and re-framing); no empirical sample reported.
This result directly contradicts classical scaling laws which assume monotonic capability gains with model scale.
Comparative theoretical claim in the paper contrasting the Institutional Scaling Law with classical empirical/theoretical scaling laws in ML literature.
The Institutional Scaling Law proves that institutional fitness is non-monotonic in model scale.
Formal mathematical derivation/proof presented in the paper (the 'Institutional Scaling Law').
AI development proceeds not through smooth advancement but through extended periods of stasis interrupted by rapid phase transitions that reorganize the competitive landscape (punctuated equilibrium pattern).
Argument based on punctuated equilibrium theory from evolutionary biology and historical analysis presented in the paper identifying discrete transitions in AI history; the paper cites and classifies eras/events as evidence.
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.
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.
Algorithmic credit systems are linked to higher levels of financial stress.
Study reports a positive association between algorithmic credit system use and reported financial stress from regression analysis on the 400-user cross-sectional dataset.
Confirmation bias poses a weakness in LLM-based code review, with implications on how AI-assisted development tools are deployed.
Synthesis of findings from Study 1 (framing-induced detection failures) and Study 2 (practical exploitability and partial mitigation via debiasing).
Adversarial framing succeeds in 88% of cases against Claude Code (autonomous agent) in real project configurations where adversaries can iteratively refine their framing to increase attack success.
Study 2 experiments in real project configurations with iterative adversary refinement evaluated against Claude Code (autonomous agent); reported 88% success rate.
Adversarial pull request framing (e.g., labeled as security improvements or urgent functionality fixes) succeeds in reintroducing known vulnerabilities in 35% of cases against GitHub Copilot under one-shot attacks.
Study 2 experiments simulating adversarial pull requests evaluated against GitHub Copilot (interactive assistant); reported success rate 35% for one-shot attacks.
The framing effect is strongly asymmetric: false negatives increase sharply while false positive rates change little.
Comparison of false negative and false positive rates across framing conditions in Study 1 experiments (250 CVE pairs across models).