Evidence (7156 claims)
Adoption
5126 claims
Productivity
4409 claims
Governance
4049 claims
Human-AI Collaboration
2954 claims
Labor Markets
2432 claims
Org Design
2273 claims
Innovation
2215 claims
Skills & Training
1902 claims
Inequality
1286 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 369 | 105 | 58 | 432 | 972 |
| Governance & Regulation | 365 | 171 | 113 | 54 | 713 |
| Research Productivity | 229 | 95 | 33 | 294 | 655 |
| Organizational Efficiency | 354 | 82 | 58 | 34 | 531 |
| Technology Adoption Rate | 277 | 115 | 63 | 27 | 486 |
| Firm Productivity | 273 | 33 | 68 | 10 | 389 |
| AI Safety & Ethics | 112 | 177 | 43 | 24 | 358 |
| Output Quality | 228 | 61 | 23 | 25 | 337 |
| Market Structure | 105 | 118 | 81 | 14 | 323 |
| Decision Quality | 154 | 68 | 33 | 17 | 275 |
| Employment Level | 68 | 32 | 74 | 8 | 184 |
| Fiscal & Macroeconomic | 74 | 52 | 32 | 21 | 183 |
| Skill Acquisition | 85 | 31 | 38 | 9 | 163 |
| Firm Revenue | 96 | 30 | 22 | — | 148 |
| Innovation Output | 100 | 11 | 20 | 11 | 143 |
| Consumer Welfare | 66 | 29 | 35 | 7 | 137 |
| Regulatory Compliance | 51 | 61 | 13 | 3 | 128 |
| Inequality Measures | 24 | 66 | 31 | 4 | 125 |
| Task Allocation | 64 | 6 | 28 | 6 | 104 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 10 | 16 | 93 |
| Worker Satisfaction | 42 | 32 | 11 | 6 | 91 |
| Task Completion Time | 71 | 5 | 3 | 1 | 80 |
| Wages & Compensation | 38 | 13 | 19 | 4 | 74 |
| Team Performance | 41 | 8 | 15 | 7 | 72 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 17 | 15 | 9 | 5 | 46 |
| Job Displacement | 5 | 28 | 12 | — | 45 |
| Social Protection | 18 | 8 | 6 | 1 | 33 |
| Developer Productivity | 25 | 1 | 2 | 1 | 29 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 7 | 4 | 9 | — | 20 |
Significantly more heavy LLM users reported that the writing was less creative and not in their voice.
Self-reported measures from participants in the human user study comparing heavy LLM users to others; no sample size or exact statistics provided in the excerpt.
In Chicago, the model shows moderate under-detection of Black residents with DIR equal to 0.22.
Reported DIR value from simulation results on Chicago 2022 data.
It is impractical to uniformly apply an alignment method across diverse, independently developed AI models in strategic settings.
Paper assertion / motivating argument (stated as motivation for investigating zero-shot Nash-like behavior); not presented as an empirical finding within the paper.
The gap between informal natural language requirements and precise program behavior (the 'intent gap') has always plagued software engineering, but AI-generated code amplifies it to an unprecedented scale.
Conceptual claim and argumentation in the paper; presented as an observed escalation in the scale of the existing 'intent gap' due to AI code generation. No quantitative evidence or sample size given in the excerpt.
The crowding-out effect of AI washing on green innovation is heterogeneous: private enterprises, small and medium-sized enterprises (SMEs), and firms in highly competitive sectors suffer more severe negative impacts.
Subgroup/heterogeneity analysis reported in the paper on the same sample of Chinese A-share listed companies (2006–2024); abstract identifies private firms, SMEs, and firms in highly competitive industries as more affected.
The negative relationship between AI washing and green innovation is transmitted through dual channels in both product and capital markets.
Mechanism analysis reported in the paper (presumably mediation or channel analysis) using the same dataset of Chinese A-share firms' annual reports and firm-level market data; abstract states product- and capital-market channels convey the crowding-out effect.
Corporate AI washing exerts a significant crowding-out effect on green innovation.
Empirical analysis using semantic measures of 'AI washing' derived from large language model (LLM) analysis of annual reports for Chinese A-share listed companies (2006–2024); paper reports statistically significant negative relationship between AI washing and firms' green innovation (details of regression models not provided in abstract).
The capital-output elasticity dropped significantly, from 0.42 in 2010–2015 to 0.35 in 2016–2022.
Estimated from an extended Cobb–Douglas production function applied to China's economy over 2010–2022, with period split 2010–2015 vs 2016–2022 (as reported in the study summary).
These dynamics amplify initial disparities and produce persistent performance gaps across the population.
Main theoretical conclusion of the paper: analysis of the proposed dynamical system showing amplification and persistence of gaps (authors' demonstrated result).
Exclusion-based cohesion can produce state-contingent illusory precision together with effective input concentration and dynamic lock-in simultaneously—i.e., these phenomena co-occur under the model's parameter regimes.
Analytical model results showing co-occurrence of multiple adverse phenomena (bias that grows in tails, illusory precision, input concentration, lock-in) under the same exclusion mechanisms; derived within the paper's theoretical framework.
When the anchor belief is updated from internally filtered aggregates, the system can exhibit dynamic lock-in: delayed recognition of regime shifts followed by abrupt correction.
Analytical dynamics studied in the model when anchor updates depend on filtered (excluded) aggregates; derivations demonstrate delayed detection and abrupt adjustments. This is a theoretical/dynamical model result, no empirical data.
Exclusion leads to effective concentration of decision inputs: the effective number of independent inputs falls below the nominal participant count.
Model-derived analytic result showing that report shrinkage and discarding reduce effective information contributions, quantified relative to nominal participation in the theoretical framework. No empirical sample.
Exclusion-based cohesion induces 'illusory precision': observed disagreement can fall while actual estimation error in tail regimes rises (i.e., lower recorded variance despite higher true error).
Theoretical result derived from the signal-aggregation model showing a regime in which filtered reports reduce observed variance even as tail-regime estimation error increases. No empirical validation provided.
Relative to a full-inclusion benchmark, exclusion-based cohesion produces state-contingent bias that is small in normal regimes but grows sharply under regime displacement (tail events).
Analytical comparisons between the exclusion model and a full-inclusion benchmark within the theoretical model; derivations showing bias as a function of regime and exclusion parameters. The result is from model analysis, not empirical data.
The establishment of the China–ASEAN Free Trade Area (CAFTA) reduced regional trade policy uncertainty.
Empirical analysis treats CAFTA as an exogenous policy shock and measures a decline in regional trade policy uncertainty using firm‑ and trade‑level data from the China Industrial Enterprise Database and China Customs Database covering 2000–2014; identification via difference‑in‑differences (DID). (Sample sizes not specified in provided summary.)
Limitations include possible limited organizational generalizability due to a single Fortune 500 lab context; ABS results depend on model specification/calibration; and operational definitions of 'resilience' and 'planning cycle' require careful reading.
Authors' reported limitations based on study design: single lab context (n = 23), dependence of ABS on model choices, and nontrivial operational definitions.
Some declines (in self-efficacy and meaningfulness) from passive AI use persist after participants return to manual work.
Within-experiment assessment of outcomes after participants returned to manual (no-AI) tasks following the AI-use manipulation in the pre-registered experiment (N = 269); reported persistent reductions in self-efficacy and meaningfulness for the passive condition.
Passive use of AI reduces perceived meaningfulness of work.
Pre-registered experiment (N = 269) with self-reported measure of work meaningfulness; passive-copy condition showed lower meaningfulness ratings than No-AI and Active-collaboration conditions.
Passive use of AI reduces psychological ownership of the produced outputs.
Same pre-registered experiment (N = 269). Participants in the passive-copy AI condition reported lower psychological ownership of their outputs (self-report scales) relative to No-AI and Active-collaboration conditions.
Passive use of AI (copying AI-generated output) reduces workers' self-efficacy.
Pre-registered between-subjects experiment (N = 269) using occupation-specific writing tasks. Participants assigned to a passive-copy AI condition reported lower self-efficacy (self-reported confidence to complete tasks without AI) compared to the No-AI (manual) and Active-collaboration conditions.
Securitization of economic dependencies—especially in strategic sectors (semiconductors, telecoms, cloud)—frames partner states as security risks and exposes them to blacklists, de-risking campaigns, and sudden loss of market access.
Process tracing of export controls and blacklisting episodes; chronologies of sanction/policy actions affecting firms and partners; policy documents and public lists (e.g., export-control lists). (Data sources: export-control lists, sanction policy documents, corporate/access denials; sample sizes not specified.)
Large-scale AI models have significant energy and resource costs, creating a notable environmental footprint that must be addressed.
Narrative integration of prior empirical studies measuring compute, energy consumption, and embodied emissions of large models (cited literature); the review does not present new quantitative measurements itself.
As AI is deployed in safety-critical domains, reliability, regulation, and human-oriented system design become essential to avoid harms.
Review of literature on safety-critical systems, human–machine interaction studies, and regulatory policy discussions; the paper reports this as a consensus implication rather than presenting new empirical tests.
Stronger empirical evidence is needed on how hazard, exposure, and vulnerability interact across space and time to shape aggregated multi-risks.
Evaluation of project activities and case studies identifying gaps in empirical spatio-temporal analyses of interacting risk components; synthesis recommends targeted empirical work.
The current literature is skewed toward descriptive and engineering work; there is a lack of causal, field‑experimental evidence on NLP interventions' effects on customer behavior and firm profits.
Review coding of study types in the sample (engineering/descriptive vs. experimental/causal) showing few field experiments or causal designs.
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.
AI‑enabled platforms can magnify winner‑takes‑most dynamics in digital services trade, concentrating market power.
Theoretical and empirical literature on network effects and platform markets reviewed in the paper; illustrative examples (no novel empirical aggregation).
Current data governance regimes in China can impede cross‑border data flows.
Comparative policy analysis and literature documenting data localization and privacy/regulatory regimes that restrict flows (descriptive evidence in the review).
Institutional barriers—fragmented international rules on data flows and privacy, regulatory divergence including data localization, weak participation in multilateral rule setting, and uneven domestic regulation of platforms—impede digital services trade.
Comparative policy analysis and literature review, supported by policy documents and case examples (qualitative evidence; no original econometric tests).
Problem C is the practical difficulty of attributing responsibility and agency across distributed socio-technical systems (robots, algorithms, institutions, humans).
Conceptual diagnosis developed in the paper and exemplified with vignettes from three application domains; defined as an analytic concept rather than empirically measured.
Jurisdictions are taking divergent policy approaches (e.g., U.S. emphasis on innovation/competition, EU emphasis on rights/standards like GDPR), producing fragmented digital trade rules.
Comparative legal and policy analysis of existing national/regional rules and international instruments (examples cited include GDPR and U.S. policy orientations); descriptive, with specific regulatory texts analyzed.
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.
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.
Exposure to top-rated exemplar papers produced large reductions in interquartile range (IQR) of estimates—within converging measure families, IQR fell by roughly 80–99%.
Stage 3 of the protocol: after agents were shown top-rated exemplar papers, measured within-measure-family IQRs of agents' estimates decreased substantially; reported quantitative reduction range of 80%–99% within measure families that converged.
Frontier language models and human editors do not reliably reproduce the evaluative signal contained in institutional publication records.
Comparison of zero-shot frontier-model average accuracy (31%) and human-panel majority-vote accuracy (42%) versus fine-tuned models (up to 59% and higher in economics), indicating that neither zero-shot frontier models nor the human panels matched fine-tuned performance on the held-out benchmarks.
Eleven frontier language models (proprietary and open) averaged 31% accuracy on a held-out four-tier benchmark of management research pitches (chance ≈25%); this is only marginally above chance.
Zero-shot (or as-provided) evaluation of eleven state-of-the-art language models on the held-out four-tier management pitches benchmark, yielding an average accuracy of 31% versus chance ≈25%. (Exact list of models and number of benchmark examples not provided in the supplied text.)
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.