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
Filter claims →
Productivity
8066 claims
Filter claims →
Governance
7278 claims
Filter claims →
Human-AI Collaboration
6912 claims
Filter claims →
Org Design
4439 claims
Filter claims →
Innovation
4359 claims
Filter claims →
Labor Markets
3652 claims
Filter claims →
Skills & Training
3018 claims
Filter claims →
Inequality
2160 claims
Filter claims →
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 |
Platforms form partnerships with media outlets, academic institutions, and civil-society actors to amplify reach and secure data.
Interview accounts and organizational documents describing cross-sector partnerships and collaboration arrangements.
Transparent workflows and clear labeling are used to build credibility with audiences.
Document analysis of platform outputs and guidelines showing explicit workflow transparency and labeling practices, supported by interview statements.
Platforms emphasize local-language expertise and culturally grounded sourcing as a strategy to improve verification and credibility.
Observed practices and platform guidelines derived from document analysis and staff interviews describing the use of local-language expertise and sourcing.
Practical policy recommendation: require transparent documentation and third‑party auditing for high‑impact LLM deployments and subsidize public‑interest evaluation infrastructure.
Policy prescription supported by the paper's normative and economic analysis; no pilot implementation or empirical evaluation of the recommendation is provided.
Policy levers that can address alignment externalities include disclosure requirements (data provenance, evaluation practices), mandatory participatory evaluation for high‑impact systems, standards for auditing, procurement rules favoring participatory transparency, and liability/certification regimes.
Policy recommendation based on economic and governance reasoning and synthesis of prior regulatory proposals; no policy pilot data or impact evaluation is reported.
Economics research should develop multi‑dimensional metrics capturing welfare, distributional impacts, and autonomy rather than relying on single aggregate accuracy or safety scores.
Prescriptive recommendation grounded in critique of current benchmarking practices and theoretical desiderata; no new metric is empirically validated in the paper.
Dynamic constraints (continuous monitoring, feedback loops, and configurable safety settings that adapt post‑deployment) are preferable to static pre‑deployment-only safety fixes.
Conceptual argument and synthesis of deployment experience and monitoring literature; suggestions for operational tooling and monitoring rather than empirical evaluation.
Participatory governance—includes varied stakeholders such as users, affected communities, domain experts, and regulators in design, evaluation, and deployment decisions—will improve alignment outcomes and legitimacy.
Theoretical and normative argument citing participatory design literature and ethical governance scholarship; paper offers procedural recommendations but no empirical trial of governance models.
Alignment should shift from static, post‑training constraints (one‑off fixes like safety filters or RLHF alone) to dynamic, participatory systems that explicitly protect pluralism, autonomy, and justice.
Normative argument and conceptual synthesis drawing on literature in AI safety, value alignment, and participatory design; prescriptive reasoning rather than original empirical results.
Investment choices in collaboration AI and digital infrastructure become central strategic decisions affecting firms' comparative advantage.
Management literature synthesis and illustrative multinational cases; argument is conceptual without firm‑level comparative empirical data presented in the paper.
AI collaboration tools (virtual assistants, meeting summarizers, asynchronous platforms) complement hybrid work by reducing coordination costs and supporting dispersed teamwork.
Conceptual integration of technology and organizational literature; supported by illustrative case examples of multinational organizations but not by new quantitative causal evidence.
Hybrid and remote work increase employee autonomy and work–life integration.
Conceptual synthesis of sociological and management literatures; supported by secondary data and illustrative case studies from multinational organizations. No primary quantitative analysis or sample size reported—based on comparative case illustrations and theoretical integration.
Tariff reductions and expanded supply channels following CAFTA contributed as secondary channels to increased third‑country agricultural imports.
Paper documents tariff changes and supply‑channel expansion as part of mechanism analysis; DID and mediator tests link tariff reductions and expanded channels to import outcomes.
CAFTA improved logistics and service frictions (e.g., storage, logistics performance) relevant to agricultural imports.
Secondary channel analysis using logistics/storage indicators and related service frictions available in the data; assessed as mediators in the DID framework.
CAFTA widened China's trading‑partner and product diversity in agricultural imports, increasing both partner and product variety from third countries.
DID estimates on partner and product diversity metrics constructed from customs import records (2000–2014); reported changes in diversity as outcomes in the paper.
A complementary‑products linkage effect is a key mechanism: expanded channels and product complementarities make sourcing non‑ASEAN goods easier and more attractive.
Mechanism analysis using product‑level and partner‑level import data (China Customs) showing increased imports of complementary products and linkages consistent with this channel in DID estimates.
The primary spillover mechanism is a 'low‑cost import experience' effect: cheaper/consistent regional sourcing lowers firms' marginal cost of engaging additional foreign suppliers, encouraging imports from third countries.
Mechanism tests using mediator variables (cost/procurement indicators) within the DID framework and firm‑level data; reported as the main channel in the paper's analysis.
A new market will emerge for controls, certification, attestations, secure toolchains, and audited model deployments; compliance costs will shape comparative advantages among firms and countries.
Policy-market synthesis and analogies to certification markets in other regulated tech domains (qualitative).
The emergence of HACCAs will create a demand shock for defensive cyber tools and services (AI-based detection, incident response, resilience engineering), accelerating R&D and capital allocation into defensive AI.
Market-impact scenario analysis and industry inference about defensive responses to heightened threats (qualitative forecasting).
Main drivers of attrition identified by the model are overtime, business-travel frequency, and promotion opportunities (each having higher influence than salary).
Feature importance analyses using permutation importance and aggregated SHAP values on the fitted logistic-regression model trained on the IBM HR Analytics dataset.
Non-monetary workplace factors (excessive overtime, frequent business travel, limited promotion opportunities) are stronger predictors of individual attrition risk than salary.
Interpretable logistic-regression model trained on the IBM HR Analytics dataset; global importance assessed using aggregated SHAP values and permutation importance to rank predictors. (Exact sample size and numeric importance ranks not provided in the summary.)
Generative AI functions as a socio‑technical intermediary that facilitates interpretation, coordination, and decision support rather than merely automating discrete tasks.
Thematic analysis and co‑word linkage between terms related to interpretative work, coordination, and decision‑support and technical GenAI terms within the corpus.
The literature indicates a managerial shift away from hierarchical command‑and‑control toward guide‑and‑collaborate paradigms, where managers curate, guide, and coordinate AI‑augmented teams rather than micro‑manage tasks.
Synthesis of themes from the 212‑paper corpus (co‑word and thematic analyses) showing recurrent managerial/behavioural concepts such as autonomy, coordination, and decision‑support tied to GenAI discussions.
Higher educational attainment is positively associated with greater willingness to keep working before retirement.
Multivariate regression analysis of the cross-sectional survey (n=889) using education level as a key explanatory variable.
Male gender is positively associated with higher willingness to remain employed before retirement.
Multivariate regression on the survey sample (n=889) including gender as an explanatory variable, controlling for demographic and socioeconomic covariates.
Trust is a principal demand driver for AI-enabled marketing among Generation Z — higher trust substantially raises adoption intention and thereby accelerates diffusion.
Interpretation/implication drawn from the large standardized path coefficients (Trust → Adoption Intention β = 0.718) and mediation results in the SEM on n = 450 Gen Z respondents.
Adoption intention partially mediates the relationship between trust and brand loyalty (indirect effect Trust → Adoption → Loyalty: standardized β ≈ 0.390, p = 0.001).
Cross-sectional survey (n = 450); mediation tested within SEM framework; reported indirect standardized effect ≈ 0.390 with p = 0.001.
Economic models of firm behavior and market microstructure should incorporate endogenous, adaptive segmentation processes and faster feedback loops enabled by human–AI systems; ABS and large‑scale interaction data can be used to calibrate such models.
Methodological recommendation grounded in the study's mixed‑methods findings (ABS experiments and 150M interaction dataset) and observed differences between autopoietic and traditional STP regimes.
Canvas Design Principles mitigate algorithmic myopia (overfitting to historical patterns) and improve adaptability and resource efficiency.
Set of design principles proposed in the paper and evaluated through agent‑based simulation scenarios and analyses of the large behavioral dataset. Specific experimental details and quantitative effect sizes for these principles are not detailed in the summary.
Reconceptualizing STP as an autopoietic (self‑organizing) system enables continuous human–AI co‑creation and yields better outcomes in unstable markets than traditional, process‑based STP.
Conceptual argument grounded in 6‑month lab ethnography (n = 23), design and deployment of the Algorithmic Canvas in that lab context, and validation via large behavioral dataset analyses and agent‑based simulations.
Algorithmic co‑creation methods detect substantial market fluctuations about 5.8× better than traditional approaches.
Computational analysis of large behavioral dataset (150 million customer interactions) and comparative performance evaluation in empirically grounded agent‑based simulations. The detection metric and statistical significance details are not provided in the summary.
The autopoietic model shortens strategic planning cycle length by approximately 90%.
Observed/recorded time‑to‑update or strategy revision metrics gathered via Algorithmic Canvas usage and lab ethnography (6‑month lab ethnography inside a Fortune 500 company, n = 23). Exact measurement protocol and whether reduction measured in live firms, simulations, or system logs is not fully detailed in the summary.
Design and policy interventions that encourage active human contributions (e.g., draft-first workflows, co-creation interfaces, training) can help preserve worker agency and mitigate psychological costs.
Recommendation based on experimental evidence that Active-collaboration preserved psychological outcomes relative to passive use; presented as policy/design prescription rather than directly tested intervention at scale.
A complementary real-world survey (N = 270) across diverse tasks reproduced the experimental pattern, suggesting external validity beyond the lab writing tasks.
Cross-sectional survey of N = 270 respondents reporting on their AI use across multiple task types; reported patterns consistent with the experiment (passive use associated with lower efficacy/ownership/meaningfulness; active collaborative use did not).
Standardized data schemas and interoperable protocols reduce transaction costs and increase returns on AI investments; public-good components (shared taxonomies, open benchmarks) will accelerate innovation in DPP ecosystems.
Policy/economic recommendation synthesized from empirical observations about interoperability needs (survey and qualitative inputs) and economic reasoning; not directly measured as an outcome in the study.
Different consumer segments imply different AI-driven engagement strategies: targeted personalization and recommender systems for 'aware' consumers, and default, nudging, and tangible-benefit signals for 'unaware' consumers.
Derived from k‑means segmentation results and implication discussion linking consumer cluster characteristics to appropriate AI/UX interventions; segmentation is empirical, the AI-prescription is inferential.
DPPs generate high-quality, structured product and lifecycle data that are non-rivalrous and highly reusable, raising firm-level incentives to invest in AI models (forecasting, optimization, provenance verification) that exploit this data to capture value across production, secondary markets, and services.
Economic/technical implication drawn from the empirical characterization of DPP data and stakeholder interviews; this is an inferential claim linking DPP data properties to incentives for AI investment rather than a directly measured outcome in the surveys.
Practical DPP deployment must combine standards, governance, and user-centric design to unlock circular-economy benefits.
Inference from empirical mapping of barriers/drivers (survey and qualitative stakeholder input) and multivariate analyses showing interplay of technical and organizational factors; sample sizes not reported.
DPPs should be seen as both technical data platforms and participatory tools that enable collaborative value creation and responsible consumption (thus supporting SDG 12: responsible consumption and production).
Conceptual interpretation synthesized from empirical findings (surveys + multivariate analyses) and theoretical framing in the paper; empirical grounding via stakeholder responses but largely a conceptual contribution.
Successful DPP adoption requires matching technical functionalities (data granularity, interoperability, user interfaces) with firm-level readiness and strategies to engage different consumer segments.
Logistic regression and PCA mapping relationships among DPP features, organizational practices and consumer profiles arising from the two online surveys and mixed-method analysis; sample sizes not reported.
DPPs facilitate knowledge sharing and open innovation across firms, embedding sustainability and knowledge management into operational practice.
Qualitative and survey responses from industry stakeholders in the two sectors; analyses reported include mapping of cross‑firm knowledge exchange and organizational practices (methods: mixed methods, logistic regression/PCA); sample sizes not reported.
DPPs enhance transparency and traceability across complex supply chains, enabling material circularity and more resilient sourcing decisions.
Survey-based evidence and multivariate analyses (PCA, logistic regression) from stakeholders in Italian fashion and cosmetics indicating perceived/observed links between DPP functionalities (data granularity, interoperability) and traceability/circularity outcomes; sample sizes not reported.
Digital Product Passports (DPPs) function as a socio-technical, cognitive infrastructure that, when DPP technical capabilities are aligned with organizational readiness and consumer engagement, materially support circularity (raw-material reuse), supply-chain resilience, and cross-firm knowledge exchange, thereby turning sustainability from a compliance burden into a source of innovation and value in fashion and cosmetics.
Mixed-methods empirical study in Italian fashion and cosmetics using two online surveys, PCA and logistic regression to map relationships among technical features, organizational practices and consumer profiles; sample sizes not reported in the summary.
Economists and AI practitioners will need capacity-building in Earth-system knowledge to ensure models capture cumulative and systemic environmental risks rather than only firm-level signals.
Recommendation based on gap analysis between current disciplinary skills and systemic-environmental modeling needs; no survey or training-efficacy data offered.
There is a need for standards for data provenance, auditability, and adversarial robustness to prevent greenwashing and model manipulation.
Policy recommendation grounded in conceptual risk analysis; no technical standard proposals or threat-model evaluations provided.
Open environmental disclosure data supports reproducible empirical research in AI economics (causal inference, counterfactuals, macro-financial modeling) on effects of regulation and capital flows on environmental outcomes.
Logical argument about data availability enabling reproducible research; no empirical examples or reproducibility metrics provided.
More reliable environmental disclosures enable algorithmic investors and market models to price externalities more accurately and to implement sustainability-aligned strategies at scale.
Conceptual argument about improved information enabling market mechanisms; no empirical market-impact study included.
Open data facilitates automated, lower-cost reporting tools (NLP extraction, sensor/IoT integration, ETL pipelines) that reduce administrative burden and increase reporting frequency and timeliness.
Conceptual claim linking open standardized data to automation potential; no implemented cases or cost estimates provided.
Improved, standardized environmental disclosures improve training data quality for predictive models, reducing measurement error and bias.
Theoretical claim about data quality effects on model performance; no empirical evaluation provided.
Better, standardized, open environmental data unlocks AI/ML opportunities, enabling scalable models for firm- and system-level environmental risk assessment, scenario analysis, stress testing, and portfolio optimization.
Conceptual implications and use-case enumeration; no empirical model-building or benchmarking presented.