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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In an additive model where human utility and fitness differ, if deception increases fitness beyond genuine utility then evolution will select for deception.
Mathematical analysis of an additive model in the paper showing selection pressure favors traits (deception) that increase the fitness function even when they reduce true human utility (theoretical derivation).
Green AI research has largely measured the footprint of models rather than the downstream workflows in which GenAI is a tool.
Literature review / mapping of recent Green AI literature reported in the paper; descriptive claim about the focus of the field (no sample size or numerical counts reported in the abstract).
These findings highlight how existing caste hierarchies are reproduced in LLM decision-making and underscore the need for culturally grounded evaluation and intervention strategies in AI systems deployed in socially sensitive domains.
Interpretation and policy recommendation based on empirical patterns found in the audit (consistent hierarchical ratings and up-to-25% differences).
Inter-caste matches are further ordered according to traditional caste hierarchy.
Reported analytic pattern where inter-caste match ratings follow the traditional caste ranking (implied ordering across Brahmin, Kshatriya, Vaishya, Shudra, Dalit).
Replacing deterministic components with probabilistic workflows changes the failure mode: LLM pipelines may generate plausible but incorrect outputs that pass superficial checks and propagate into irreversible actions such as DOI minting and public release.
Conceptual argument supported by the paper's incident descriptions (e.g., a detected coordinate transformation error); the statement is presented as a general risk rationale.
The remaining 26 barriers are carried over from prior digital transformation waves — 22 in amplified form and 4 unchanged.
Comparative coding/classification within the review corpus indicating whether each barrier is novel or carried over, and whether it is amplified versus unchanged.
Three barriers were identified as agentic-specific: error propagation in multi-agent systems, role ambiguity, and accountability diffusion.
Classification of the 29 coded barriers by 'agentic specificity' within the literature review; these three barriers were labeled agentic-specific by the authors.
There are macroeconomic risks associated with AI-led unemployment.
Paper's macroeconomic analysis drawing on labor economics and technology adoption research; no quantitative estimates or sample sizes provided in the summary.
Managerial incentives drive premature workforce contraction during AI adoption.
Analytical claim grounded in labor economics and organizational behavior review; the summary indicates examination of managerial incentives but does not report primary empirical tests or sample sizes.
Premature workforce contraction in response to AI adoption foreshadows deeper structural challenges as AI systems mature.
Forward-looking claim based on synthesis of literature and theoretical projection; no empirical quantification or sample provided in the summary.
This pattern of premature workforce reductions reflects longstanding corporate short-termism rather than genuine technological displacement.
The paper's interpretation drawing on labor economics and organizational behavior literature; no empirical study or sample size reported in the summary.
Organizations face mounting pressure to demonstrate immediate returns on AI investments, often through workforce reductions that outpace actual automation capabilities.
Argument in paper citing accelerating AI adoption across sectors and observed managerial responses; no primary dataset or sample size reported in the text.
Such predatory-hiring cases often fall outside the scope of merger control because they fail to meet the applicable thresholds, warranting consideration under the abuse of dominance prohibition in Article 102 TFEU.
Legal analysis stated in abstract referencing merger control thresholds and Article 102 TFEU (no quantitative sample provided in abstract).
When a dominant undertaking in a concentrated market strategically targets and hires a large portion—or the entirety—of a smaller competitor’s key personnel, this behavior can raise significant competition concerns.
Legal argument presented in abstract; draws on relevant case law and scholarship (no empirical sample or experimental method reported in abstract).
There is a governance window—estimated at 10–15 years—before current deployment trajectories risk path-dependent social, economic, and institutional lock-in.
Forward-looking estimate/projection provided in the paper based on the authors' characterization of deployment trajectories and governance dynamics (no empirical sample size provided in the excerpt).
Societal consequences of labor displacement intensify the governance gap by concentrating consequential AI decision-making among an increasingly narrow class of technical and capital actors.
Analytic/theoretical claim in the paper drawing on the paper's multi-domain argument (no empirical sample size or quantified concentration metrics provided in the excerpt).
This nominal-vs-genuine oversight distinction represents the primary architectural failure mode in deployed AI governance.
Argumentative claim based on the paper's multi-domain synthesis and theoretical analysis; no empirical sample size or quantified causal inference provided in the excerpt.
The distinction between nominal and genuine human oversight is largely absent from current governance frameworks, including the EU AI Act and NIST AI Risk Management Framework 1.0.
Comparative policy/regulatory review claimed in the paper (explicit reference to the EU AI Act and NIST AI RMF 1.0); no sample size—based on textual/regulatory analysis rather than statistical data in the provided excerpt.
There exists a critical and underexamined governance gap between nominal human oversight of AI systems (humans in formal authority positions) and genuine human oversight (humans with cognitive access, technical capability, and institutional authority to understand, evaluate, and override AI outputs).
Conceptual/qualitative analysis and argumentation presented in the paper; implied synthesis of case examples and theoretical considerations rather than a quantified empirical study in the provided excerpt.
The accelerating displacement of human labor by artificial intelligence (AI) and robotic systems represents a structural transformation whose societal consequences extend far beyond conventional labor market analysis.
Stated as a framing claim in the paper; supported by the paper's literature review and multi-domain conceptual argument (no empirical sample size or quantitative data reported in the provided excerpt).
Sustaining such cooperative informational systems has historically proven difficult due to structural incentives that gradually erode transparency and trust.
Historical/analytical assertion in the paper; presented as a high-level observation (no dataset or empirical historical analysis provided in the excerpt).
The interaction between strict algorithmic control and worker counter-strategies leads to persistent limit cycles in strategy frequencies rather than convergence to a stable compliant workforce.
Dynamical systems analysis and simulation trajectories from the EGT model showing limit cycles / oscillatory equilibria in strategy proportions; model-based (no empirical sample).
Policy enforcement reduces total spending by 27.3%.
Quantitative result reported from the paper's experiments across baselines and scenarios (paper reports a 27.3% reduction attributed to policy enforcement).
In many deployment contexts, especially countries with strong real-time fiat systems like UPI, relying on crypto rails is misaligned with regulatory and infrastructure realities.
Contextual/argumentative claim in the paper contrasting crypto reliance with fiat systems such as UPI (no empirical country-level sample reported).
The emission-reduction effect of AI innovation is more significant for firms located in regions with underdeveloped factor markets.
Heterogeneity (regional subsample/interaction) analysis reported in the paper on the 21,428 firm-year sample, indicating larger AI-related emission reductions in regions with less developed factor markets.
The emission-reduction effect of AI innovation is more significant for firms in high-environmental-sensitivity industries.
Heterogeneity (subsample/interaction) analysis in the paper using the 21,428 firm-year observations, showing stronger AI-related emission reductions in industries characterized as high environmental sensitivity.
The emission-reduction effect of AI innovation is more significant for enterprises with a low supply chain concentration.
Heterogeneity (subsample) analysis reported in the paper using the 21,428 firm-year dataset, comparing effects across firms with different supply chain concentration levels.
Executives’ green cognition and government environmental attention together constitute dual internal and external driving forces for corporate carbon emission reduction.
Further analysis reported in the paper (moderation/interaction analysis or additional regressions) on the same 21,428 firm-year sample showing these factors strengthen carbon reduction associated with AI innovation.
AI innovation can significantly reduce corporate carbon emission intensity.
Empirical analysis using panel data of 21,428 firm-year observations from Chinese A-share listed manufacturing companies over 2010–2022; result reported in the paper's main regressions (method described as micro-level empirical analysis).
Using a stylised inpatient capacity signalling example and minimal game-theoretic reasoning, task optimisation alone is unlikely to change system outcomes when incentives are unchanged.
Theoretical analysis using a stylised inpatient capacity signalling example and game-theoretic reasoning presented in the paper (no empirical data/sample reported in the abstract).
Deployment of AI systems carries significant costs including ongoing costs of monitoring and it is unclear whether optimism of a deus ex machina solution is well-placed.
Conceptual/argumentative claim made by the authors in the paper (no empirical study or sample size reported in the abstract).
Mandatory release delays can paradoxically reduce deployed model quality by shifting preemption to the announcement stage, where quality locks in before the mandated waiting period.
Model extension analyzing mandatory waiting periods: equilibrium strategic behavior shifts to earlier announcements and quality commitment, yielding lower quality at deployment than without the delay.
Premature release imposes safety externalities on society that firms do not fully internalize.
Model assumption and subsequent analysis: the paper models a socially harmful safety externality from early deployment that firms ignore (or undervalue) in their private payoff calculations.
Equilibrium release occurs strictly before the social optimum.
Analytic characterization of the symmetric Nash equilibrium in a theoretical preemption game where firms trade off development time (quality) against first-mover advantages; comparative statics show equilibrium release time < socially optimal release time.
Enterprise adoption of LLMs is constrained by hallucination, domain drift, and the inability to enforce regulatory compliance at the reasoning level.
Framed as the motivating problem in the paper's introduction/abstract (conceptual claim; no empirical test reported here).
No regulatory framework requires disclosure of machine/AI labor output.
Author's assertion in the paper (policy claim; no legislative survey or quantification reported).
No index tracks machine labor output over time.
Author's assertion in the paper (stated lack of existing indices; no systematic review/sample reported).
This labor force is entirely invisible to the economic infrastructure humanity has built to measure work: no standardized unit of measurement exists.
Author's assertion/diagnosis in the paper (argumentative/observational, no empirical survey or sample reported).
New mechanisms of surplus value distribution operate in platform-based business models and through AI-mediated processes.
Analytical/theoretical argumentation and literature synthesis in the conceptual paper (no empirical validation reported).
Unbalanced or poorly governed adoption of Big Data and AI contributes to increased systemic risk, cybersecurity vulnerability, regulatory fragmentation and third-party dependence on BigTech platforms.
Argument based on qualitative literature review and synthesis of international empirical studies and comparative sector analysis; no single-sample empirical study in this paper.
Extreme automation (high AI intensity) causes employment decline.
Part of the U-shaped relationship reported by the paper's empirical results; described qualitatively in the abstract/summary.
The environmental impact of AI is weaker in energy-efficient countries.
Heterogeneity analysis in the paper dividing sample by energy-efficiency status (energy-efficient vs. energy-inefficient countries) shows a smaller AI→CO2 association in energy-efficient countries (104-country panel, 2000–2023).
Advanced digital infrastructure (DII) significantly mitigates the positive effect of AI on CO2 emissions.
Moderation analysis in the panel regressions (104 countries, 2000–2023) including interaction terms between AI adoption and digital infrastructure; results reported that stronger DII reduces the environmental impact of AI.
High institutional quality (GQI) significantly mitigates the positive effect of AI on CO2 emissions.
Moderation analysis in the panel regressions (same 104-country sample, 2000–2023) including interaction terms between AI adoption and governance quality; reported results indicate the AI→CO2 effect is weaker when GQI is stronger.
The literature shows persistent gaps in empirical validation, standardized evaluation methods, and sector-specific comparative analyses of agentic AI in financial services.
Review-level assessment noting limited empirical studies, heterogeneous evaluation metrics, and few direct cross-sector comparisons up to mid-2024.
Significant implementation barriers persist, notably workforce transformation challenges, legacy system integration difficulties, and trust deficits.
Thematic synthesis across empirical and conceptual papers in the review reporting implementation barriers and change management issues.
Ethical concerns—including bias, lack of transparency, and regulatory compliance risks—remain critical for agentic AI in financial services and necessitate layered governance and human-AI collaboration.
Collation of ethical, legal, and governance issues reported across the reviewed multidisciplinary studies and normative discussions.
Insurance is comparatively underrepresented in the literature and in reported agentic AI deployments compared with banking and investment.
Review finding (counts/themes across included studies indicating fewer studies/applications in insurance relative to banking and investment).
Kerangka hukum ketenagakerjaan Indonesia saat ini bersifat reaktif, dengan fokus pada kompensasi pasca-PHK yang belum mampu menjawab dampak jangka panjang disrupsi AI.
Analisis normatif terhadap peraturan perundang-undangan dan temuan dari literatur yang ditinjau; kesimpulan yang dilaporkan oleh penulis penelitian.
Belum terdapat pengaturan eksplisit mengenai kewajiban pelatihan ulang (retraining) maupun mekanisme distribusi manfaat teknologi secara adil dalam kerangka hukum ketenagakerjaan Indonesia saat ini.
Temuan dari analisis peraturan perundang-undangan nasional (UU Cipta Kerja dan peraturan turunannya) dan literatur yang dikaji dalam penelitian normatif.