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 |
The negative effect of AI avoidance job crafting on career-relevant outcomes (career satisfaction and performance) is mediated by increased work alienation.
Mediation analysis on the multi-wave, multi-source survey data (287 employee–leader dyads) showing a pathway from AI avoidance job crafting → work alienation → worse career outcomes.
AI avoidance job crafting negatively predicts career satisfaction and performance.
Multi-source, multi-wave survey of 287 employee–leader dyads in China linking employee-reported AI avoidance job crafting to lower career satisfaction and lower performance.
AI-driven job displacement disproportionately affects low-skilled workers.
Reported empirical result from the paper's PLS-SEM analysis on the 351-respondent dataset.
Traditional car-following models, such as the Intelligent Driver Model (IDM), often struggle to generalize across diverse traffic scenarios and typically do not account for fuel efficiency.
Literature-based statement within the paper motivating the study (review of limitations of traditional car-following models). No sample size reported.
Analysis of global datasets on energy dependency, economic concentration, debt levels, demographic trends, digital infrastructure, and AI adoption highlights that interconnected systemic risks can amplify economic instability.
Paper reports drawing upon multiple global datasets (energy dependency, economic concentration, debt, demographics, digital infrastructure, AI adoption) to analyze systemic risk interactions; specific datasets, sample sizes, and statistical methods are not detailed in the excerpt.
Events such as supply chain disruptions, oil price surges linked to geopolitical conflicts, and sudden labour market shifts due to reverse migration have exposed the limitations of prediction-based planning frameworks.
Illustrative examples cited in the paper; the claim is supported by referenced global events and the paper's use of global datasets, but no specific empirical case-study sample sizes or quantification are provided in the excerpt.
Traditional economic models that rely heavily on historical data and linear forecasting are increasingly inadequate in capturing the complexity and unpredictability of contemporary economic shocks.
Conceptual claim supported by discussion and examples of recent shocks (supply chain disruptions, oil price surges, labor market shifts); no specific empirical evaluation or quantified model comparison reported in the excerpt.
The global economic system is undergoing a structural transformation characterized by geopolitical tensions, energy price volatility, trade fragmentation, demographic imbalances, and rapid technological disruption driven by artificial intelligence.
Narrative synthesis in the paper drawing on global trends; the paper references global datasets on energy dependency, trade patterns, demographics, and AI adoption (no specific sample size or empirical study detailed in the excerpt).
The main risk is not merely copying, but the possibility that useful capability can be transferred more cheaply than the governance structure that originally accompanied it.
Conceptual threat model articulated in the paper; argued on normative/theoretical grounds without reported empirical measurement or sample.
Distillation becomes less valuable as a shortcut when high-level capability is coupled to internal stability constraints that shape state transitions over time.
Theoretical argument presented as the paper's core claim; introduces a conceptual mechanism (capability-stability coupling) and argues why this would reduce the usefulness of distillation. No empirical data, experiments, or sample are reported.
Hallucination and content filtering are the most common frustrations reported across all platforms.
Qualitative and/or survey-coded responses about user frustrations aggregated across platforms (overall N=388); paper reports these two issues as the most common.
The competence shadow compounds multiplicatively to produce degradation far exceeding naive additive estimates.
Analytic/closed-form performance bounds derived in the paper showing multiplicative compounding (theoretical result; no empirical sample reported).
The competence shadow is a systematic narrowing of human reasoning induced by AI-generated safety analysis; it is defined as not what the AI presents, but what it prevents from being considered.
Conceptual definition and formalization within the paper (theoretical exposition; no empirical test reported).
Safety engineering resists benchmark-driven evaluation because safety competence is irreducibly multidimensional, constrained by context-dependent correctness, inherent incompleteness, and legitimate expert disagreement.
Conceptual/theoretical argument and formalization presented in the paper (no empirical sample reported).
In experimental settings, the model is able to induce belief and behaviour changes in study participants.
Controlled experimental interventions reported in the study where participant beliefs and behaviors were measured pre/post or between conditions; aggregate result: model induced changes.
The tested model can produce manipulative behaviours when prompted to do so.
Human-AI interaction tests in which the model was prompted to produce manipulative behaviours; empirical observations reported in study across participants and prompts.
Standard evaluation of LLM confidence relies on calibration metrics (ECE, Brier score) that conflate two distinct capacities: how much a model knows (Type-1 sensitivity) and how well it knows what it knows (Type-2 metacognitive sensitivity).
Authors' conceptual argument and motivation for introducing a new evaluation framework; contrasted standard calibration metrics (ECE, Brier) with Type-1 vs Type-2 capacities in the paper's introduction and methods.
Traditional expert-based assessment faces a critical scalability challenge in large systems (e.g., serving 36 million children across 250,000+ kindergartens in China), making continuous quality monitoring infeasible and relegating assessment to infrequent episodic audits.
Authors' contextual motivation citing scale figures (36 million children, 250,000+ kindergartens) and describing time/cost constraints of manual observation leading to infrequent audits.
There is a significant boundary in the reverse confidence scenario: a substantial proportion of participants struggled to override initial inductive biases and thus had difficulty learning in that condition.
Behavioral experiment (N = 200) reporting that many participants failed or struggled in the reverse confidence mapping condition; proportion described in paper (exact proportion not given here).
Preliminary evaluation reveals that current foundation action models struggle substantially with professional desktop applications (~60% task failure rate).
Preliminary empirical evaluation reported by the authors; reported task failure rate ~60% (no sample size provided in abstract).
The largest existing open dataset, ScaleCUA, contains only 2 million screenshots, equating to less than 20 hours of video.
Quantitative statement about ScaleCUA reported in paper: 2,000,000 screenshots and <20 hours equivalence.
Progress toward general-purpose CUAs is bottlenecked by the scarcity of continuous, high-quality human demonstration videos.
Asserted in paper as motivation; refers to the gap in available continuous video data for training CUAs.
Refining the state (as above) raises state-action blind mass from 0.0165 at \tau=50 to 0.1253 at \tau=1000.
Empirical measurement reported on the instantiated model over the BPI 2019 log showing state-action blind mass values at two threshold (tau) settings.
Empirical evidence shows that many failures arise from miscalibrated reliance, including overuse when AI is wrong and underuse when it is helpful.
Paper cites empirical literature (unspecified in excerpt) as the basis for this claim; no sample size or methods given here.
Evaluation practices focus primarily on model accuracy rather than whether human-AI teams are prepared to collaborate safely and effectively.
Paper-level critique / literature observation asserted in text; no empirical method or sample reported in excerpt.
The reduction in engagement from AI labeling (AI-generated or AI-enhanced) was particularly pronounced for emotional content compared to rational content.
Interaction of content type (emotional vs. rational) with labeling in the two online experiments (study 1: n = 325; study 2: n = 371) reported in the abstract.
Labeling content as AI-enhanced reduced both affective and behavioral engagement compared to human-created content.
Same two online experiments on Prolific (study 1: n = 325; study 2: n = 371) where participants viewed Instagram profiles labeled as human-created, AI-enhanced, or AI-generated.
Labeling content as AI-generated reduced both affective and behavioral engagement compared to human-created content.
Two online experiments conducted via Prolific (study 1: n = 325; study 2: n = 371). Participants viewed Instagram profiles containing visual content labeled as human-created, AI-enhanced, or AI-generated and engagement was measured.
Currently, the region remains reactive as a 'recipient' rather than a 'creator' or an effective partner in the AI ecosystem.
Characterization reported by the authors based on their regional research and field study (qualitative findings from leaders across public/private sectors).
This gap hinders the ability of many governments in the region to push their countries toward joining the ranks of those benefiting from the AI revolution—both in developing the public sector and supporting economic growth and social development.
Authors' analysis and interpretation based on the regional research/field study described in the report.
The Arab region’s capacity for Artificial Intelligence (AI) governance remains limited relative to the accelerating pace of global AI developments and associated challenges.
Stated conclusion in the executive report based on a regional field study (authors' analysis of interviews/surveys and research across the region).
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).
Reliance on massive, schema-heavy prompts results in prohibitive per-token API costs and high latency, hindering scalable production deployment.
Introductory problem statement in the paper arguing that large context prompts increase per-token API costs and latency for API-based LLMs; no quantitative study or sample size provided for this claim within the excerpt.
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).
AI-enabled, democratised production is more likely to intensify competition and produce winner-take-most outcomes than to generate broadly distributed entrepreneurial success.
Synthesised theoretical prediction based on the unified framework (attention scarcity + free-entry dilution + superstar/preferential attachment dynamics) developed in the paper; no empirical validation provided.
When the framework is extended to include quality heterogeneity and reinforcement dynamics, equilibrium outcomes exhibit declining average payoffs.
Analytical extension of the baseline formal model to incorporate heterogeneous quality and reinforcement (preferential attachment) dynamics; theoretical derivation in the paper; no empirical sample.
In markets with near-zero marginal costs and free entry, increases in the number of producers dilute average attention and returns per producer.
Formal theoretical model introduced in the paper (Builder Saturation Effect) that assumes near-zero marginal costs, free entry, and finite human attention; no empirical sample or experimental data reported.
Agent memories currently remain private and non-transferable because there is no way to validate their value.
Descriptive assertion in the paper about current state of agent memories; no empirical survey or measurement cited.
Insufficient organizational resources significantly inhibit AI adoption in procurement (β = -0.19, p < 0.05).
Same questionnaire survey (n=326) and multiple linear regression analysis; reported coefficient β=-0.19 with p<0.05.
Measuring only technical model performance (such as predictive accuracy) is insufficient for assessing the strategic impact of AI in drug discovery.
Argued in the paper as a critique of current evaluation practices; presented as a conceptual point rather than supported by new empirical data in the excerpt.
Pressure remains high to increase the probability of success to improve the effectiveness of pharmaceutical R&D.
Asserted in the paper as motivational context for the work; framed as an industry pressure point rather than backed by a specific empirical sample or quantified survey in the excerpt.