Evidence (13827 claims)
Adoption
8454 claims
Productivity
7544 claims
Governance
6789 claims
Human-AI Collaboration
6327 claims
Org Design
4126 claims
Innovation
4058 claims
Labor Markets
3520 claims
Skills & Training
2924 claims
Inequality
2057 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 195 | 97 | 889 | 1979 |
| Governance & Regulation | 815 | 391 | 188 | 121 | 1539 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 624 | 233 | 123 | 96 | 1084 |
| Research Productivity | 410 | 121 | 56 | 331 | 929 |
| Output Quality | 466 | 177 | 59 | 47 | 749 |
| Decision Quality | 320 | 174 | 75 | 42 | 618 |
| Firm Productivity | 435 | 55 | 88 | 20 | 604 |
| AI Safety & Ethics | 214 | 276 | 65 | 33 | 593 |
| Market Structure | 178 | 166 | 122 | 24 | 495 |
| Task Allocation | 206 | 64 | 70 | 31 | 376 |
| Skill Acquisition | 165 | 57 | 60 | 17 | 299 |
| Innovation Output | 201 | 27 | 41 | 18 | 288 |
| Employment Level | 105 | 51 | 107 | 13 | 278 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 149 | 46 | 26 | 3 | 224 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 61 | 20 | 12 | 182 |
| Error Rate | 69 | 91 | 10 | 2 | 172 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 92 | 19 | 13 | 19 | 145 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Automation Exposure | 51 | 54 | 22 | 12 | 142 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Developer Productivity | 94 | 17 | 14 | 6 | 132 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Skill Obsolescence | 5 | 45 | 6 | 1 | 57 |
| Creative Output | 31 | 16 | 7 | 2 | 57 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
With coordinated reform, AI could boost Cameroon’s long-term productivity by 1.5% to 2.8% annually.
Result reported from the paper's digital infrastructure modeling; no empirical field trial or sampled population reported in the excerpt.
This model draws on international standards from the OECD, UNESCO, and the African Union, alongside the NIST Risk Management Framework.
Paper text states the model's normative/standards sources; descriptive claim about frameworks referenced.
The study proposes a three-layer framework tailored to Cameroon’s specific political economy using comparative policy analysis and digital infrastructure modeling.
Methodological claim in the paper (description of what the study proposes); based on the authors' analytical work rather than reported empirical validation.
Cameroon should not view AI simply as modernization; it must be treated as a sovereign strategy built on institutional economics, deliberate governance, and a solid blended finance architecture.
Normative policy recommendation derived from the paper's comparative analysis and modeling; no empirical trial or longitudinal data reported in the excerpt.
Artificial Intelligence is ... a structural force that determines national competitiveness and economic resilience.
Author assertion supported by literature review and high-level argumentation (comparative policy analysis); no empirical sample or dataset reported in the excerpt.
Linking these measures to administrative data from 2012 to 2023 shows a broad shift from manual and digital toward frontier skills across occupations.
Longitudinal analysis linking OTSS to administrative labor market data covering 2012–2023, showing temporal changes in skill composition toward frontier skills.
We compute OTSS for all occupations in the German labour market.
Paper reports application of the OTSS metric across the set of occupations covering the German labour market.
Using natural language processing, generative AI and supervised machine learning, we develop an AI‐powered skill classification that enriches occupation‐linked skill labels with standardised GenAI‐generated descriptions and structured indicators of technological content, enabling transparent classification by technology intensity.
Paper describes methodological approach combining NLP, generative AI and supervised ML to create the skill classification and enriched labels.
This paper introduces a novel skill‐based measure of occupational technology intensity – the occupational technology skill share (OTSS) – that distinguishes between manual, digital and frontier technologies, including artificial intelligence (AI).
Paper statement of contribution / methodological development (description of new measure OTSS).
A hybrid AI-human sprint planning framework should assign algorithmic tools to estimation and backlog formatting while mandating human deliberation for risk assessment and ambiguity resolution.
Theoretical framework proposed by the authors, motivated by the experimental findings (trade-offs observed between efficiency and risk capture/rework) and qualitative analysis.
Human-only planning excels at adaptability.
Controlled experiment comparing human-only, AI-only, and hybrid models with qualitative indicators of planning robustness and adaptability showing superior adaptability for human-only planning.
AI-only planning minimizes time and cost.
Controlled, three-condition experiment (AI-only, human-only, hybrid) conducted on a live client deliverable at a mid-sized digital agency; quantitative metrics included time and cost measures (reported alongside estimation accuracy, rework rates, and scope change recovery time).
The bounded-autonomy architecture is a practical, deployed approach for making imperfect language models operationally useful in enterprise systems.
Deployment and reported performance in the described multi-tenant enterprise application evaluation (completion rates, safety interceptions, speedups); the paper synthesizes these empirical results to support the practical claim.
The enterprise application remains the source of truth for business logic and authorization, while the orchestration engine operates over an explicit published actions manifest.
Architectural proposal and implementation details described in the paper; asserted as part of the bounded-autonomy design deployed in the enterprise application.
Several safety properties are structurally enforced by code and intercepted all targeted violations regardless of model output.
Design and deployment of bounded-autonomy architecture with typed action contracts, permission-aware capability exposure, scoped context, validation before side effects, and consumer-side execution boundaries; empirical claim that these code-enforced properties intercepted targeted violations during evaluation.
Both AI conditions delivered 13–18x speedup over manual operation.
Timing/performance comparison across the three experimental conditions (manual operation, unconstrained AI, full bounded autonomy) within the deployed evaluation; reported speedup range 13–18x relative to manual operation.
The bounded-autonomy system completed 23 of 25 tasks with zero unsafe executions.
Evaluation in a deployed multi-tenant enterprise application across 25 scenario trials spanning seven failure families; comparison across three conditions (manual, unconstrained AI with safety layers disabled, full bounded autonomy).
CoCoGen+ outperforms baselines in efficiency.
Comparative experiments reported in the paper showing CoCoGen+ versus baseline methods on efficiency metrics; the abstract does not report numeric effect sizes or sample sizes.
Experiments on varying learning tasks validate the feasibility of CoCoGen+.
Simulation/experimental evaluation on multiple learning tasks reported in the paper; abstract does not state dataset sizes, number of tasks, or other experimental details.
To promote long-term collaboration, CoCoGen+ integrates a payoff-redistribution-based incentive mechanism to compensate organizations for their contributions and competition-caused utility degradation.
Mechanism design described in the paper (proposed incentive mechanism); presented theoretically and incorporated into experiments.
We provide a tractable equilibrium characterization of the game and derive implementable synthetic-data generation strategies that maximize social welfare.
Analytical equilibrium characterization and derived strategies reported in the methods/analysis sections of the paper; theoretical derivations rather than randomized trial data.
We introduce CoCoGen+, a coopetition-compatible data generation and incentivization framework that jointly models non-IID data and inter-organizational competition while endogenizing GenAI-based synthetic data generation as a strategic decision.
Design and formal description of the CoCoGen+ framework within the paper (theoretical contribution); no sample size applicable.
We formalize the framework and outline a research agenda, motivated by business and economics, around marketplace simulation, metrics, optimization, and adoption in evaluation campaigns like TREC.
Statement of paper scope and contributions (formalization and research agenda); factual description of the paper's contents rather than an empirical claim.
By simulating repeated interactions and evolving user and agent preferences, the framework enables longitudinal evaluation and marketplace-level metrics, such as retention and market share, that complement and can extend beyond traditional accuracy-based metrics.
Descriptive claim about the capabilities of the proposed simulation-based framework as stated in the paper; described as enabling longitudinal and marketplace-level metrics (no empirical validation or sample size in the abstract).
We introduce Marketplace Evaluation, a simulation-based paradigm that evaluates information access systems as participants in a competitive marketplace.
Author's stated contribution in the paper (introduction of a proposed framework); the paper itself presents the framework (formalization described later), not an external empirical validation.
Modern information access ecosystems consist of mixtures of systems, such as retrieval systems and large language models, and increasingly rely on marketplaces to mediate access to models, tools, and data, making competition between systems inherent to deployment.
Statement in paper abstract/introduction describing current ecosystem architecture and marketplace mediation; conceptual/observational claim (no empirical data or sample size reported).
Successful AI implementation in auditing requires an integrated framework that aligns technological readiness, auditor acceptance, and innovation diffusion to sustainably improve audit quality in Indonesia.
Authors' conclusion and recommendation derived from thematic synthesis of reviewed literature and comparative findings.
Comparative analysis indicates Indonesia remains at the early majority stage of AI adoption in auditing.
Authors' comparative synthesis of the reviewed literature and country-specific discussion classifying Indonesia's adoption stage as early majority.
Comparative analysis indicates global audit firms are positioned at the innovators and early adopters’ stage of AI adoption.
Authors' comparative synthesis of the reviewed literature classifying global audit firms' diffusion stage (innovation adoption framework) based on patterns in the articles.
AI implementation has been shown to significantly enhance audit efficiency, accuracy, and overall audit quality.
Synthesis of findings across the reviewed articles (thematic analysis) reporting positive effects of AI on efficiency, accuracy, and audit quality.
Global auditing practices increasingly utilize machine learning, natural language processing, and robotic process automation to support risk-based auditing, fraud detection, and continuous auditing.
Thematic analysis of the 15 selected journal articles identifying dominant AI techniques (ML, NLP, RPA) and common use cases (risk-based auditing, fraud detection, continuous auditing).
The conclusions remain robust after substituting different methods for measuring total factor productivity (TFP).
Robustness checks in which alternative TFP measurement methods were used in the panel fixed-effects regressions on the same 2015–2024 sample of Chinese A-share listed firms.
The positive effect of data factor utilization on AI patent output is more pronounced in firms with low total factor productivity (TFP), exhibiting a 'contrarian' catch-up characteristic.
Heterogeneity/interaction analysis in the panel fixed-effects regression dividing firms by TFP level (low vs. high) using the same sample of Chinese A-share listed firms (2015–2024).
The level of data factor utilization has a significant positive impact on AI patent output.
Panel fixed-effects regression applied to a sample of Chinese A-share listed companies in core digital economy industries over 2015–2024; AI patent output used as dependent variable.
Overall, GAI provides a principled and scalable approach to integrating AI-generated information.
Summary claim in the abstract based on the combination of the theoretical properties and empirical results reported in the paper.
Across applications, GAI improves confidence interval coverage without inflating width.
Empirical claim reported across the multiple application studies in the paper (abstract states CI coverage improvement while maintaining or not inflating width); details in main text/appendix presumably contain the quantitative analysis.
In health insurance choice, GAI cuts labeling requirements by over 90% while maintaining decision accuracy.
Reported empirical result from the paper's health insurance choice experiment; abstract gives the >90% reduction claim but does not include sample size or exact metrics in the abstract.
In retail pricing, where all methods access the same auxiliary inputs, GAI consistently outperforms alternative estimators, highlighting the value of its construction rather than differences in information.
Empirical experiment in a retail pricing application comparing multiple estimators given identical auxiliary inputs; stated as consistent outperformance in the abstract (no numerical effect sizes or sample sizes provided there).
In conjoint analysis with weak auxiliary signals, GAI reduces estimation error by about 50% and lowers human labeling requirements by over 75%.
Reported empirical result from the paper's conjoint analysis experiment(s); exact sample size and experimental details are not stated in the abstract.
Empirically, GAI outperforms benchmarks across diverse settings.
Empirical experiments reported across multiple application settings (conjoint analysis, retail pricing, health insurance choice) comparing GAI to alternative estimators/benchmarks.
The authors establish asymptotic normality for the GAI estimator and show a 'safe default' property: relative to human-data-only estimators, GAI weakly improves estimation efficiency under arbitrary auxiliary signals and yields strict gains whenever the auxiliary information is predictive.
The paper claims formal theoretical results (asymptotic normality and efficiency comparisons) — supported by analytic derivations/proofs in the manuscript as referenced in the abstract.
GAI uses an orthogonal moment construction that enables consistent estimation and valid inference with flexible, nonparametric relationship between LLM-generated outputs and human labels.
The paper presents a methodological proposal (Generative Augmented Inference) and states theoretical properties (orthogonal moment construction, consistency, valid inference) — supported by formal asymptotic analysis/proofs in the paper (the abstract references establishing asymptotic normality).
This work takes a foundational step toward dignified human-AI interaction futures by balancing productivity with the preservation of human expertise.
Author-stated contribution and goal of the paper (conceptual + empirical work). Abstract claims contribution but does not present quantified validation of 'foundational' status.
AI delivers initial operational/productivity gains in high-stakes work settings.
Claimed empirical observation from the year-long study (abstract: 'Initial operational gains'). No quantitative productivity metrics reported in abstract.
The framework operationalizes 'sociotechnical immunity' via dual-purpose mechanisms that both serve institutional quality goals and build worker power to detect, contain, and recover from skill erosion while preserving human identity.
Descriptive claim about the nộive of the proposed framework as stated in the abstract; no empirical performance metrics provided in abstract.
We offer a framework for dignified Human-AI interaction co-constructed with professional knowledge workers facing AI-induced skill erosion without traditional labor protections.
Paper contribution: proposed framework described as co-constructed with knowledge workers; abstract states aim and intended beneficiaries but does not report empirical validation details in the abstract.
Finance applications of AI strengthen risk assessment and process efficiency.
Abstract statement summarizing literature findings across reviewed finance-related studies.
Logistics applications of AI improve forecasting and supply chain resilience.
Reported thematic finding in the abstract based on synthesis of the included studies.
Marketing relies on predictive analytics and conversational interfaces.
Thematic claim in the abstract summarizing the roles of AI in marketing drawn from the reviewed literature.
Human resources applications of AI focus on recruitment and workforce planning.
Specific thematic finding reported in the abstract from the literature synthesis of included studies.