Evidence (4892 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 |
Org Design
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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.
Downstream clinical development costs and translational failure rates remain the major drivers of total R&D expenditure; early-stage AI savings may not translate into proportionate increases in approved drugs.
Economic analysis and discussion in the paper referencing known cost distributions in drug development and historical attrition rates in clinical phases.
Inherent biological complexity and translational gaps between in silico predictions, preclinical models, and human biology constrain downstream success rates.
Review of translational failures and literature cited in the paper demonstrating mismatch between preclinical signals and clinical outcomes; conceptual analysis of biological complexity.
Gaps exist between computational designs and chemical/experimental feasibility (e.g., synthetic accessibility and assay readiness), limiting the usefulness of some generative outputs.
Case studies and critiques in the paper showing generated molecules that are synthetically infeasible or incompatible with experimental constraints; discussion of missing integration of practical constraints in many generative models.
Many models have limited interpretability and insufficient uncertainty quantification, hampering trust and decision-making.
Methodological analysis in the paper noting common deep-learning approaches lacking clear interpretability and uncertainty estimates; references to literature on model explainability and calibration gaps.
Poor data quality, fragmentation, and limited accessibility reduce model reliability and generalizability.
Survey of data characteristics and limitations presented in the paper; examples of biased or sparse datasets and the paper's discussion of impacts on model performance and transferability.
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.
Predictions from AI depend on data quality and coverage and still require experimental (wet-lab) validation.
Discussion of early failures and limits in case studies and expert observations within the narrative review; methodological argument about dependence of ML models on input data.
When incentive signals depend non-trivially on persistent environmental memory, the resulting dynamics generically cannot be reduced to a static global objective defined solely over the agent state space (i.e., no global potential function over agents exists in the generic case).
A genericity theorem/argument in the paper (mathematical demonstration showing that for nontrivial dependence on environmental memory the closed-loop vector field is, for a generic set of parameterizations, not gradient of any scalar function on agent space).
AI notably reduces customer stability in sports enterprises (SE).
Empirical estimation using the DML model on the same panel dataset of 45 Chinese listed SEs (2012–2023); authors report a statistically significant negative effect of AI on customer stability.
The environmental footprint of healthcare systems is growing and persistent inequities in access and outcomes have intensified calls for procurement reform.
Contemporary literature review and synthesis of sector reports and studies documenting healthcare emissions/footprint and health inequities (no original empirical data reported in this paper).
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.)
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.
Regulatory uncertainty around blockchain/DeFi for corporate finance and cross-border data rules is a material risk to adoption.
Paper notes regulatory uncertainty as a risk; no jurisdictional legal analysis or compliance case studies provided in the summary.
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.
Integration complexity with legacy ERPs and heterogeneous vendor ecosystems is a significant implementation challenge.
Paper lists this as a challenge/limitation based on pilot experience and analysis. No quantified measure of integration effort is provided in the summary.
EPC projects feature milestone-based payments, complex stakeholder flows, and large working-capital needs that strain traditional on-premise ERPs.
Problem context statement presented in the paper; consistent with commonly reported characteristics of EPC projects. The summary does not cite empirical industry-wide data.
Reproducibility and deployment gaps are widespread: missing code, inconsistent benchmarks, and insufficient productionization focus (monitoring, model updates, rollback).
Surveyed literature often lacks released code and consistent benchmarks; thematic analysis highlights absence of operational deployment practices.
Common ML pipeline pitfalls include overfitting, poor cross-validation practices, lack of real-time/online evaluation, and inadequate feature engineering.
Critical assessment of experimental practices in the surveyed literature identifying methodological shortcomings that can inflate reported performance.
There is a lack of large, labeled, realistic IoT datasets; class imbalance, concept drift, dataset bias, and synthetic datasets that poorly reflect real traffic are common problems.
Review of datasets (N-BaIoT, Bot-IoT, TON_IoT, UNSW-NB15, KDD variants, custom/synthetic datasets) and critical assessment of their limitations across studies.
Resource constraints (limited CPU, memory, energy, and network bandwidth on devices and edge nodes) significantly limit feasible ML model complexity and deployment choices.
Multiple surveyed studies report hardware constraints and evaluate runtime/memory/latency; survey synthesizes these resource limitations as a recurring challenge.
Despite high reported detection accuracies in academic work, there is a shortage of production-grade, deployable ML-IDS for IoT.
Critical review of surveyed papers showing many report lab metrics but few report deployment case studies, production rollouts, or provide deployment artifacts (code, runtime/energy measurements).
Limitations of the review include restricted sample size, Scopus-only coverage, emergent-literature timeframe, and heterogeneity in study designs and measures, which constrain generalizability.
Authors' limitations subsection explicitly listing these constraints from their SLR process.
There has been insufficient attention in the literature to ethics, fairness, and consumer welfare in algorithmic pricing.
Persistent gap identified in the SLR—few or no included studies focused on ethics/fairness/welfare issues according to authors' coding.
Existing empirical studies on digital VBP exhibit methodological limitations, including small/limited samples, short time windows, and inconsistent measures.
Authors' methodological critique from the SLR based on assessment of study designs and measures reported in the 30 articles.
The evidence base is skewed toward pilots and high‑performer contexts; there is a lack of long‑panel, multi‑project longitudinal studies to validate typical returns and scalability.
Authors' assessment of evidence types in the 160 studies: mix of conceptual papers, case studies, pilots, and only limited larger empirical evaluations.
Empirical evaluation of integrated defenses, quantitative cost/benefit analyses, and standardized threat models for VR are research gaps that remain unaddressed in the literature window surveyed (2023–2025).
Authors' stated limitations from their comparative literature review of 31 studies noting an absence of primary empirical validation and quantitative economic analyses in the reviewed corpus.
Immersive VR systems collect continuous multimodal signals (motion tracking, gaze, voice, biometrics) that enable novel inference, spoofing, and manipulation attacks beyond traditional IT threats.
Synthesis of threat descriptions across the 31 reviewed peer‑reviewed studies (2023–2025) documenting sensor modalities and attack vectors; qualitative comparative evaluation of attack surfaces.
Mean emotional self-alignment between poster and responder is 32.7%, indicating systematic affective mismatch rather than congruence.
Pairwise comparison of emotion labels across post–response pairs in the dataset; computation of mean percentage where poster and immediate responder share the same emotion (32.7%).
Conversational coherence declines rapidly with thread depth, indicating shallow, weakly connected multi-turn exchanges.
Lexical-semantic coherence metrics (e.g., embedding-based similarity) computed across comment threads of varying depth in the Moltbook dataset; observed rapid decrease in coherence scores as thread depth increases.
When pipelines have cross-cutting ties, prices oscillate, allocation quality drops, and management becomes difficult.
Empirical simulation results from the ablation study: configurations with non-hierarchical, cross-cutting graph structures produced larger price volatility, frequent oscillations in price updates, and lower allocation value/throughput compared to hierarchical graphs (measured across many runs and random seeds within the 1,620-run experimental set).
On the 22 postdating (contamination-free) incidents, no agent achieved end-to-end exploitation success across all 110 agent–incident pairs evaluated.
Empirical evaluation of 110 agent–incident pairs reported in the study (end-to-end exploit attempts on the 22 incidents).
The original EVMbench had a data contamination risk because it relied on audit-contest data published before every evaluated model's release, which could have been seen during model training.
Timing relationship between the audit-contest dataset used by EVMbench and the release dates of evaluated models (dataset predated model releases).
The original EVMbench evaluation was narrow: it evaluated 14 agent configurations and most models were tested only with their vendor-provided scaffold.
Description of the original EVMbench experimental setup (number of agent configurations and scaffold usage) cited in this study.
Limitations of the study include reliance on self-reported perceptions (subject to response and survivorship bias), lack of experimental/causal identification, potential non-representative sample, and cross-sectional design limiting inference about long-term productivity effects.
Authors' stated limitations in the paper summary.
Current bottlenecks are disparate quantum and classical resources operating in isolation, causing manual job orchestration, inefficient scheduling, data-movement overheads, and slow iteration that limit productivity and algorithmic exploration.
Use-case-driven analysis and observations from early hybrid deployments and literature; systems design decomposition highlighting latency and data-staging requirements; no quantitative benchmark data.
Improving explainability can trade off with predictive performance, privacy, and robustness; these trade-offs must be managed rather than ignored.
Review aggregates technical literature and conceptual analyses documenting trade-offs reported by researchers (e.g., simpler interpretable models sometimes having lower predictive accuracy; disclosure risks to privacy; robustness concerns). No single causal estimate provided.
The evidence base presented is limited to a single SME pilot, so generalizability across sectors, firm sizes, and data regimes is untested and requires further research.
Explicit limitation noted in the paper and the fact that the pilot illustrated is a single case study (sample size = 1 SME pilot).
Tasks that are routine, repetitive, or pattern‑based (e.g., boilerplate coding, refactoring, unit test generation, some accessibility fixes) will be increasingly automated by AI.
Task‑level decomposition and examples of current automation capabilities (code generation, test suggestion tools); conceptual projection rather than empirical measurement.
Common barriers to effective RM implementation include siloed functions/weak coordination, limited resources or expertise, poor data quality/lack of metrics, and cultural resistance driven by short-term incentives.
Frequent identification of these barriers across the reviewed literature and practitioner sources synthesized via thematic analysis over the last ten years.
Hierarchy compresses: fewer organizational layers are needed for a given firm output as coordination costs fall.
Analytical proposition in the theoretical model and simulation results showing reduced number of layers under coordination compression.
Heterogeneity in study designs and contexts within the literature limits direct comparability and generalizability of findings.
Limitation noted in the paper based on the authors' assessment of diversity across the 103 reviewed studies (varying methods, contexts, metrics).
Institutional inertia, fragmented governance structures, limited technical capacity, and weak data stewardship impede scale‑up of AI systems in the public sector.
Thematic synthesis of barriers reported across empirical studies and institutional reports within the systematic review (103 items).
Low‑ and middle‑income contexts face persistent gaps—infrastructure, data ecosystems, and talent retention—that slow AI adoption in public governance.
Consistent findings across multiple studies in the 103‑item corpus reporting infrastructure deficits, weak data ecosystems, and brain drain/retention issues in LMIC settings.
On-Premise RAG requires internal technical capabilities (MLOps, infrastructure engineers) to maintain and update the system.
Organizational evaluation and implementation discussion noting operational responsibilities and skill requirements for on-prem deployment.
On-Premise RAG incurs higher latency compared with cloud RAG.
Technology evaluations included measured system latency comparisons between architectures; exact latency values and statistical details not provided in summary.
On-Premise RAG requires upfront capital expenditure (hardware) and ongoing maintenance (operations, model updates, staff).
Organizational evaluations / cost accounting and implementation discussion indicating hardware, operations, and personnel requirements for on-prem deployment; specific cost figures not provided in summary.