Evidence (2066 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 |
Inequality
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Empirical observations show that youth mobilize AI tools for translation, content creation, customer engagement, and micro-entrepreneurial activities, enabling partial and situational approximation of selected formal-sector practices.
Qualitative interview data from the 125 semi-structured interviews in three DRC cities, used as illustrative grounding for observed uses of AI by youth.
Dijital platformlar insan deneyimini veriye dönüştürerek ekonomik değere tahvil eden yeni bir rejim (gözetim kapitalizmi) kurmuştur.
Teorik ve kavramsal analiz; çalışma Zuboff'un gözetim kapitalizmi yaklaşımına atıf yapmaktadır. No empirical sample or quantitative evidence reported.
Policy recommendations: improve digital infrastructure in less-developed areas, support digital upskilling, and strengthen regulations to ensure inclusive and equitable digital development.
Policy conclusions and recommendations derived from the study's empirical results (panel analysis of 31 provinces, 2011–2021) and discussion of distributional implications; these are prescriptive rather than causal findings.
The income-increasing effect of the digital economy operates primarily through wage growth.
Mechanism analysis reported in the paper based on the same two-way fixed effects panel (31 provinces, 2011–2021) that decomposes channels and finds wages as the main mediator.
Digital economic development significantly increases household income in China.
Two-way fixed effects panel regression using provincial-level panel data for 31 Chinese provinces (2011–2021), with robustness checks reported in the paper.
Experts assigned the highest responsibility for addressing these risks to general-purpose AI developers and governance actors (including governments, regulators, and standards bodies).
Delphi ratings of actor responsibility reported in paper: highest responsibility attributed to general-purpose AI developers and governance actors by 272 experts.
The machines are increasingly becoming competent.
Authorial assertion about the trend in AI capability (no metrics or studies provided in the excerpt).
The concept of co-intelligence describes a new cognitive ecology where the human and artificial minds mutually influence one another to come up with ways of comprehending, creating and making choices that neither of them could accomplish individually.
Conceptual claim attributed to Ethan Mollick (2024) and extended by the author — described conceptually rather than demonstrated empirically in the excerpt.
None of the past technologies have spread into so many aspects of human life, so fast.
Author's comparative assertion about the speed and breadth of AI diffusion relative to prior technologies (no empirical comparison provided in the excerpt).
Artificial intelligence has become a partner in our everyday activities: it dictates our emails, diagnoses our diseases, educates our young children, controls our budgets, creates our artworks, and influences the policies made by governments and corporations.
Authorial assertion listing domains of current AI use (no empirical study or quantified data provided in the excerpt).
The internet had to cope with more or less a decade before it could reach one billion users; social media did it in half times.
Comparative historical adoption claim presented by the author (no citation or empirical method given in the excerpt).
Less than a year after its debut, hundreds of millions of individuals on all seven continents were using large language models, in virtually every field of professional activity, and in most languages.
Authorial assertion summarizing global LLM adoption (no specific study, dataset, or methodology provided in the excerpt).
There were now a hundred million ChatGPT users in two months.
Authorial assertion in the text citing a user-count milestone for ChatGPT (no study or data source provided in the excerpt).
AI adoption raises real output.
Panel local projections linking establishment-level AI adoption (share of job postings requiring AI skills) to real output across 13 industries over 2017-2025.
AI adoption raises labor productivity.
Panel local projections estimating the effect of establishment-level AI-skill posting share on labor productivity across 13 industries (2017-2025).
By Round 3, equity-aware LLM refinement reduces energy costs by 3.2%.
Empirical results reported in abstract: energy cost reduction of 3.2% after three rounds of LLM-mediated reward refinement (15 experimental runs).
By Round 3, equity-aware LLM refinement improves satisfaction for Elderly Females (+567%).
Empirical results reported in abstract following three rounds of LLM-based reward refinement; improvement magnitude given as +567%. 15 experimental runs.
By Round 3, equity-aware LLM refinement improves satisfaction for Health Sensitive (+53.8%).
Empirical results reported in abstract following three rounds of LLM-based reward refinement; improvement magnitude given as +53.8%. 15 experimental runs.
By Round 3, equity-aware LLM refinement improves satisfaction for Mid-aged Females (+28.2%).
Empirical results reported in abstract following three rounds of LLM-based reward refinement; improvement magnitude given as +28.2%. 15 experimental runs.
By Round 3, equity-aware LLM refinement improves satisfaction for Young Males (+17.6%).
Empirical results reported in abstract following three rounds of LLM-based reward refinement; improvement magnitude given as +17.6%. 15 experimental runs.
We introduce the Comfort Equity Index (CEI) as a novel feedback signal.
Paper contribution / methodological description introducing CEI (no quantitative validation details reported in abstract).
Compounded through 500 turns of reciprocation, these differentials accumulated into in-group trust biases of +0.014 to +0.100 (d = 0.84-4.52), illustrating how modest per-interaction targeting propagates into structural inequality in persistent networks.
Aggregate/longitudinal result from the simulation after 500 turns: reported cumulative change in in-group trust bias (absolute change +0.014 to +0.100) and reported effect sizes in Cohen's d (0.84–4.52); based on the same experimental setup (6 model families, 20 seeds each).
Per-turn in-group versus out-group differentials of 5 to 16 percentage points were statistically significant for all six models (Wilcoxon signed-rank, all Benjamini-Hochberg-corrected p < 0.001), establishing group-contingent targeting as a robust property of instruction-tuned language models across architectures and training regimes.
Statistical analysis reported in the paper: per-turn differential between in-group and out-group targeting measured as percentages (5–16 percentage points); significance assessed with Wilcoxon signed-rank tests and Benjamini-Hochberg correction; applied across six model families each with 20 seeds.
When group labels were visible, we observed network assortativity (all absent when labels were hidden).
Reported network-level outcomes from the simulation comparing visible vs hidden label conditions across the experimental runs (6 model families, 20 seeds each, 500 turns).
When group labels were visible, we observed action homophily.
Result reported from the simulation comparing visible versus hidden group label conditions across the described experimental runs (6 model families, 20 seeds each, 500 turns).
When group labels were visible, we observed in-group trust bias.
Result reported from the simulation comparing conditions with visible versus hidden group labels; based on interactions of instruction-tuned LLM agents across the reported experimental runs (6 model families, 20 seeds each, 500 turns).
We ran a controlled multi-agent simulation in which instruction-tuned language model agents interacted across 500 turns under three conditions manipulating group label salience and resource scarcity, across six model families with 20 seeds each.
Descriptive methods statement from the paper: controlled multi-agent simulation; instruction-tuned LLM agents; 3 experimental conditions (manipulating group label salience and resource scarcity); 6 model families; 20 random seeds per model; 500 turns per simulation run.
Although AI creates obstacles, it also has the potential to be an important tool for creating innovative opportunities and continued growth if managed with sound practices.
Concluding statement in the paper's abstract presenting a normative/conditional conclusion based on the paper's evaluation and synthesis of evidence (no primary quantified results provided in the supplied text).
AI leads to the creation of new jobs.
The paper explicitly states it examines the creation of new jobs as a ramification of AI (abstract); claim presented qualitatively without reported sample sizes or quantified effect in the provided text.
There exists a data supply chain that runs from individual translators through language service providers (LSPs) and platforms to model developers.
Mapping and descriptive analysis of industry supply chains and intermediary roles provided in the paper; conceptual and empirical examples of flows of translation data from translators to model developers. No numerical sample reported.
Article 30-4 of the Japanese Copyright Act legitimates a mode of use the paper terms 'appropriation without consumption'—i.e., mining works for statistical features rather than reading or experiencing them.
Textual/legal analysis of Article 30-4 of the Japanese Copyright Act and its interpretation; comparative legal reading presented in the paper. No numerical sample reported.
The development of statistical machine translation (SMT), neural machine translation (NMT), the Transformer architecture, and multilingual large language models (LLMs) cannot be disentangled from the accumulation of translation data (TM/parallel corpora).
Historical and technical literature review linking MT/NLP methodological advances to the availability and use of parallel corpora and TM; comparative analysis of model development histories described in the paper. No numerical sample reported.
Translation memories (TM) and parallel corpora preserve a one-to-one correspondence between source and target text and therefore constitute extraordinarily valuable supervised training data for machine translation.
Conceptual argument and literature review of machine translation practice (discussion of TM/parallel corpora as supervised training data); examples and descriptive evidence from MT research and industry practice presented in the paper. No numerical sample reported.
The paper proposes a policy architecture for 'shared gains' centered on learning equity, transition protections, accountable algorithmic management, and distribution-sensitive metrics beyond GDP.
Paper's normative policy proposal presented in abstract, based on the integrative framework and synthesis of secondary sources; no empirical sample size reported.
India's macro growth remains robust.
Statement in abstract referencing official Indian statistics (MoSPI–NSO GDP estimates, 2025); no numerical sample size provided in abstract.
Evidence indicates accelerating AI adoption among firms in advanced economies.
Abstract cites validated secondary sources including OECD (2026) and other global reports; no primary sample size reported in paper abstract.
AI is increasingly embedded in production, services, and workforce management.
Statement in paper's abstract supported by integrative socio-technical political economy framework and validated secondary sources (OECD, ILO, UNDP, WTO, WEF). No primary sample size reported.
Structured AI-based interventions provide causal evidence that they can transform access to scientific feedback from a largely private advantage into a more widely distributed resource.
Causal inference based on randomized field experiment showing increased revision likelihood and broader uptake of LLM tools across diverse regions and author groups.
Effects were strongest among teams with lower h-indexes and earlier career stages.
Heterogeneous treatment effects by team-level metrics (h-index) and career stage reported in the randomized experiment.
Effects were strongest for manuscripts less embedded in the scholarly literature.
Heterogeneous treatment effects reported by manuscript-level embedding in literature (e.g., referencing/citation context) within the randomized experiment.
Effects of AI feedback were strongest among authors from non-English-dominant research regions.
Heterogeneous treatment effects reported in the randomized experiment stratified by authors' geographic / language-dominance region; sample includes authors from 133 geographic regions.
Exposure to AI feedback increased authors' subsequent use of LLM tools in their future papers, suggesting longer-run shifts in scientific practice.
Follow-up measurements in the randomized field experiment tracking authors' later behavior (use of LLM tools in subsequent papers); comparison between treatment and control authors.
Authors who received LLM-generated feedback had a significantly higher likelihood of revising their manuscripts, corresponding to a 12.55% relative increase over the baseline revision rate.
Randomized field experiment comparing treatment (LLM feedback) vs control; sample described as >31,000 arXiv preprints and >45,000 researchers; reported comparative revision rate and statistical significance.
A difference-in-differences design centered on ChatGPT's release supports a causal interpretation of GenAI's local labor-market effects.
Quasi-experimental difference-in-differences analysis using ChatGPT's release as an event/shock, comparing outcomes across neighborhoods with different pre-existing GenAI exposure measures derived from 5 million job postings.
Policy responses should prioritise governance frameworks that emphasise equity, accountability, and inclusive distribution of value to address concentrated digital power.
Normative policy recommendations derived from the paper's conceptual analysis and synthesis of recent literature (policy prescription, no empirical evaluation reported).
Wage gains coincide with an increase in within-firm wage dispersion in small firms, with wage variance rising by around 7.5%.
Within-firm wage variance analysis (likely computed from worker-level wages aggregated to firm-level dispersion) showing a ~7.5% increase in wage variance in small firms after automation adoption.
Using a difference-in-differences framework exploiting import lumpiness in product categories linked to automation technologies, we find a positive average adoption effect on adopters’ average wages, which stabilizes at around 4% five years after an automation spike.
Difference-in-differences (DiD) estimation exploiting time variation in import 'spikes' in automation-related product categories (including robots) on the integrated panel of Italian importing firms (2011–2019).
Mincer-type wage regressions reveal that automation adopters pay approximately 3% higher wages after controlling for worker sorting.
Mincer-style wage regressions with controls for worker sorting (individual-level regression analysis on the integrated dataset).
The automation wage premium for adopting firms stands at approximately 10%.
Descriptive comparison of wages between automation-adopting firms and others using integrated firm-worker-trade data for Italian importing firms (2011–2019).
The paper's contribution is to clarify the trade-offs that infrastructure decisions often obscure, distinguish deliberate triad governance from default allocation by market power or regulatory inertia, and propose a Deliberate Triad Choice Framework for policymakers considering AI infrastructure decisions of significant scale.
Stated contributions in the abstract: conceptual clarification, normative distinction between deliberate governance and default allocation, and proposal of a policy framework (Deliberate Triad Choice Framework).