Evidence (4004 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).
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Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 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 | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Labor Markets
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Important gaps include customer acquisition, personalization at scale, use of external text sources (social media, news, reviews), operational process improvement, and cross‑channel integration.
Gap detection via low‑density regions in the UMAP thematic map of sentence‑transformer embeddings and manual review showing low article counts for these topics within the 109‑article sample.
Existing literature on NLP in marketing is concentrated around customer retention tasks (e.g., churn prediction, complaint handling, relationship management).
Thematic clustering from sentence‑transformer embeddings of article text combined with UMAP visualization, and manual review of article topics and keywords identifying frequent retention‑related themes.
NLP applications in bank marketing are severely under‑studied.
Descriptive result from the PRISMA review showing only 8/109 articles focused on NLP in bank marketing (≈7%), plus thematic mapping showing sparse coverage in bank‑marketing/NLP intersection.
Vietnam's civil-law features—statutory specificity, formal procedures, and constitutional principles like legal certainty and fairness—make straightforward AI deployment legally fraught.
Close textual analysis of Vietnam's statutes, constitutional provisions, and administrative procedures (doctrinal legal analysis); no quantitative sample.
Automated decisions complicate assigning responsibility and hinder judicial and administrative reviewability.
Doctrinal examination of accountability and review mechanisms in administrative law plus comparative institutional analysis of automated decision-making governance.
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.
Provider incentives may be misaligned (e.g., optimizing for engagement or test performance instead of durable learning), requiring contracts, regulation, or purchaser design to align incentives.
Consensus from interdisciplinary workshop (50 scholars) highlighting incentive risks and market-design considerations; descriptive, not empirical.
Extensive learner data needed to personalize AI feedback raises privacy and data-governance concerns (consent, storage, usage).
Qualitative consensus from workshop participants (50 scholars) noting data-collection requirements and governance risks; no empirical governance studies included.
Automated feedback may not capture pedagogical nuances expert teachers use (motivation, socio-emotional cues, complex reasoning), limiting pedagogical fit.
Expert syntheses from the workshop of 50 scholars highlighting limits of automation relative to expert teacher judgment; no empirical comparisons presented.
AI-generated feedback can be incorrect, misleading, or misaligned with learning objectives; assessing feedback quality is nontrivial.
Repeated concern raised across workshop participants (50 scholars) in qualitative synthesis; noted as a substantive risk and open challenge rather than empirically quantified here.
Generalization across domains and long-term robustness to adversarial adaptation require further validation.
Authors explicitly note the need for further validation; the paper's reported experiments do not (in the provided summary) disclose broad domain coverage, longitudinal tests, or adversarial evolution studies.
A modular system may increase engineering complexity and compute overhead compared to a single LLM endpoint.
Authors' caveat in the paper noting higher engineering and compute costs as a trade-off for modularity; the summary does not provide quantitative cost or latency measurements.
Quality of CoMAI depends on rubric design and on how the finite-state machine and agent prompts are specified.
Authors' noted limitation/caveat in the paper that system performance hinges on rubric and prompt/FSM design choices; this is a qualitative dependency rather than an empirically quantified effect in the summary.
Using C.A.P. entails trade-offs: potential increases in latency and compute cost and a risk of over-correction (unnecessary clarification).
Paper explicitly notes these trade-offs as part of the design discussion and proposes measuring latency, compute cost, and unnecessary clarification rate in evaluations; this is an acknowledged design risk rather than an empirically quantified result.
Integration costs—domain modeling, human-in-the-loop protocols, and regulatory/liability frameworks—are significant barriers to deployment.
Conceptual assessment of operational and regulatory requirements; no quantified cost studies provided.
AFs and LLMs may be gamed or misled; adversaries may exploit systems leading to strategic argumentation or manipulation.
Conceptual security/adversarial concern based on known vulnerabilities in ML and strategic behavior; no adversarial tests reported.
Faithful extraction—aligning LLM-extracted arguments with formal AF primitives and ensuring fidelity to source evidence—is a key technical challenge.
Paper's explicit identification of failure modes and alignment issues; grounded in documented limitations of IE/LLMs (no empirical quantification here).
Computational argumentation approaches have required heavy feature engineering and domain-specific knowledge to be effective.
Conceptual claim grounded in prior work and practical experience reported in the literature; no quantitative cost estimates provided in the paper.
Automation bias (human tendency to defer to automated outputs) compounds the risk that GLAI errors become embedded in legal processes.
Behavioral literature review on automation bias and trust in AI systems; applied to legal-context vignettes. No primary empirical test within the paper.
Current models heavily rely on large static datasets and batch training and exhibit poor lifelong/continual learning.
Synthesis of common practices in contemporary ML (supervised pretraining and offline training paradigms); no new experiments provided.
Proactive AI at national scale amplifies concerns around transparency, accountability, privacy, and potential misuse, necessitating robust regulatory and ethical frameworks.
Normative and ethical analysis in the paper, supported by general literature on large-scale AI governance; no empirical assessment of regulatory effectiveness in Russia included.
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.
There are limited randomized controlled trials or longitudinal evaluations; few studies measure patient-relevant outcomes or economic impacts.
Literature synthesis noting scarcity of RCTs and long-term observational studies, and absence of widespread patient-outcome and cost-effectiveness evaluations in existing publications.
Many published studies focus on standalone algorithm accuracy rather than clinician–AI joint performance in routine workflows.
Review of the literature categorizing study designs (preponderance of algorithm development/validation studies, fewer reader-in-the-loop, simulation, or deployment studies).
Advanced technologies' complexity and lack of explainability create risks for audit reliability and professional judgement.
Findings from literature synthesis and professional/regulatory perspectives included in the review; presented as an identified risk/challenge rather than quantified effect.
Audit 5.0 introduces key challenges: data quality and integration issues, complexity and explainability of advanced technologies, regulatory and ethical uncertainty, and skills shortages combined with cultural resistance.
Systematic literature review and synthesis of professional standards and regulatory perspectives; assertions based on reviewed literature rather than a single empirical dataset.
Gaps in infrastructure readiness, digital awareness, and inclusive policy frameworks hinder equitable AI adoption among micro‑enterprises.
Cross‑study synthesis of barriers identified across the 55 included articles; infrastructural, awareness, and policy barriers are explicitly reported as recurring themes.
Only 24.4% of at-risk workers have viable transition pathways, where 'viable' is defined as sharing at least 3 skills and achieving at least 50% skill transfer.
Analysis of job-to-job transitions on the validated knowledge graph using an operational definition of viable pathways (>=3 shared skills and >=50% skill transfer); proportion of at-risk workers meeting that criterion reported as 24.4% (underlying at-risk worker count not given in the excerpt).
20.9% of jobs in the dataset face high automation risk.
Risk classification applied to the jobs represented in the knowledge graph (sample size: 9,978 job postings); proportion of jobs labeled as 'high automation risk' is reported as 20.9%.
Japan's population is shrinking, the share of working-age people is falling, and the number of elderly is growing fast.
Statement grounded in official national statistics referenced by the paper (demographic time series used to initialize and calibrate the system dynamics model).
A preregistered, nationally representative replication experiment in the United States (N = 1,200) replicates the causal finding that a labor-replacing (vs. labor-creating) AI frame reduces willingness to politically engage with future AI developments.
Preregistered randomized experiment (nationally representative US sample, N = 1,200) replicating the UK manipulation and measuring willingness to engage politically regarding AI.
A preregistered, nationally representative experiment in the United Kingdom (N = 1,202) shows that exposure to a labor-replacing (vs. labor-creating) AI frame causally reduces trust in democracy.
Preregistered randomized experiment (nationally representative UK sample, N = 1,202) manipulating AI framing (labor-replacing vs. labor-creating) and measuring trust/satisfaction with democratic institutions.
Large-scale survey data indicate that the public tends to view AI as labor-replacing rather than labor-creating.
Cross-sectional survey (N = 37,079 respondents across 38 European countries); descriptive analysis of responses about AI's labor market impact.
Only 12% of gig workers participate in retirement savings programs.
Survey and administrative measures of retirement-savings participation among gig workers in the 24-country sample.
Only 23% of gig workers report access to employer-provided health insurance.
Self-reported benefits coverage from labor force surveys and linked administrative records for gig workers across the 24 OECD countries (2015–2025).
Human judgment is constrained by bounded rationality, cognitive biases, and information-processing limitations.
Cited as established findings from prior research across decision sciences and related fields (extensive literature evidence referenced; no new empirical data in this paper's abstract).
Ireland exhibits the largest gender gap in advanced digital task use: approximately 44% of men versus 18% of women perform advanced digital tasks — a 26 percentage point gap, close to double the European average.
Country-level descriptive statistics from ESJS for Ireland reporting shares of men and women performing advanced digital tasks. (Exact Irish sample size not provided in the excerpt.)
Across Europe, women are around 15 percentage points less likely than men to perform advanced digital tasks in their jobs.
Empirical analysis of the European Skills and Jobs Survey (ESJS) (Cedefop, 2021) using regression-based estimates and descriptive statistics across European countries. (Exact sample size and country count not provided in the excerpt.)
AI substitutes many routine tasks, including both manual and cognitive/rule-based activities, disproportionately affecting middle-skill occupations.
Task-based substitution reasoning within SBTC framework and cross-sectoral task analysis. The paper provides conceptual synthesis rather than presenting new microdata or quantified task-level estimates.
Key implementation challenges include data quality and integration, model interpretability, cybersecurity and privacy, regulatory/compliance uncertainty, skills gaps among accounting professionals, and implementation costs.
Identified by the paper through literature review and practitioner reports; these are presented as recurring barriers rather than quantified with a specific sample.
Two regimes emerge: an inequality-decreasing regime when AI behaves like a broadly available commodity technology or when labor-market institutions share rents widely (high ξ).
Model regime characterization and calibrated counterfactuals showing falling wage dispersion and ΔGini under commodity-like AI assumptions or higher rent-sharing elasticity.
Generative AI compresses within-task skill differences (reduces dispersion of individual task performance).
Theoretical task-based model and calibrated quantitative simulations (Method of Simulated Moments matching six empirical moments) showing reductions in within-task performance dispersion after introducing AI technology.
Because the design is cross-sectional and sampling purposive/geographically constrained, causal inference and generalizability are limited.
Authors' stated limitations in the summary: cross-sectional design and purposive, geographically constrained sample (Karnataka, India).
Workplace stress is associated with lower employee retention.
PLS-SEM analysis on a cross-sectional survey of N = 350 pharmaceutical workers in Karnataka, India (purposive sampling). Reported direct path: Stress → Retention, β = 0.321, p < 0.001. (Note: the paper interprets this as stress reducing retention; sign/coding conventions of the variables are not detailed in the summary.)
If deployed without mitigation, GenAI CDS risks widening disparities by performing worse on underrepresented groups or being unequally distributed across resource-rich versus resource-poor settings.
Fairness literature, subgroup performance concerns, and distributional risk analysis cited in the paper; direct empirical demonstrations of widened disparities due to GenAI CDS are limited in the literature per the paper.
Limited public datasets and vendor lock-in constrain independent reproducible evaluations and audits of current generative models in healthcare.
Observation and policy analysis in the paper noting scarcity of public clinical datasets for state-of-the-art models and proprietary constraints; no dataset counts provided.
GenAI CDS creates data privacy and security risks because of high-value medical data and use of external cloud services.
Known cybersecurity risks and documented incidents in health IT; the paper cites the general risk context rather than specific breach sample counts tied to GenAI deployments.
GenAI CDS can amplify bias and inequities if training data underrepresent groups or reflect historical disparities.
Fairness and robustness audit literature and subgroup performance analyses referenced in the paper; specific empirical demonstrations for contemporary GenAI CDS are limited and sample sizes not given.
GenAI CDS systems hallucinate and can produce incorrect but plausible recommendations, which can cause patient harm if trusted unchecked.
Documented failure modes of generative models and examples from controlled evaluations; the paper references known hallucination behavior from model audits and case reports, though it does not quantify incidence rates or provide large-scale observational harm data.