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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Law and Ethics questions showed the largest paraphrase-induced accuracy drops (19.8 percentage points).
Category-specific results from the 100-question paraphrase subset in Experiment 2, with Law and Ethics items showing the largest average drop of 19.8 percentage points.
Philosophy category exhibited the maximum observed lexical contamination (up to 66.7%).
Per-category contamination rates output by the lexical detection pipeline on MMLU items; the highest observed category rate reported was 66.7% for Philosophy.
Current models appear to internalize preferences as persistent, high‑priority rules rather than conditional behavioral signals contingent on conversational norms and context.
Behavioral patterns observed across BenchPreS scenarios (preference application persisting in inappropriate contexts) and ablation results; interpretive claim based on empirical behavior rather than direct model internals inspection.
BenchPreS detects a pervasive context‑sensitivity failure: models often treat stored preferences as globally enforceable rules rather than conditional, context‑dependent signals.
Pattern of results across the benchmark showing high MR alongside cases where preference application should have been suppressed; qualitative interpretation of model behavior across varied interaction partners and normative contexts in the dataset.
Modern frontier LLMs frequently misapply stored user preferences in contexts where social or institutional norms require suppression (third‑party communication).
Empirical evaluation using the BenchPreS benchmark: models were provided stored preferences and asked to generate responses across contexts requiring either application or suppression; Misapplication Rate (MR) computed as fraction of instances where preferences were applied despite required suppression. Multiple state‑of‑the‑art models were tested (described generically as “frontier models”) across the scenario set.
Passive monitoring and predictive models are insufficient for governing the complex dynamics of a tech-driven economy.
Conceptual critique based on economic cybernetics literature and the author's expert assessment; no empirical test comparing governance regimes is provided.
Digitalization is deepening digital inequality (unequal access to digital tools, skills, and benefits) across social groups and regions.
Qualitative analysis and expert assessment; the paper calls for new metrics but does not present systematic empirical measures of inequality.
Digital transformation can generate technological unemployment if not managed with appropriate retraining and social protection measures.
Expert assessment and literature-informed argumentation in the paper; no empirical longitudinal analysis isolating technology-driven job losses presented.
Forced or poorly regulated digitalization risks exacerbating social stratification.
Conceptual argument supported by qualitative analysis of policy documents and expert assessment; no empirical causal estimates provided.
Manufacturing and Retail experienced net employment contractions attributable mainly to task automation and substitution.
Simulated employment-level series and net change calculations by sector (Manufacturing, Retail) across 2020–2024 in the paper's dataset, together with literature-derived mechanisms emphasizing automation/substitution in these sectors (systematic review of selected publishers 2020–2024).
Explainability, trust, and demonstrated real-world effectiveness are key demand-side frictions; small-scale laboratory gains rarely translate into broad clinical uptake without workflow fit.
Adoption studies, qualitative interviews with clinicians and purchasers, and observations that many high-performing lab models see limited clinical use due to workflow and trust issues.
Hidden costs can arise from increased liability exposure, workflow redesign burden, and potential productivity loss during transition periods.
Qualitative deployment studies and procurement narratives reporting unanticipated legal, operational, and productivity impacts during early rollouts.
Human-AI collaboration can also generate harms, including automation bias, deskilling, and workflow disruption.
Behavioral laboratory experiments, simulation/reader studies demonstrating automation bias, qualitative reports and observational deployment accounts documenting workflow frictions and concerns about reduced trainee exposure.
Operational sustainability is a challenge: coordinating long R&D timelines and ensuring expert governance for drug development within DAOs is difficult.
Case-study observations and discussion of organizational challenges; acknowledged lack of longitudinal performance data in the studied projects.
Token economics can create speculative behavior misaligned with long-horizon drug development incentives.
Theoretical analysis of token market dynamics and incentive misalignment; supported by general observations of crypto market speculative behavior, but no DAO-specific empirical causation demonstrated.
Traditional hierarchical firms struggle to coordinate dispersed expertise and finance public‑good stages of drug development.
Theoretical/organizational analysis and literature synthesis on coordination problems and financing gaps for public-good preclinical stages; qualitative argumentation rather than empirical causal inference.
Empirical evidence shows that every 1 percentage Industrial Robot Density elevation leads to a 0.8 percentage point decrease in the Manufacturing Global Value Chain Participation Rate.
Empirical claim reported in the paper; method described as empirical analysis but the provided excerpt does not specify dataset, country sample, time period, model specification, controls, or sample size.
Developing countries face Technology Embargo, Rule Bundling and Capital Concentration Triple Barriers.
Theoretical and literature-based claim described by the authors; no empirical quantification of these barriers (e.g., number of embargoes, measures of rule bundling, capital concentration metrics) included in the excerpt.
There is a need for cross-jurisdictional regulatory standards to support deployment of ML-blockchain accounting systems.
Policy analysis and stakeholder feedback indicating regulatory fragmentation and the requirement for harmonized standards; asserted as a study finding. (Summary does not list consulted jurisdictions or regulatory bodies.)
Data privacy trade-offs are a significant challenge when combining ML and decentralized ledger technologies for accounting oversight.
Analytic discussion and evaluation of privacy implications arising from the hybrid architecture and use of decentralized ledgers with empirical datasets. (No specific privacy-attack tests or privacy metric values reported in the summary.)
The integration reveals scalability limitations as a critical challenge.
Findings from system evaluation and analysis that identified performance and scalability constraints when applying the hybrid solution to high-risk economic sectors. (No quantitative scalability metrics or testing conditions provided in the summary.)
There is a growing tension between relatively rigid education and training systems and the rapidly changing skill requirements of digitally driven labor markets.
Argument motivated and supported by comparative assessment of international practices and systemic analysis; descriptive/comparative evidence rather than quantified empirical testing.
O SCF é expandido para uma camada de segunda ordem (SCF-E) que incorpora déficit de imaginação tecnocultural e governança simbólica, explicando por que a IA permanece em pilotos e não se converte em capacidade organizacional.
Extensão conceitual (segunda ordem) relatada no artigo; respaldada metodologicamente pela combinação QUAN→QUAL, incluindo etnografia orientada ao SCF (detalhes empíricos no corpo do artigo, não no resumo).
A literatura de adoção tecnológica (TAM, UTAUT, Difusão de Inovações) tende a tratar a resistência como variável comportamental genérica ou deficiência de 'treinamento', negligenciando dimensões simbólicas (ritos, identidades e poder), mecanismos cognitivos de ameaça (aversão à perda, sobrecarga e heurísticas) e seus efeitos econômicos.
Revisão bibliográfica e posicionamento teórico declarado no artigo comparando modelos consagrados com a perspectiva proposta; sem indicação de meta-análise ou contagem empírica no resumo.
A Fricção Psicoantropológica (SCF) é proposta e detalhada como um coeficiente mensurável do custo cultural e da resistência cognitiva que reduz a capacidade de pequenas e médias empresas (PMEs) de transformar iniciativas de Inteligência Artificial (IA) em geração de valor em escala.
Proposição teórica e operacionalização apresentada no artigo; desenho metodológico descrito como QUAN→QUAL incluindo construção de escala psicométrica e etnografia organizacional. O resumo não especifica tamanho de amostra para validação.
The paper highlights that urgent policy intervention is required to reestablish a balance between the benefits of AI and the ethical ramifications that arise from these technologies, with a particular emphasis on job displacement.
Author conclusion drawn from the stated literature-based analysis; the excerpt does not list the specific studies, empirical findings, or criteria used to reach this policy recommendation.
There has been an increase in the level of concern regarding the ethical implications arising from the automation of tasks and the subsequent job displacement due to AI.
Author statement based on a review of (unspecified) novel studies and existing literature; no empirical sample size, instrumentation, or quantitative measure of 'concern' reported in the provided text.
Over-reliance on data-driven insights without adequate human oversight can worsen market uncertainty.
Reported in the study's qualitative case studies and interpretive analysis as a potential negative consequence of improper AI/Big Data use (no quantified examples provided in the summary).
Algorithmic bias is a potential pitfall of using AI and Big Data that can exacerbate market uncertainty.
Identified as a risk in the paper's qualitative analysis and discussion of pitfalls (no incident counts or empirical quantification provided in the summary).
The risk to the tax system is heightened by the federal government’s dependence on individual labor income even as economic value shifts toward mobile capital and AI ownership by large firms.
Analytical claim in the paper linking tax base dependence to shifts in economic value; no empirical measurement of 'mobile capital' or quantified shift included in the excerpt.
AI threatens to disrupt the tax system’s ability to fulfill its fundamental goals of raising revenue, redistributing income, and regulating taxpayer behavior.
Normative/policy argument made in the paper (no empirical testing or quantified projections provided in the excerpt).
These AI-driven outcomes will have far-reaching impacts on the federal tax system, which heavily relies on taxing individual labor income and payroll rather than capital or consumption.
Paper's policy analysis asserting the composition of federal tax reliance (no revenue breakdowns or statistical evidence included in the excerpt).
Even under optimistic projections, AI is expected to exacerbate wealth inequality because ownership and immense value are concentrated within a subset of Big Tech companies and AI startups.
Argumentative claim in the paper asserting concentration of ownership and value in certain firms; no empirical measures or firm-level data presented in the excerpt.
Some experts predict widespread job displacement due to AI.
Statement in the paper referencing expert predictions (no specific experts, studies, or sample sizes cited in the excerpt).
Global AI governance, regulatory fragmentation, and the effects of privacy laws on market competition are under-studied areas.
Low topic prevalence for topics corresponding to global governance, regulatory fragmentation, and privacy-law effects on competition in the >4,600-paper corpus as identified by topic modeling and policy-alignment analysis.
The economic impacts of risk-based AI regulations are under-studied in the current literature.
Topic-modeling indicates few papers focusing on economic impacts of risk-based regulation; authors' crosswalk with policy documents shows this as a gap.
Research on effective industrial policy for AI is relatively underexplored.
Low prevalence of industrial-policy-related topics in the topic-modeling output and comparison to stated policy priorities in national AI strategies and legislation across regions.
There are notable gaps in the literature in measuring AI-driven economic growth.
Comparison of topic prevalence from the topic-modeling exercise with policy priorities derived from national AI strategies and legislation across regions, showing low coverage of research explicitly measuring AI-driven economic growth.
Petroleum imports have a large and negative impact on Indonesia's economic growth.
Macroeconomic analysis within the study (regression/statistical assessment of drivers of economic growth) identifying petroleum imports as a substantial negative contributor to growth.
Current national and regional approaches to AI governance are often fragmented, focusing narrowly on industrial competition, piecemeal regulation, or abstract ethical principles.
Asserted in abstract; implies a review/comparison of existing policies but the abstract does not detail methods or sample beyond later comparative analysis.
AI deepens inequality.
Asserted in abstract; the abstract does not state empirical methods or data backing this claim.
AI's current trajectory exacerbates labor market polarization.
Asserted in abstract; no study design or empirical sample specified in the abstract.
When ERM is implemented merely as a formal compliance mechanism, firms do not realize the same benefits as when ERM is embedded in culture and daily decision-making.
Synthesis from reviewed empirical and conceptual studies indicating differences in outcomes depending on the nature of ERM implementation; underlying studies appear to include comparative observations but are not detailed in the summary.
Traditional silo-based risk management approaches are inadequate for MSMEs in increasingly volatile and uncertain business environments.
Conceptual arguments and literature reviewed in the article contrasting silo-based approaches with integrated ERM frameworks; based on theoretical and empirical critiques in the reviewed literature.
There are concerns that AI may undermine the right to privacy in India.
Legal and policy analysis in the paper discussing privacy risks associated with AI and data-driven governance (review of privacy frameworks and potential conflicts). No empirical sample size; based on normative/legal analysis.
There are concerns that AI has the potential to further increase economic inequality in India.
The paper raises this as a policy/legal concern using theoretical and analytical argumentation (literature/policy review); no primary empirical study or sample size reported in the summary.
AI adoption increases psychosocial pressure on workers.
Themes surfaced via content analysis of recent peer-reviewed literature on AI and workforce wellbeing within the qualitative library research (specific studies not listed).
AI adoption contributes to inequality (uneven distribution of benefits and opportunities).
Synthesis of arguments and empirical findings from accredited journals included in the literature-based study (sources not enumerated).
AI leads to skill mismatch between workers and emerging job requirements.
Identified through thematic analysis of recent literature on workforce dynamics and skills in the qualitative review (specific article count not reported).
AI causes job displacement.
Recurring finding across reviewed accredited journal articles summarized via thematic content analysis in the library research (no quantitative sample provided).