Evidence (186 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
10085 claims
Filter claims →
Productivity
8974 claims
Filter claims →
Governance
8062 claims
Filter claims →
Human-AI Collaboration
7749 claims
Filter claims →
Org Design
5057 claims
Filter claims →
Innovation
4896 claims
Filter claims →
Labor Markets
4088 claims
Filter claims →
Skills & Training
3372 claims
Filter claims →
Inequality
2377 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 | 882 | 244 | 117 | 1097 | 2424 |
| Governance & Regulation | 1010 | 469 | 229 | 135 | 1875 |
| Organizational Efficiency | 977 | 235 | 149 | 90 | 1462 |
| Technology Adoption Rate | 781 | 299 | 143 | 128 | 1362 |
| Research Productivity | 506 | 155 | 74 | 363 | 1110 |
| Output Quality | 555 | 219 | 71 | 70 | 915 |
| Decision Quality | 395 | 200 | 95 | 54 | 751 |
| Firm Productivity | 523 | 67 | 101 | 27 | 724 |
| AI Safety & Ethics | 262 | 309 | 75 | 36 | 688 |
| Market Structure | 195 | 201 | 135 | 30 | 566 |
| Task Allocation | 248 | 77 | 96 | 38 | 464 |
| Innovation Output | 300 | 34 | 55 | 20 | 411 |
| Skill Acquisition | 207 | 75 | 65 | 21 | 368 |
| Employment Level | 138 | 67 | 119 | 24 | 350 |
| Fiscal & Macroeconomic | 156 | 80 | 53 | 33 | 329 |
| Task Completion Time | 211 | 38 | 13 | 16 | 280 |
| Firm Revenue | 183 | 52 | 29 | 5 | 270 |
| Consumer Welfare | 131 | 77 | 48 | 13 | 269 |
| Inequality Measures | 50 | 141 | 54 | 9 | 254 |
| Worker Satisfaction | 104 | 85 | 25 | 13 | 227 |
| Error Rate | 87 | 112 | 11 | 5 | 215 |
| Automation Exposure | 69 | 69 | 37 | 20 | 198 |
| Wages & Compensation | 102 | 49 | 31 | 11 | 193 |
| Team Performance | 115 | 30 | 30 | 11 | 187 |
| Regulatory Compliance | 88 | 74 | 17 | 7 | 186 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 116 | 21 | 15 | 8 | 161 |
| Job Displacement | 12 | 92 | 26 | 1 | 131 |
| Hiring & Recruitment | 57 | 12 | 9 | 5 | 83 |
| Skill Obsolescence | 6 | 59 | 10 | 2 | 77 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 23 | 17 | 1 | 59 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
The U-shaped pattern is concentrated in software-based AI applications rather than supporting hardware.
Heterogeneity/subgroup analyses in paper that separate software-based AI applications from supporting hardware and find the non-linear pattern concentrated in software applications.
Spline regressions, the Lind–Mehlum U-test, an instrumental-variable analysis using leave-one-out peer AI investment, and entropy balancing all support the non-linear (U-shaped) pattern.
Robustness and identification methods reported in paper: spline regressions, Lind–Mehlum U-test for U-shape, IV using leave-one-out peer AI investment, and entropy balancing.
There is a U-shaped association between AI investment and internal control deficiency (ICD) risk.
Main empirical finding reported in paper based on analyses of 41,725 firm-year observations; supported by spline regressions and Lind–Mehlum U-test.
Two minimal extension policies, each derived from the observation, close the regime along orthogonal axes: a sample-size-aware static rule (Periodic-with-floor) closes the granularity-failure case, while a history-conditioned suspicion-escalation policy closes the coverage-failure case for the naive Drift strategy — and neither closes both, exactly as the observation predicts.
Design and analysis of two auditor policies in the paper; theoretical argument from Observation 1 and supporting simulation results illustrating which failure modes each policy addresses.
The effectiveness of automated tax systems is mediated by contingencies including digital literacy, institutional trust, and regulatory clarity.
The review identifies recurring contextual factors across the 36 articles that are reported to moderate or mediate the impact of automation on outcomes (qualitative and quantitative findings cited in the synthesis).
Safeguards such as audit trails, explainability, and human oversight impose additional implementation costs that must be weighed against efficiency benefits.
Normative and economic reasoning based on requirements for compliance and system design; no empirical cost estimates provided.
Alignment with evolving regulatory expectations (evidence standards, auditing, liability) is necessary to translate AI capabilities into products and reduce adoption risk.
Policy-focused argument referencing regulatory uncertainty; no empirical measures of regulatory impact included.
Key tradeoffs in contemporary financing models include speed/flexibility versus regulatory coverage and long‑term cost, and data reliance versus privacy/fairness.
Multi‑criteria comparative evaluation and conceptual analysis across financing models; synthesis draws on regulatory context and observed product features rather than primary quantitative tradeoff estimation.
Inadequately enforced data protection standards in the EU reinforce Big Tech’s dominance.
Critical review in the paper of enforcement shortcomings in EU data protection (e.g., GDPR enforcement gaps); supported by legal and policy argumentation rather than new empirical enforcement statistics.
The increase in ICD risk at higher levels of AI investment is weaker among firms with above-normal external audit attention.
Moderator (heterogeneity) tests reported in paper showing the ICD-risk increase with AI investment is attenuated for firms receiving above-normal external audit attention.
The increase in ICD risk at higher levels of AI investment is weaker among firms with CIO presence.
Moderator (heterogeneity) tests reported in paper showing attenuated ICD-risk increase for firms that have a Chief Information Officer (CIO).
The increase in ICD risk at higher levels of AI investment is weaker among IT-industry firms.
Moderator (heterogeneity) tests reported in paper showing smaller upward slope of ICD risk with AI investment for firms in the IT industry.
At lower levels of AI exposure, AI investment is associated with lower ICD risk.
Substantive component of the reported U-shaped relationship estimated in regressions on the 41,725 firm-year sample.
Regulatory compliance demands have surpassed the capacity of manual corporate reporting.
Assertion in paper (conceptual observation about reporting capacity); no empirical measurement or sample size reported.
Algorithmic scenario planning is being used for tax avoidance.
Presented in the abstract as an example of algorithmic technologies applied to international tax purposes (scenario planning for tax avoidance); no empirical details provided in the abstract.
A budget-neutral anti-gaming design reduces conduct boundary mass by 0.032 relative to computable static rules.
ABM/RL simulation comparison reported in the paper (design variants evaluated across scenario/sweep runs and the firm-period panel).
Exploitative working conditions violate workers' rights.
Legal assessment based on documents and the authors' interpretation of rights under applicable law (GDPR and labour rights frameworks). (Specific legal rulings or counts not provided in the excerpt.)
Many responses misinterpreted regulatory requirements or relied on shallow justification.
Qualitative coding/analysis of LLM responses against expert rubric showing frequent misinterpretation of regulations and superficial reasoning.
Uncertainty around compliance and excessive risk avoidance reduce the space for lawful business activity.
Interpretive synthesis of evidence and arguments across the reviewed literatures (sanctions compliance, institutional voids); no original empirical test.
A scoping review found that only 9.0% of FDA-approved AI/ML device documents contained a prospective post-market surveillance study.
Paper references a scoping review that examined FDA-approved AI/ML device documents and reported the 9.0% figure.
We identify a structural feature of any noise-aware static-auditor design: a cover regime in which coverage gaps and granularity gaps cannot be closed simultaneously (formalized as Observation 1).
Theoretical observation/proposition in the paper (Observation 1) derived from the formal model of continuous auditing under noise-aware static auditing rules.
AI creates novel non-tariff frictions, e.g., pressures toward data localization and regulatory requirements for algorithmic transparency.
Comparative legal and policy analysis of emerging regulations (e.g., data localization laws, algorithmic regulation initiatives) and illustrative jurisdictional examples.
Opaque AI models risk violating notice, reason-giving, and appeal rights protected under administrative due process.
Analysis of procedural due-process requirements (notice, reason-giving, appeal) in Vietnam's legal framework and assessment of opacity issues in algorithmic systems; qualitative reasoning, no empirical testing.
Static ACLs evaluate deterministic rules that ignore partial execution paths and therefore can only capture a subset of organizational constraints.
Formal argument and examples showing static ACLs map to Policy functions that do not depend on partial_path; illustrative limitations presented.
Prompt-level instructions and static access control lists (ACLs) are limited special cases of a more general runtime policy-evaluation framework and cannot, in general, enforce path-dependent rules.
Formalization showing prompt/system messages and static ACLs map to restricted forms of the Policy(agent_id, partial_path, proposed_action, org_state) function; logical proof/argument in the paper and illustrative counterexamples.
Aggregating informal and recommendation data raises privacy and consent issues in low-regulation contexts, requiring governance safeguards.
Policy and ethical consideration based on the nature of the data used; no specific privacy-impact assessment reported in the summary.
NLP/ML systems can inherit biases from inputs (underrepresentation, noisy self-reports, biased recommendations) and may therefore disadvantage some youth unless transparency and fairness constraints are implemented.
Reasoned risk assessment grounded in known properties of ML/NLP; the pilot summary does not report an audit or measured bias outcomes.
Regulators and payers remain central bottlenecks—AI can accelerate discovery but cannot bypass clinical evidence requirements.
Policy discussion and regulatory analysis in the paper noting that approvals require clinical evidence independent of discovery modality.
AI remains an augmenting technology rather than a standalone solution: no AI-only originated drug has yet achieved regulatory approval.
Review of drug-approval records and company disclosures summarized in the paper; explicit statement that to date no entirely AI-originated molecule has received full regulatory approval.
Limited transparency and interpretability of many AI algorithms (black-box models) complicate clinical and regulatory trust and adoption.
Regulatory reports, methodological critiques, and case examples in the review highlighting interpretability concerns and their impact on clinical/regulatory acceptance.
Data protection and privacy (especially sensitive health data) complicate open-data DAO models.
Conceptual analysis referencing privacy/data-protection concerns for health data (e.g., GDPR-like regimes); no empirical evaluation of privacy breaches within DAOs provided.
Cybersecurity and data-privacy concerns arise from cloud provider centralization versus blockchain transparency.
Paper highlights this trade-off in its challenges section; discussion-based evidence rather than quantified security assessment in the summary.
Regulatory fragmentation and lack of harmonized standards increase compliance complexity for healthcare AI deployments.
Policy analyses, regulatory reviews, and industry reports synthesized in the paper describing divergent national/regional regulatory approaches and their operational consequences.
Generative AI use introduces significant organizational risks including data privacy breaches and leakage when models or third‑party services are used.
Conceptual analysis and references to documented incidents and industry reports within the review; no single aggregated incident dataset provided.
Aligning multiple standards is complex, posing a disadvantage and implementation risk.
Stated explicitly in Disadvantages/Risks: complexity of aligning multiple standards is listed. This is a reasoned observation in the paper rather than empirically demonstrated.
Data privacy and cross-border compliance issues arise from using cloud and SECaaS, complicating legal compliance for firms.
Regulatory analyses and compliance reports; documented examples in case studies and industry guidance on cross-border data flows.
Regulatory and biosafety concerns (including environmental release risks and dual‑use issues) increase fixed costs and create entry barriers that shape industry structure and diffusion.
Policy and governance literature reviewed alongside technical case studies; citations of regulatory requirements, biosafety frameworks, and examples of compliance costs affecting project viability.
Internal control reliability is an important condition for understanding AI-related organizational outcomes.
Author conclusion/interpretation in paper, motivated by empirical findings linking AI investment and ICD risk and by heterogeneity results.
The study uses 41,725 firm-year observations from Chinese A-share listed firms.
Descriptive statement of sample in paper; sample drawn from Chinese A-share listed firms and reported as 41,725 firm-year observations.
In neither unit did internal control mechanisms identify any information-security incident, sensitive-data leakage, or formal compliance challenge from external oversight bodies during the period examined.
Author reports absence of recorded incidents in internal control mechanisms and no external oversight challenges for both units over the study period; based on internal records and SEI-GDF auditable indicators.
Ordinary adaptive updates do not reliably reduce boundary search.
ABM/RL simulation experiments reported in the paper (multiple runs and the firm-period panel); qualitative comparative statement from simulation outputs.
Using Federated Learning (FL) with orbital planes as distributed learners and HAPS for aggregation avoids centralization of raw channel data.
Method description: federated-learning architecture with clients mapped to orbital planes and HAPS performing coordination/aggregation; explicitly states no central pooling of raw channel samples.
Achieving CIA in the cloud requires technical controls (encryption, access controls, IAM, MFA, zero-trust), resilience measures (backups, redundancy, DR/BCP), and continuous monitoring (logging, SIEM, EDR/XDR).
Synthesis of technical best practices and vendor/industry guidance; supported by technical evaluations and case studies in the literature.
The results show a 30% improvement in compliance accuracy after the adoption of AI.
Observational study combining AI modelling and questionnaires comparing AI-based strategies to traditional ones; quantitative improvement in compliance accuracy reported but no sample size or further methodological details provided in the supplied text.
AI enhances transparency in financial systems.
Reported in Findings; based on mixed-methods analysis including policy reports and comparative country data (no quantified effect provided).
Graduated oversight preserves compliance evidence coverage for regulated functions.
Regulatory coverage analysis and framework specifications in the paper (qualitative/analytic demonstration rather than empirical validation).
Each oversight tier defines required evidence artifacts for compliance auditability.
Framework design specifying evidence/artifact requirements per tier (methodological specification in the paper).
Digital technology adoption promotes certification acquisition (mechanism test: coefficient = 0.286, p < 0.001).
Mechanism testing reported in Results using the same panel and estimation strategy; reported regression coefficient and p-value linking digital adoption to certification acquisition.
Digital technology adoption increases certification acquisition by 55.0%.
Same panel (8,547 firms, 42 countries, 2015–2023) using staggered DiD estimator; reported point estimate in Results.
The deployment maintained statutory compliance and role-based accountability.
Paper asserts that DOMUS operations preserved statutory compliance and role-based accountability during the pilot (claimed based on system design and pilot evaluation; no specific compliance audits or metrics provided in the provided text).