Evidence (7278 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9047 claims
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Productivity
8066 claims
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Governance
7278 claims
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Human-AI Collaboration
6912 claims
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Org Design
4439 claims
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Innovation
4359 claims
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Labor Markets
3652 claims
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Skills & Training
3018 claims
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Inequality
2160 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 795 | 210 | 105 | 955 | 2131 |
| Governance & Regulation | 886 | 414 | 197 | 126 | 1654 |
| Organizational Efficiency | 826 | 204 | 129 | 87 | 1257 |
| Technology Adoption Rate | 681 | 259 | 128 | 110 | 1189 |
| Research Productivity | 464 | 138 | 65 | 349 | 1028 |
| Output Quality | 503 | 196 | 61 | 53 | 813 |
| Decision Quality | 351 | 180 | 84 | 51 | 673 |
| AI Safety & Ethics | 238 | 288 | 71 | 34 | 637 |
| Firm Productivity | 455 | 58 | 92 | 20 | 631 |
| Market Structure | 186 | 172 | 123 | 25 | 511 |
| Task Allocation | 222 | 70 | 76 | 34 | 407 |
| Innovation Output | 238 | 28 | 48 | 18 | 334 |
| Skill Acquisition | 177 | 62 | 62 | 17 | 318 |
| Employment Level | 107 | 57 | 108 | 13 | 287 |
| Fiscal & Macroeconomic | 135 | 72 | 44 | 26 | 284 |
| Firm Revenue | 172 | 50 | 28 | 5 | 256 |
| Consumer Welfare | 121 | 68 | 45 | 12 | 246 |
| Task Completion Time | 183 | 33 | 10 | 13 | 240 |
| Inequality Measures | 45 | 126 | 50 | 6 | 227 |
| Worker Satisfaction | 95 | 74 | 23 | 12 | 204 |
| Error Rate | 77 | 98 | 11 | 4 | 190 |
| Regulatory Compliance | 84 | 73 | 17 | 7 | 181 |
| Automation Exposure | 61 | 61 | 27 | 14 | 166 |
| Training Effectiveness | 98 | 21 | 14 | 19 | 154 |
| Wages & Compensation | 78 | 37 | 25 | 6 | 146 |
| Developer Productivity | 105 | 18 | 14 | 6 | 144 |
| Team Performance | 87 | 17 | 28 | 10 | 143 |
| Job Displacement | 12 | 83 | 23 | 1 | 119 |
| Hiring & Recruitment | 53 | 8 | 8 | 3 | 72 |
| Social Protection | 39 | 17 | 8 | 2 | 66 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 50 | 6 | 1 | 62 |
| Labor Share of Income | 17 | 20 | 17 | — | 54 |
| Worker Turnover | 15 | 15 | — | 3 | 33 |
| Industry | — | — | — | 1 | 1 |
Governance
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AI-powered EPM heightens employee engagement.
Reported as an aggregated finding in the scoping review of 29 empirical studies.
AI-powered EPM increases the frequency of feedback to employees.
Stated as a benefit in the scoping review synthesis across included studies (n=29).
AI-powered EPM platforms result in considerable improvements in efficiency, including increased frequent feedback, heightened employee engagement, identification of potential leaders and significant time savings for managers.
Synthesis claim from the scoping review of 29 empirical studies; no quantitative effects reported in the excerpt.
The delivery of high-quality healthcare depends essentially on the effective functioning of personnel, who are the vital resource for maintaining reputation, fostering a culture of continuous improvement, and ensuring the overall effective operation of the healthcare sector.
Conceptual assertion in the paper supported by literature synthesis in the scoping review (29 studies).
Modern methodological assessment emphasizes the importance of recording individual contribution in various areas, assessing not only the fulfillment and quality of assignments, but also aspects such as collaboration, creativity, innovative behavior and professional growth.
Descriptive conclusion from the scoping review synthesizing themes across 29 empirical studies (2020–2025).
Employee Performance Management (EPM) systems are undergoing a pivotal shift from annual manual data collection ... into more agile human research operations.
Claim summarized from the scoping review of 29 empirical studies (PRISMA-ScR adherence stated).
Developing an integrated national AI strategic framework is critically necessary to position Georgia as a regional technological leader.
Method: policy recommendation derived from the paper's sectoral analysis and comparative study of successful national strategies; argumentative/ normative claim rather than experimental evidence.
An effective AI ecosystem requires an adaptive regulatory framework, infrastructural investments, the integration of ethical standards, and cross-sectoral coordination.
Method: synthesis of findings from the comparative policy analysis and literature; policy prescription based on observed patterns across successful national strategies.
Countries such as Singapore, the United Kingdom, Canada, and France have achieved AI policy success through institutional flexibility and targeted policies independent of the dominant USA and China models.
Method: comparative analysis of national AI strategies and institutional arrangements across the four named countries; qualitative assessment (no numeric sample size).
The paper analyzes the sectoral economic effects of AI using projections from Goldman Sachs, McKinsey, Penn Wharton, and the IMF, and assesses the potential for technology integration in Georgia's finance, healthcare, and education sectors.
Method: synthesis/analysis of published projections from Goldman Sachs, McKinsey, Penn Wharton, and IMF applied to Georgia's sectoral context; comparative assessment of applicability to finance, healthcare, education in Georgia. (No sample size reported.)
Enterprise capability adaptation serves as the key support for implementing intelligent international marketing models.
Conclusion from the paper's review and content analysis of literature (2010–2025); presented as a synthesized enabling factor rather than empirically quantified effect.
Mainstream innovation models include data-driven precision marketing, AI-powered cross-border CRM, intelligent omnichannel integration, and cross-cultural intelligent localization marketing.
Summary from the paper's systematic review and content analysis of core literature (2010–2025); descriptive synthesis, no primary experimental sample size reported.
New theoretical frameworks have emerged: data-driven precision marketing theory, nonlinear customer journey reconstruction theory, cross-border intelligent value co-creation theory, and global intelligent marketing ecosystem theory.
Identified via the paper's systematic review and content analysis of literature from 2010–2025; presented as conceptual/theoretical developments rather than quantified empirical effects.
Intelligent technologies have increased international marketing ROI by 12%–25%.
Mixed-method systematic review and content analysis of core literature sources from 2010 to 2025 (as reported in the paper). No primary dataset or sample size reported for this quantified range.
Focusing on observation instead of prediction, and governance rather than control, complements existing alignment and safety practices while preserving human judgment, institutional choice, and long-term wellbeing.
Normative argument presented in the paper linking observational monitoring to governance objectives; no empirical evaluation provided.
Interpretable, aggregate behavioral signals (as described) support human-in-the-loop interpretation and enable earlier awareness of when AI use patterns may be drifting from creative augmentation toward automation pressure, authority substitution, or unintended displacement of human agency.
Conceptual claim about intended use of monitoring signals; no empirical test or sample presented.
A system-level framework for externalized behavioral monitoring should treat generative AI systems as participants in socio-technical ecosystems rather than static tools, emphasizing interpretable, aggregate behavioral signals such as shifts in output velocity, semantic and structural reuse, persistence of synthetic roles, and cross-context propagation.
Proposed conceptual framework and list of candidate behavioral signals in the paper (design/specification, no empirical validation).
Post-deployment observability is a foundation for well-being-aligned human–AI co-evolution.
Conceptual argument and system-level framework presented in the paper (no empirical study or sample reported).
The ML community should adopt PBOS as its default contract for such collaborations.
Normative recommendation by the authors; presented as a conclusion/proposal rather than empirically validated policy.
The boundary could not have been drawn correctly without scientists at the negotiating table.
Normative/analytic claim offered by the authors asserting the necessity of scientist involvement in contract design; no empirical evidence provided in the excerpt.
This boundary (pre-training open / post-training proprietary) is technically meaningful, legally clean, and auditable.
Claim about the properties of the PBOS boundary presented as an argument/claim in the paper; no empirical/legal audit data provided in the excerpt.
PBOS: pre-training artifacts (architectures, training code, benchmarks, untrained weights) are open science; post-training artifacts (weights trained on proprietary data) are business IP.
Proposed contract template/definition presented in the paper (prescriptive/design proposal); no empirical validation reported in the provided text.
Future research should focus on empirical assessments of the economic ramifications of artificial intelligence, particularly regarding productivity enhancement, labour market restructuring, and equitable income distribution.
Recommendation in the paper's discussion/conclusion based on identified gaps and the theoretical model (no empirical study presented to support specific magnitudes).
Regulatory bodies should ensure access to data, support platform markets, and promote that artificial intelligence redistributes wealth among the owners of capital, data and labour.
Normative recommendation grounded in the paper's theoretical-legal model and comparative policy discussion (method: deductive/inductive reasoning; no empirical intervention or evaluation).
The European Union has established a comprehensive legal and regulatory framework for the digital economy and artificial intelligence, including rules on platform usage, digital goods liability, data protection (GDPR), and AI.
Comparative legal review of EU regulations and statutes described in the paper (method: comparative approach).
The rise of digital technologies and artificial intelligence will dramatically improve the way existing economic systems function.
Theoretical synthesis and comparative legal analysis presented in the paper; no empirical data or sample reported (methodology: inductive and deductive reasoning, comparative approach).
Empirically stable pricing near the Nash Bargaining benchmark is observed in testing.
Reported empirical observation from experiments across varying population sizes and a 30-day horizon (abstract statement).
Testing across 6–100 agents over a 30-day horizon confirms scalability across population size.
Reported experimental sweep over agent population sizes from 6 to 100 across a 30-day horizon (as stated in abstract).
Nash-guided price proximity rewards align agent learning toward bargaining-optimal strategies.
Algorithmic design claim from the paper: inclusion of a Nash-guided price-proximity reward to shape agent learning (abstract statement).
The paper integrates Nash Bargaining Solution into Multi-Agent Deep Deterministic Policy Gradient, creating Nash-MADDPG, where Nash bargaining determines efficient bilateral pricing.
Methodological claim describing the proposed algorithm and role of Nash bargaining (as stated in abstract).
Nash-MADDPG achieves superior fairness, showing a 40.1% improvement in Jain's index.
Reported fairness metric (Jain's index) improvement in the paper's evaluation over a 30-day horizon (abstract statement).
Nash-MADDPG yields a 62.9% improvement in trading volume over Double Auction.
Reported comparison versus Double Auction in the paper's 30-day continuous-operation evaluation (abstract statement).
Nash-MADDPG improves social welfare by 61.6% over Double Auction in evaluation over 30-day continuous operation.
Simulation evaluation reported in the paper: 30-day continuous operation comparison against Double Auction baseline (as stated in abstract).
The paper proposes a multi-layered governance framework combining core regulatory requirements with supporting ecosystem measures to ensure accountability, security, and transparency in the age of autonomous financial agency.
Policy proposal presented in the paper (concluding recommendation summarized in the abstract).
These results position PRISM-Coach as a practical blueprint for privacy-by-design adaptive learning systems in everyday wellness.
Authors' interpretation in paper based on the implemented system and evaluation results (telemetry + survey + matched comparison).
92% report increased privacy confidence after transparency disclosures.
In-app needs assessment survey reported in paper; percentage stated (92%). Sample size for survey not given in abstract.
Survey results show that 82% report positive perceived benefit.
In-app needs assessment survey reported in paper; percentage stated (82%). Sample size for survey not given in abstract.
In the matched comparison, AI-enabled workflow yields higher average weight loss: 5.2 kg versus 3.1 kg.
Matched 19-week comparison window reported in paper; average weight loss numbers provided (5.2 kg vs 3.1 kg); sample size not stated in abstract.
In a matched 19-week comparison window, the AI-enabled workflow achieves adherence of 0.74 versus 0.48 under static grouping.
Matched 19-week comparison window reported in paper; comparison of AI-enabled workflow vs static grouping; sample size for comparison not stated in abstract.
At the population level, daily check-in adherence increases from 0.35 to 0.68.
Three years of telemetry from ~2,800 users reported in paper (population-level metrics).
PRISM-Coach was instantiated in a commercially deployed lifestyle coaching platform and evaluated using three years of telemetry from approximately 2,800 users and an in-app needs assessment survey.
Reported deployment and evaluation details in paper; telemetry period = 3 years; approximate user count = 2,800; survey described.
A human-in-the-loop coaching assistant generates de-identified summaries and draft messages without sending raw PII or PHI to external AI services.
System design and implementation described; claimed as part of instantiated PRISM-Coach deployment.
The system uses a privacy-constrained contextual bandit to assign users to eligible peer groups under coach-capacity and stability constraints.
Algorithmic method described in paper; implemented in the deployed system.
The system uses vault-based controlled identity restoration.
Method/architecture description in paper; implemented as part of instantiated platform.
PRISM-Coach separates each user into four bounded views: Identity, Operational, Learning, and Coaching, each with distinct access controls and risk profiles.
System architecture described in paper; implemented design (instantiated) reported.
We recommend that LLM forecasting evaluations use continuous (and unbounded) measures of accuracy alongside bounded binary threshold metrics.
Recommendation based on the paper's empirical findings that binary threshold metrics miss upper-tail costs while continuous/tail-inclusive metrics reveal inverse-scaling effects; rationale provided by experimental comparisons (empirical support described in paper).
Community and Indigenous approaches offer alternative models of authority over AI infrastructure rooted in stewardship rather than extraction, although these approaches are constrained.
Normative argument and engagement with community/Indigenous scholarship and examples; presented as an alternative model in the paper (qualitative).
Scholarly and empirical research should prioritize multilevel analysis, algorithmic governance, and ethical considerations to study the AI-infused strategic landscape.
Paper's concluding research agenda based on gaps identified in the conceptual analysis; prescriptive recommendation rather than empirical finding.
Adoption under higher communicative standards and institutional norms can mitigate suboptimal collective equilibria by imposing social commitments on individual users.
Theoretical argument and model-based analysis proposing communicative and institutional interventions as mitigating mechanisms (conceptual and formal reasoning).
Individually stable strategies can be scaled to collective equilibria using three extrapolation principles: (a) non-communicative aggregation, (b) local social signaling, and (c) institutional norms setting.
Theoretical extrapolation/principled modeling presented in the paper (conceptual and formal extension from individual to collective level).