Evidence (13870 claims)
Adoption
8467 claims
Productivity
7558 claims
Governance
6805 claims
Human-AI Collaboration
6363 claims
Org Design
4132 claims
Innovation
4065 claims
Labor Markets
3526 claims
Skills & Training
2945 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 196 | 98 | 892 | 1984 |
| Governance & Regulation | 817 | 394 | 188 | 121 | 1544 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 627 | 233 | 123 | 96 | 1088 |
| Research Productivity | 411 | 123 | 56 | 332 | 933 |
| Output Quality | 467 | 178 | 59 | 47 | 751 |
| 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 | 167 | 122 | 24 | 496 |
| Task Allocation | 207 | 64 | 71 | 32 | 379 |
| Skill Acquisition | 165 | 59 | 60 | 17 | 301 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 52 | 107 | 13 | 279 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 150 | 48 | 26 | 3 | 227 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 63 | 20 | 12 | 184 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 93 | 21 | 13 | 19 | 148 |
| 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 |
| Creative Output | 31 | 17 | 7 | 3 | 59 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Zero-shot baselines and standard retrieval stagnate around 50-60% accuracy across model generations on the graduate-level final exam.
Pilot study reported on a full graduate-level final exam comparing zero-shot and standard retrieval baselines across model generations; reported accuracy range given as ~50-60%. Exact number of exam questions or models compared not stated.
Afriat's theorem guarantees that demand satisfies the Generalized Axiom of Revealed Preference (GARP) if and only if it can be generated by maximizing some utility function subject to a budget constraint.
Theoretical claim citing Afriat's theorem (mathematical result used as foundational justification in the paper).
We fine-tune Amazon Chronos-2, a transformer-based probabilistic time-series model, on synthetic data generated from utility-maximizing agents.
Methods described in the paper: authors report fine-tuning Chronos-2 on synthetically generated time series from utility-maximizing agents (methodological statement).
This yields a common scale (bits of usable information) for comparing a wide range of interventions, contexts, and models.
Theoretical implication of the authors' formalization combining Bayesian persuasion and V-usable information (paper argues for a common information scale measured in bits).
To formalize mecha-nudges, we combine the Bayesian persuasion framework with V-usable information, a generalization of Shannon information that is observer-relative.
Methodological/theoretical development described in the paper (formal combination of two theoretical frameworks).
We introduce mecha-nudges: changes to how choices are presented that systematically influence AI agents without degrading the decision environment for humans.
Conceptual/definitional contribution made in the paper (novel concept introduced by authors).
Nudges are subtle changes to the way choices are presented to human decision-makers (e.g., opt-in vs. opt-out by default) that shift behavior without restricting options or changing incentives.
Background/definition stated in the paper (conceptual; references to standard behavioral-economics definition of nudges).
Data sources include field research conducted in 2024 and public reports from the Ministry of Industry and Information Technology and the National Bureau of Statistics.
Paper statement describing data provenance: field surveys in 2024 (n=326) plus public reports from MIIT and National Bureau of Statistics.
The visualization avoided redistributing value.
Reported result from the within-subjects experiment (N=32) stating that the visualization did not redistribute value between parties (i.e., it improved outcomes/efficiency without changing value split).
We conduct an in-depth case study of SWE-bench GitHub issue resolution using two representative models, GPT-5 mini and DeepSeek v3.2.
Descriptive: authors report running an in-depth case study on the SWE-bench GitHub issue resolution dataset using two named models (GPT-5 mini and DeepSeek v3.2).
Human-like presentations did not raise conformity pressure.
Reported experimental result: manipulaton of presentation style (human-like vs not) and measurement of conformity pressure; the abstract states that human-like presentation increased perceived usefulness/agency without increasing conformity pressure. No quantitative details provided in abstract.
Larger panels yielded no gains in accuracy relative to a single AI.
Reported experimental comparison manipulating panel size in the study (three tasks). The abstract states that larger panels did not produce accuracy gains versus a single AI. (No sample size or numerical effect reported in abstract.)
The authors construct a mean-reverting jump-diffusion stochastic process model and conduct Monte Carlo simulations to evaluate hedging efficiency of the proposed futures contracts.
Methodological claim: explicit description of the mathematical model (mean-reverting jump-diffusion) and simulation method (Monte Carlo) used in the paper.
The paper proposes an original 'Revenue-Sharing as Infrastructure' (RSI) model in which the platform offers its AI infrastructure for free and takes a percentage of the revenues generated by developers' applications, reversing the traditional upstream payment logic.
Theoretical model proposal and conceptual description in the paper; presented as original contribution (no empirical implementation reported).
Recent literature distinguishes three generations of business models: a first generation modeled on cloud computing (pay-per-use), a second characterized by diversification (freemium, subscriptions), and a third, emerging generation exploring multi-layer market architectures with revenue-sharing mechanisms.
Literature review and conceptual synthesis presented in the paper; no empirical study or sample reported.
Capital income taxes, worker equity participation, universal basic income, upskilling, and Coasian bargaining cannot eliminate the excess automation.
Model-based policy counterfactuals evaluated in the paper showing these interventions fail to achieve the social optimum in the theoretical framework; no empirical sample.
Wage adjustments and free entry cannot eliminate the excess automation.
Analytical result in the model showing endogenous wage changes and free entry do not restore the socially optimal level of employment; theoretical equilibrium analysis, no empirical data.
We analyze a regional standardized sentiment database (97,719 responses).
Dataset description in the paper specifying the size of the standardized sentiment database.
We analyze a raw Fukui spending database (90,350 records).
Dataset description in the paper specifying the size of the raw Fukui spending database.
We evaluate our approach on spapi, a production in-vehicle API system at Volvo Group involving 192 endpoints, 420 properties, and 776 CAN signals across six functional domains.
Case study / evaluation dataset description (explicit counts provided in paper).
The analysis relies on partial least squares path modeling (PLS-PM) to test eight predictions linking technological perceptions, organizational factors, and adoption outcomes.
Author-stated analytical method: PLS-PM; eight predictions tested; uses the survey data described above.
The study uses cross-sectional survey data from 523 human resource professionals and hiring managers representing 184 organizations across multiple industries in the United States.
Author-stated sample description in the paper: cross-sectional survey; 523 HR professionals/hiring managers; 184 organizations; multiple industries; U.S.
Each task is evaluated under three agent configurations (no-skills, LLM-generated skills, and human-expert skills) and validated through real hardware execution.
Experimental design described in the paper specifying three agent configurations per task and hardware validation of task runs.
IoT-SkillsBench spans three representative embedded platforms, 23 peripherals, and 42 tasks across three difficulty levels.
Benchmark composition statistics reported in the paper (counts of platforms, peripherals, tasks, and difficulty levels).
We introduce a skills-based agentic framework for HIL embedded development together with IoT-SkillsBench, a benchmark designed to systematically evaluate AI agents in real embedded programming environments.
Methodological contribution described in the paper (introduction of framework and benchmark; the paper reports design and implementation).
The cooperative video game KeyWe, with a scripted agent, served as a valid testbed for studying human-agent teamwork and the effects of the training intervention.
Methodological choice: KeyWe was used as the experimental environment and the agent behavior was scripted for consistency; all behavioral and performance measures were collected within this setting.
Half of the participants received the teamwork training and half did not (between-subjects comparison).
Experimental design description: participants were split into trained and untrained groups (50/50).
The study observes five delivery configurations: a traditional baseline and four successive platform versions (V1–V4).
Study design described by the authors; outcomes measured across these five configurations for the three programs.
The study covers three real software modernization programs: a COBOL banking migration (~30k LOC), a large accounting modernization (~400k LOC), and a .NET/Angular mortgage modernization (~30k LOC).
Study design / sample description provided by the authors in the paper's methods section.
Evidence on AI in software engineering still leans heavily toward individual task completion, while evidence on team-level delivery remains scarce.
Paper's literature-context statement (intro); asserted by the authors as motivation for the study (no primary data supporting this meta-claim provided within the study).
The research methodology is based on the envelope model ("input" orientation) to assess the level of transformation of labor resources and labor markets due to the spread of artificial intelligence.
Methodological statement in the paper specifying the use of an input-oriented envelope model applied to a sample of European Union countries.
We document a systematic pattern we call the 'Intent-Source Divide' (experiential vs transactional intent is associated with different source mixes).
Labeling of the observed consistent association between query intent (experiential vs transactional) and citation-source mix in the audited dataset of Google Gemini responses.
We audit 1,357 grounding citations from Google Gemini across 156 hotel queries in Tokyo.
Manual audit of Google Gemini grounding citations for 156 hotel queries in Tokyo; counted 1,357 grounding citations.
The model yields two limits on the speed of learning and adoption: a structural limit determined by prerequisite reachability and an epistemic limit determined by uncertainty about the target.
Theoretical result stated in the paper (model-derived identification of two distinct limiting factors on learning speed).
Teaching is modeled as sequential communication with a latent target.
Modeling assumption explicitly stated in the paper (formalization of teaching in the theoretical framework).
The paper models the learner as a mind: an abstract learning system characterized by a prerequisite structure over concepts.
Modeling assumption explicitly stated in the paper (definition of the 'mind' in the theoretical model).
The findings provide evidence against concerns that AI mediation undermines people's ability to distinguish truth from lies.
Synthesis of experimental results showing unchanged lie-detection accuracy despite declines in perceived trust/confidence.
Participants were no more inclined to suspect those using AI tools of lying.
Experimental comparisons assessing participants' propensity to suspect AI-mediated speakers of deception showed no increase in suspicion for users of AI tools.
Participants' actual judgment accuracy (ability to detect lies) remained unchanged across AI-mediated and non-AI-mediated videos.
Primary experimental result comparing lie-detection accuracy (truthful vs deceptive statements) across the three AI mediation conditions in the preregistered experiments (N = 2,000).
We conducted two preregistered online experiments (N = 2,000).
Methods statement in the paper: two preregistered online experiments with a combined sample size of 2,000 participants.
The study collected data from 293 questionnaire respondents and 12 interview participants.
Mixed-methods data collection reported in the paper: n=293 survey respondents and n=12 interviewees.
The study synthesises findings from 36 peer-reviewed articles published between 2015 and 2025.
Systematic literature synthesis / review of peer-reviewed articles; sample = 36 articles (2015–2025) as stated in the paper.
This research deepens theoretical understanding by integrating CE principles, Industry 4.0 architectures, green innovation theory, and lifecycle assessment into a unified conceptual framework.
Authors' description of theoretical contribution in the abstract, based on their synthesis of the bibliometric and systematic review findings.
This study offers the first comprehensive mixed-methods assessment of how AI transforms industrial production ecosystems in the post-ChatGPT era.
Authors' methodological/novelty claim in the abstract; supported by description of methods (bibliometric analysis of 196 articles and systematic review of 104 studies).
We construct a multidimensional energy justice index to analyze AI’s net effects, pathways, and institutional dependencies.
Methodological statement: authors create an energy justice index (multidimensional) used as dependent variable in empirical analysis.
This study uses a panel dataset for 30 Chinese provinces from 2008 to 2022.
Statement of dataset coverage in the paper: 30 provinces, years 2008–2022 (panel data).
This study uses a mixed-method research design combining quantitative ROI modelling and cost–benefit analysis, qualitative synthesis of secondary enterprise case studies, and architectural analysis of Azure-native GenAI services.
Explicit methodological description in the abstract of the paper.
Ninety-five high-quality studies were analyzed using principal component analysis and k-means clustering.
Paper states screening produced 95 high-quality studies which were subjected to PCA and k-means clustering for analysis.
A systematic literature review of 450 records from major databases was conducted using PRISMA 2020 guidelines.
Statement in the paper describing methods: systematic literature review using PRISMA 2020; initial search returned 450 records from major databases.
This Article presents the results of an experiment in which a transcript of a hypothetical client interview involving potential disability discrimination, retaliation, and wrongful termination claims was submitted to each AI system, with prompts requesting identification and assessment of viable legal theories.
Methodological description of the experiment: one hypothetical client interview transcript fed to each of four AI engines with prompts to identify and assess legal theories.