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 systems have access to reams of sensitive user data.
Stated as a factual consequence of the described integration (conceptual observation in the paper); no empirical measurement or dataset cited in the excerpt.
An analysis of LL144 audit reports reveals demographic missingness ranging from under 3% to over 50%, which reduces the applicant pool used for fairness calculation and undermines the metrics.
Empirical analysis of LL144 audit reports reported in the paper (specific sample size not given in the excerpt); quantitative range for missingness reported as 'under 3% to over 50%'.
AI adoption has the potential to amplify systemic vulnerabilities in financial markets.
Comparative institutional analysis and qualitative evidence across China, the United States, and the United Kingdom (2022–2025) reported in the abstract noting potential amplification of systemic vulnerabilities linked to AI.
AI adoption introduces governance challenges related to algorithmic bias.
Comparative mixed-methods evidence (secondary quantitative indicators and documentary qualitative evidence) across China, the United States, and the United Kingdom (2022–2025) as summarized in the abstract.
AI adoption introduces governance challenges related to model opacity.
Qualitative documentary evidence and comparative analysis across the three jurisdictions (China, US, UK, 2022–2025) reported in the abstract indicating governance concerns, including model opacity.
These systems create governance challenges that are not fully captured by traditional software or predictive ML technical debt.
Argumentative claim in the paper contrasting agentic-system risks with traditional software/ML technical debt; no empirical validation or comparative study reported.
Cloud orchestrators follow efficiency-oriented logics of integration and standardization with limited openness.
Claim presented as a finding from the paper's comparative taxonomy and qualitative analysis of platform business models; method appears to be conceptual/qualitative comparison rather than a reported quantitative sample (no sample size in abstract).
Achieving this system-level transformation takes time: it requires trust and accountability infrastructure, machine-legible and interoperable data and interfaces, the design and adoption of these new workflows, and economic incentives that favor reconstruction rather than local optimization.
Argumentative claim listing necessary preconditions and complementary investments; presented conceptually without reported empirical measurement in the provided text.
The main reason [the disruption has not fully arrived] is not model capability, nor even the tools built to harness those models; rather, most organizations are still using AI to accelerate workflows designed for a pre-AI world.
Argued in the paper as an explanatory thesis; supported by conceptual argument and illustrative examples (consumer markets, education, news, coding) rather than reported empirical analysis in the provided text.
The disruption many expect has not fully arrived.
Stated as an observation in the paper's introduction/abstract; no empirical method, sample size, or data reported in the excerpt.
Reputation mechanisms presuppose persistent identity, behavioral continuity, sanction sensitivity, and costly non-fungibility; absence of any of these undermines reputation systems.
Analytic claim in the paper articulating necessary conditions for reputation mechanisms to function; presented as theoretical grounding rather than empirically tested criteria.
The analogy from human identity verification and reputation mechanisms (e.g., 'Know Your Customer', credit scores) to 'Know Your Agent' regimes is fundamentally incomplete.
Comparative conceptual argument in the paper highlighting disanalogies between human actors and modular language model agents; no empirical comparison or data provided.
Identity-based, ex post, regulative, sanction-based governance, such as reputation, is structurally inapplicable to dissociative agents.
Normative/theoretical argument in the paper deduced from properties of dissociative agents and requirements of identity-based governance; no empirical or experimental support reported.
Dissociativity leaves agents without grounding for identifiability, predictability, credibility, and rehabilitability — the very properties that reputation mechanisms aim to sustain — thereby collapsing trust.
Conceptual inference in the paper combining the dissociative characterization of agents with the functional requirements of reputation systems; no empirical validation provided.
An agent's persona is fluid, vulnerable to adversarial attack, and may not internalize sanctions.
Argumentative claim in the paper citing susceptibility of modular agent components (prompts, tools, memory) to manipulation; no empirical attack experiments or sample sizes reported.
Language model agents are ontologically dissociative: they are essentially an assemblage of mutable modules -- foundational models, system prompts, tool-access policies, external memory, and, in some cases, a multi-agent system as a whole -- any of which may change agent behavior.
Theoretical characterization and system-level description in the paper; lists components that can be changed to alter behavior; no empirical measurement or sample reported.
These results suggest the problem is not in any specific auditor but in any audit whose evidence comes from the audited party.
Synthesis and conclusion drawn from the authors' analyses and experiments across the studied auditing frameworks.
We call this a trust paradox: every audit must trust some artifact, but current frameworks trust exactly the ones a provider has the strongest reason to manipulate.
Conceptual framing and critique of existing auditing frameworks (argument/analysis in paper).
The audit therefore reduces to a consistency check on the provider's own reports.
Logical implication derived from the provider-controlled hiding of model/tokenizer/execution (argument/analysis in paper).
Providers hide the model, the tokenizer, and the execution to protect their IP, mitigate jailbreaks, and preserve user privacy, which means an auditor can only inspect proofs the provider supplies.
Conceptual/architectural claim about current commercial provider practices and their implications for auditability (argumentation in paper).
Limited data, resource constraints and skill gaps significantly influence the pace and form of AI adoption in SMEs.
Synthesis of barriers identified across multiple studies in the 2016-2024 literature (review-level claim without a single quantitative estimate).
Ethical concerns—especially algorithmic bias—and the need for human oversight remain essential for ensuring positive financial outcomes.
Argument and synthesis from the reviewed literature highlighting ethical risks and recommended governance (conceptual and empirical discussions across studies).
SMEs face barriers to AI adoption such as limited data, skill shortages, and high implementation costs.
Review synthesis of barriers reported in multiple studies from 2016-2024 (no pooled quantitative prevalence reported).
Existing AI education, AI literacy, and human-AI collaboration frameworks remain centred on prompting, task execution, and productivity support and are poorly equipped to address this tacit layer of expert cognition.
Argumentative critique in the paper drawing on conceptual analysis and review of prevailing frameworks; no empirical evaluation or sample reported.
Incumbent workforce management frameworks remain anchored to a purely human labor model, rendering AI agents invisible to capacity planning, performance attribution, and governance enforcement.
Stated in paper's problem motivation / literature review; presented as an observed gap motivating the design-science contribution. No sample size or empirical study described in the provided text.
AI may influence society broadly via ethical issues, economic inequality, and social adaptation challenges.
Paper lists ethics, economic inequality, and social adaptation as societal-level areas affected by AI (abstract). Presented as thematic concerns reviewed in the paper; no empirical estimates included in the provided text.
AI-driven automation is associated with job loss.
The paper lists automation and job loss among the areas it examines (abstract). The provided text frames job loss as a potential negative ramification but does not report primary empirical estimates or sample sizes.
Existing approaches remain fragmented across formal verification, runtime assurance, neuro-symbolic reasoning and trustworthy Artificial Intelligence (AI) research communities.
Author claim about the state of the research landscape; asserted fragmentation without bibliometric or survey data provided in excerpt.
Current reasoning systems still suffer from hidden logical inconsistencies, hallucinated symbolic transitions, unsupported theorem applications, and limited reliability guarantees.
Author assertion identifying failure modes of current reasoning systems; presented qualitatively without quantitative error rates or experimental sample sizes in the excerpt.
Translators have functioned as 'invisible teachers' of AI—through the construction of translation memories, post-editing, and quality assessment—without recognition as teachers of models.
Conceptual framing and synthesis of workflow practices (TM construction, post-editing, QA) and their role as supervision for ML; qualitative argument and illustrative examples in the paper. No quantitative sample reported.
Translators' renditions have been bought as deliverables under contract, segmented as technical objects, and processed as 'information analysis' data under copyright law—resulting in the loss of moral, creative, and economic attribution to the translators who produced them.
Comparative reading of contract practices and copyright treatment (legal/contractual analysis across jurisdictions), descriptive examples of how translations are delivered, segmented, and processed; qualitative argumentation in the paper. No quantitative sample reported.
Existing legal perspectives on the intellectual property of AI-generated works and related enforcement challenges are inadequately addressed under current frameworks.
Analytic review of legal perspectives and enforcement issues presented in the paper; conclusion based on the author's analysis rather than quantitative data.
The current Iranian legal framework contains significant regulatory gaps with respect to intellectual property protection for AI-generated works.
Comparative legal analysis of Iranian statutes (1969 Law for the Protection of Authors, Composers, and Artists Rights and the Patent and Trademark Registration Law) against other legal systems (European Union, United Kingdom, United States); the paper's findings are based on legal/textual analysis rather than empirical sampling.
The most critical intellectual property issue raised by AI-generated outputs is ownership of moral and economic rights in the absence of a human creator.
Theoretical discussion and literature review presented in the paper identifying legal and doctrinal questions around authorship and ownership when no human creator is involved (no empirical sample size).
The impact of EPU on ETM is relatively moderate in intensity but more persistent compared with the impact from AI.
GARCH-Conditional Quantile Regression (persistence measures reported in the study summary; exact metrics/sample size not provided).
The risk spillover from AI to ETM is characterized by high volatility and strong extremeness.
GARCH-Conditional Quantile Regression results showing AI→ETM spillover features (method reported; sample size not stated).
A GARCH–conditional quantile regression model reveals asymmetry of risk spillovers: the intensity of upside risk spillovers is far greater than downside ones.
GARCH-Conditional Quantile Regression (GARCH-CQR) applied to volatility and tail-risk dynamics among AI, EPU and ETM (method reported; sample size/time-series length not stated).
The cross-quantilogram indicates that the negative predictive effect of EPU on ETM is mainly concentrated in periods of policy stability.
Cross-quantilogram analysis applied to EPU and ETM time-series, with quantile-specific predictive effects identified (method reported; sample size not stated).
The nonparametric quantile causality test shows a unidirectional causal relationship from EPU to China’s education and training market (ETM).
Nonparametric quantile causality test applied to time-series data on EPU and ETM in China (method reported; sample size not stated).
There is an urgent question of how humans can effectively supervise and control an economy operated by AI agents when this system may expand beyond the capacity of traditional governance.
Framed as a central research/policy concern in the paper's abstract; conceptual argument rather than empirical finding.
The Agent Economy raises new regulatory challenges concerning data privacy, security, ethics, and the risk of job displacement.
Stated in paper abstract as identified risks; based on literature synthesis and comparative policy analysis approach (method described), but no empirical incidence metrics reported.
Organizations implementing AI without responsible transition mechanisms may worsen workforce anxiety, skill obsolescence, inequality, and trust erosion.
Paper's theoretical/conceptual assertion about risks of poorly-managed AI adoption; no empirical validation reported in the excerpt.
The International Monetary Fund estimates that nearly 40% of global employment is susceptible to AI, with exposure rising to 60% in advanced economies owing to cognitive task-oriented jobs.
Cited IMF estimate reported in the paper (reference to an IMF analysis; no sample size given in the excerpt).
Tenure negatively relates to AI use (OR = 0.846 per category).
Reported odds ratio from logistic regression for tenure categories predicting AI use; OR = 0.846 per tenure category.
LLM-assisted discovery can increase report volume while maintainer-side validation, triage, funding, and release capacity may not scale—an effect that is acute in open source.
Claims supported by case material from Mozilla Firefox collaborations and Anthropic Mythos Preview public data, plus discussion of open-source maintainer constraints; no sample size given in the abstract.
The resulting bottleneck is not only finding more bugs; it is absorbing, validating, triaging, patching, and shipping a larger stream of reports.
Argument based on observed changes in report volume and workflow demands from public collaborations and market/program data referenced in the paper; exact empirical counts not provided in the abstract.
Under water-constrained conditions, the framework achieves reductions of approximately 3-5% in generation-related freshwater withdrawals.
Quantitative results from simulation case studies on the IEEE test systems (reported percentage reduction ~3-5%); sample context: water-constrained simulation scenarios on IEEE 30-bus and 118-bus systems (sample_size = 2 test systems).
Because they are decoupled from the optimization process, static statistical accounting approaches are incapable of guiding workload relocation or power dispatch to mitigate water stress.
Argumentative claim in paper about limitations of static accounting methods with respect to guiding operational decisions (methodological critique).
Existing approaches typically rely on static statistical accounting to quantify these water footprints, but such static methods fail to capture how dispatch optimization and workload relocation dynamically affect water withdrawals.
Critical assessment in paper contrasting prior static statistical accounting approaches with dynamic needs; presented as methodological critique (no particular empirical sample in excerpt).
AI-driven efficiency pressures in IT services may compress billable work and alter hiring and wage structures, raising transition risks even for technical workers.
Abstract cites high-reliability sector evidence (Reuters 2026a; Nasscom) to support this sector-specific claim; no sample size provided in abstract.