Evidence (14922 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 |
The proposed framework positions Medicaid procurement as a lever for climate action, health equity, and long-term system resilience.
Theoretical synthesis and policy argumentation drawing on Stakeholder Theory, TBL, and examples from literature and benchmarking (conceptual claim; no empirical outcome data demonstrating realized lever effects).
International benchmarking with the UK National Health Service (NHS) Net Zero strategy demonstrates feasibility and scalability of ESG-integrated procurement approaches.
Comparative case benchmarking using the NHS Net Zero strategy as an international exemplar (qualitative comparative analysis; single-case international comparison; no pilot or implementation data for Medicaid presented).
The paper synthesizes theoretical foundations, operational mechanisms, and policy instruments—particularly Section 1115 waivers—to propose a practical roadmap for embedding ESG principles into Medicaid procurement.
Policy analysis and literature synthesis combining theoretical discussion with review of policy tools (Section 1115 waivers singled out); the roadmap is a proposed construct in the paper, not empirically implemented.
Value-based procurement can and should be reconceptualized beyond cost containment to include environmental stewardship, social equity, and institutional accountability.
Argument based on literature review across healthcare procurement, ESG governance, and TBL; normative policy analysis rather than empirical testing.
This paper develops an ESG-integrated framework for greening the Medicaid supply chain, anchored in Stakeholder Theory and the Triple Bottom Line.
Conceptual framework development based on theoretical synthesis of Stakeholder Theory and Triple Bottom Line (TBL) and literature in sustainable supply chain management and ESG governance (method: literature-driven framework construction; no empirical validation reported).
The SDK provides interoperability via MCP and A2A.
Implementation and interoperability description in the paper claiming MCP and A2A support; can be verified in code and integration tests.
AESP enforces the invariant that agents are economically capable but never economically sovereign.
Formal design of the protocol and five enumerated mechanisms described in the paper (policy engine, human review, EIP-712 commitments, HKDF isolation, ACE-GF substrate). Enforcement claim derives from architectural guarantees rather than empirical validation in the abstract.
The Agent Economic Sovereignty Protocol (AESP) is a layered protocol that lets agents transact autonomously at machine speed on crypto-native infrastructure while remaining cryptographically bound to human-defined governance boundaries.
Protocol design and specification presented in the paper; implementation claimed (see TypeScript SDK). No runtime throughput/latency measurements reported in the abstract.
Rigorous user evaluation can help develop systems that allow for effective and responsible human agency in veracity-assessment processes.
Interpretation and conclusion drawn from the study's findings showing differences in user behavior across support types and highlighting design implications for tooling that supports human verification.
User responsibility for assessing veracity is particularly critical in sectors that rely on on-demand, AI-generated data extraction, such as biomedical research and the legal sector.
Framing and motivation in the paper (domain argument citing the high-stakes nature of biomedical and legal information extraction). This is a contextual claim rather than a new empirical result from the study.
LLM explanations enable rapid veracity assessments.
Same controlled user study, where assessment speed (time-to-decision) was measured for the LLM-explanation condition and found to be fast relative to other conditions.
Passage retrieval offers a reasonable compromise between accuracy and speed, with judgments of veracity comparable to using the full source text.
Controlled user study comparing three types of supporting information (full source text, passage retrieval, LLM explanations). Outcome measures reported include veracity-judgment accuracy and time-to-assessment. (Sample size and statistical details not specified in the abstract; see paper for exact n and tests.)
The effects of AI, FinTech, financial evolution, governance quality and economic performance on monetary policy improve across higher quantiles.
Reported MMQR findings in the paper indicating that the estimated influence of these variables on monetary-policy-related outcomes (across quantiles) becomes stronger at higher quantiles.
Financial development shows increasing positive effects on growth at higher quantiles.
MMQR estimation in the paper reporting that the financial development variable's positive effect on growth increases at upper quantiles.
Financial technology (FinTech) shows increasing positive effects on growth at higher quantiles.
MMQR coefficient estimates for the FinTech variable across quantiles reported in the paper, showing rising positive effects toward higher τ values.
Tailoring AI explanations to individual users can improve human–AI team performance and provides insights into how personalization may enhance human-AI collaboration.
Synthesis of experimental findings across the two preregistered tasks: observed interactions between user characteristics and explanation types, and demonstration of complementarity in the geography task, form the basis for this general claim. (This is an inferential conclusion drawn from the experiments; full generalizability depends on task scope and replication.)
In the geography-guessing task, user characteristics interact with explanation types, and these interactions contribute to human–AI complementarity (the joint performance exceeds either alone).
Results from the preregistered geography-guessing experiment showing interaction effects between user characteristics and explanation types that lead to observed complementarity. (Exact effect sizes, statistical significance, and sample size not provided in the excerpt.)
We designed a geography-guessing task in which humans and AI possess complementary strengths.
Task design described in the paper intended to generate complementary error patterns between humans and the AI model (methodological claim based on experimental design). (Details on design specifics and validation not provided in the excerpt.)
AI has the potential to deliver predictive benefits for recruitment and retention.
Aggregated findings from empirical studies in the systematic review and supporting meta-analytic/qualitative evidence across the 85 publications that examine recruitment/retention applications.
The meta-analysis shows a small-to-moderate direct positive relationship between AI use and operational productivity (r = 0.28).
Quantitative meta-analysis reported in the paper; pooled effect size r = 0.28; heterogeneity I^2 = 74% (based on the meta-analytic sample drawn from the reviewed studies).
The study links digital technologies to evolving economic models, offering insights into how nations can leverage digital infrastructures to foster competitiveness, resilience, and sustainable growth.
Claim about the paper's contribution and policy-relevant insights; the abstract does not lay out the specific analytical framework, case comparisons, or empirical backing used to generate these policy prescriptions.
Digital transformation enhances efficiency and inclusion.
Reported as a finding in the paper; the abstract does not specify the empirical indicators, measurement approach, or samples used to establish efficiency and inclusion gains.
China’s digital economy framework demonstrates the role of state-led policies, technological innovation, and private sector dynamism in shaping one of the world’s most advanced digital ecosystems.
Paper includes a special focus on China (case analysis implied); the abstract does not provide the specific evidence, datasets, or case-study methodology supporting this claim.
The digital revolution has fundamentally reshaped global economic structures, driving a transition from traditional labor- and capital-intensive systems toward knowledge-, data-, and technology-driven models.
Assertion presented in the paper's analysis; specific empirical methods, data sources, and sample size are not provided in the abstract.
Emerging data suggest AI is already widely adopted for entertainment purposes — especially by young people — and represents a large potential source of revenue.
Reference to unspecified 'emerging data' (likely usage statistics or surveys) cited by the authors; the excerpt does not give the data source, methodology, or sample size.
Generative AI systems are predominantly designed, evaluated, and marketed as intelligent systems which will benefit society by augmenting or automating human cognitive labor, promising to increase personal, corporate, and macroeconomic productivity.
Authors' synthesis of mainstream discourse and industry positioning (marketing, research and product literature) as described in the paper; no specific sample size or empirical study reported in the excerpt.
Generative AI (GenAI) offers transformative potential for productivity and innovation.
Synthesis of themes reported across the 28 reviewed papers (authors' thematic summary of literature highlighting potential productivity and innovation gains).
Short-term productivity gains are documented.
Findings from some of the 81 reviewed sources report short-term productivity improvements associated with Agentic AI or related interventions. The abstract does not quantify the gains or specify domains/settings.
Analytics can serve as the focal interpretive intercession between AI outputs and human decision-makers, facilitating transparency, accountability, and contextual decision-making.
Conceptual proposition drawn from interdisciplinary literature synthesis and the proposed framework. No empirical validation or measured outcomes presented.
The review suggests future research to ensure that GeoAI advances are fair, transparent, and aligned with urban policy goals.
Recommendation and research agenda presented in the paper based on identified gaps and ethical/policy considerations from the literature review (formulative guidance rather than empirical proof).
There are opportunities to use GeoAI to enhance climate resilience, alleviate poverty, foster inclusive urban strategies, and develop better cities.
Prospective and applied examples synthesized in the review that illustrate possible applications of GeoAI for resilience, poverty alleviation, and inclusive planning (these are framed as opportunities; specific pilot studies or effect sizes are not provided in the excerpt).
Recent research highlights improvements in methodology, decision-making support, and impacts on resilience, social inclusion, and fair governance.
Aggregate claim from the review of recent research; supported by cited methodological advances and application studies showing decision-support impacts (the excerpt does not enumerate the studies or quantitative measures).
GeoAI methods support spatial planning, risk assessment, and policymaking in cities facing climate change, socio-economic disparities, and environmental challenges.
Review of applied GeoAI studies and case examples reported in the paper that demonstrate use in spatial planning, risk assessment, and policy support (specific studies and sample sizes not provided in the excerpt).
The workforce should be prepared for GenAI-driven changes through targeted skilling programs (upskilling, reskilling, cross-skilling).
Recommendation based on literature and the authors' analyses/discussions; no trial data or program evaluation metrics are reported in the abstract.
Using suitable approaches to skill development and committing to continuous learning within organizations, GenAI drives innovation, improves decision-making, and creates new growth opportunities.
Conclusion drawn from the paper's literature recherche, task analyses (including Erasmus+ projects), and discussions with trainers/educators. The abstract does not present controlled empirical evidence or quantified effect sizes for these outcomes.
GenAI supports skill-assessment tools that enable continuous, granular evaluations of employees’ abilities.
Supported by literature synthesis, analysis of occupational tasks (Erasmus+ projects), and practitioner discussions; no quantitative validation (e.g., accuracy, reliability, sample sizes) reported in the abstract.
GenAI supports learning and development by performing various tasks that influence the creation and interaction with content.
Claim based on reviewed literature and task analyses presented in the paper; specifics of experiments or deployment (e.g., tools used, participant counts) are not provided in the abstract.
Upskilling, reskilling, cross-skilling, and learning initiatives are necessary mechanisms for organizations to prepare their workforce for GenAI-driven changes.
Derived from literature recherche and analysis of individual tasks across occupations within Erasmus+ projects, plus practitioner discussions; no sample sizes or outcome metrics specified.
Generative AI (GenAI) models are growing rapidly, changing job roles, and revolutionizing entire industries.
Stated by the authors based on a literature recherche (scope and search strategy not specified in abstract). No quantitative sample size or bibliometric details provided.
LLM use increases information overload (additional analyses).
Reported follow-up/additional analyses from the experiment indicating a statistically significant association between LLM use condition and higher scores on information-overload measures.
From a practical perspective, the study highlights the importance of designing decision systems that leverage AI’s analytical strengths while preserving human oversight, responsibility, and strategic sense-making.
Practical recommendations derived from the paper's synthesis of literature and theoretical framework (prescriptive guidance; abstract contains no implementation data or outcome measures).
Advances in algorithmic intelligence have enabled organizations to augment human decision-making through data-driven insights, predictive analytics, and automated reasoning systems.
Claim derived from review of technological and applied research literature synthesized in the conceptual meta-analysis (no specific datasets or sample sizes reported in abstract).
Policy priorities should include enforceable AI governance, life-cycle carbon accounting across hydrogen supply chains, and targeted SME capability policies to realize conditional synergies between digitalization and green transition.
Policy recommendations derived from the review of empirical and institutional literature (authorial proposal based on synthesized evidence; not an empirical test).
Digital tools can accelerate green innovation and emissions reductions when coupled with credible standards, auditability, clean power, and workforce capability building.
Synthesis of peer-reviewed research and authoritative institutional reports (review article); conditional-synergy thesis based on multiple empirical and policy studies cited in the review (no single primary sample size reported).
Evaluating employee performance has become increasingly important in order to align workforce capabilities with evolving technological demands.
Framed as an emphasis/argument in the study's rationale; not accompanied here by reported quantitative measures.
Artificial Intelligence (AI) has emerged as a powerful force shaping the modern economy, particularly within the Information Technology (IT) sector.
Stated as background context in the paper's introduction; supported by literature-style assertion rather than presented empirical results in this excerpt.
U.S. web developers tend to benefit more from ChatGPT’s launch compared to web developers in other regions.
Heterogeneous (subgroup) analysis reported in the paper comparing geographic subgroups (U.S. vs other regions) among web developers; method likely DiD with subgroup interaction. (Exact sample sizes and statistical significance not given in the abstract.)
Following ChatGPT’s launch, some online labor markets experienced productivity effects characterized by increased work volume and earnings, exemplified by the web development OLM.
Empirical analysis using a Difference-in-Differences (DiD) design on OLM data; the abstract identifies web development OLM as an example. (Sample size and exact data window not specified in the abstract.)
These findings highlight research opportunities for machine learning applications in finance and for the development of sentiment-based corporate disclosure analytics.
Interpretation by the authors based on identified gaps in the 42-study review (e.g., underused corporate-report sentiment, limited labeled data, geographic concentration, few deep-learning/end-to-end approaches).
Regression-based and other supervised learning approaches remain dominant.
Aggregated reporting from the 42-study review showing a prevalence of regression and supervised ML methods in the literature sample.