Evidence (7953 claims)
Adoption
5539 claims
Productivity
4793 claims
Governance
4333 claims
Human-AI Collaboration
3326 claims
Labor Markets
2657 claims
Innovation
2510 claims
Org Design
2469 claims
Skills & Training
2017 claims
Inequality
1378 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 402 | 112 | 67 | 480 | 1076 |
| Governance & Regulation | 402 | 192 | 122 | 62 | 790 |
| Research Productivity | 249 | 98 | 34 | 311 | 697 |
| Organizational Efficiency | 395 | 95 | 70 | 40 | 603 |
| Technology Adoption Rate | 321 | 126 | 73 | 39 | 564 |
| Firm Productivity | 306 | 39 | 70 | 12 | 432 |
| Output Quality | 256 | 66 | 25 | 28 | 375 |
| AI Safety & Ethics | 116 | 177 | 44 | 24 | 363 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 76 | 38 | 20 | 315 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 77 | 34 | 80 | 9 | 202 |
| Skill Acquisition | 92 | 33 | 40 | 9 | 174 |
| Innovation Output | 120 | 12 | 23 | 12 | 168 |
| Firm Revenue | 98 | 34 | 22 | — | 154 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 84 | 16 | 33 | 7 | 140 |
| Inequality Measures | 25 | 77 | 32 | 5 | 139 |
| Regulatory Compliance | 54 | 63 | 13 | 3 | 133 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Task Completion Time | 88 | 5 | 4 | 3 | 100 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 32 | 11 | 7 | 97 |
| Wages & Compensation | 53 | 15 | 20 | 5 | 93 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 24 | 22 | 9 | 6 | 62 |
| Job Displacement | 6 | 38 | 13 | — | 57 |
| Hiring & Recruitment | 41 | 4 | 6 | 3 | 54 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 10 | 6 | 2 | 40 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 5 | 9 | — | 26 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
This study is descriptive and comparative rather than quantitative; it relies on available policy documents and secondary literature rather than original field interviews or measured outcomes.
Explicit methodological statement in the paper listing qualitative document analysis, comparative literature review, and policy commentary; limitation acknowledged by authors.
A research agenda for AI economics should include: formalizing consent as a transaction/contracting problem; empirical RCTs and natural experiments measuring effects of consent designs; mechanism design for privacy-preserving data sharing; and policy evaluation of consent regulations.
Explicitly listed research directions in the workshop outputs and position papers; these are proposed next steps rather than empirical findings.
Follow-up empirical methods should include qualitative interviews, focus groups, usability studies, field experiments (A/B tests), and policy/legal-technical assessments.
Recommended research methods enumerated in the workshop outputs and position papers; these are proposed future methods rather than findings from conducted studies.
The Futures Design Toolkit (scenario planning, persona generation, speculative design) was used as a primary method in the workshop.
Methodological description in the workshop summary listing the Futures Design Toolkit and associated activities; procedural claim rather than empirical.
The study has potential selection and ecological-validity constraints because it was conducted at two institutions across six courses, limiting generalizability.
Authors note limitations regarding sample scope (two institutions, six courses) and the ecological validity of the experimental tasks/settings.
The study employed a multi-method approach combining experimental quantitative analysis (descriptives, GLM, non-parametric robustness checks) with qualitative topic-based coding of open-ended survey responses.
Methods description: randomized/experimental assignment; quantitative analyses using GLM and non-parametric tests; qualitative topic-based coding of student responses; sample N = 254 across six courses at two institutions.
The study did not directly measure accessibility or impacts on students with disabilities, though qualitative results suggest possible intersections with inclusive and multimodal learning design.
Limitation stated by authors: no direct measurement of accessibility outcomes; qualitative responses hinted at potential relevance to inclusive design but no empirical measurement of disability-related impacts.
The study focused on short-term, knowledge-based tasks and did not measure long-term learning or retention.
Authors explicitly note as a limitation that the experimental tasks were short-term and knowledge-based and that long-term retention was not measured.
Empirical generalization across all climate-AI systems is constrained by heterogeneous data availability and proprietary models, limiting the ability to produce universal quantitative claims.
Stated methodological limitation in the paper, noting heterogeneous data and the proprietary nature of some models restrict broad generalization.
The paper does not provide granular quantitative estimates of the economic cost of infrastructural asymmetries in climate-AI.
Explicit limitation stated by the authors in the Methods/Limitations section.
There is a need for empirical research quantifying earnings dispersion, labor substitution effects, and the welfare impacts of GenAI-driven content economies over time.
Explicit research recommendation made in the paper based on gaps identified during analysis of the 377 videos (study is qualitative and does not measure these outcomes).
The analysis identifies ten shared use cases that creators present as pathways to income using GenAI.
Coding of the 377-video corpus resulted in a catalog of ten use cases (as reported in the paper).
Risk and ambiguity manipulations: risk condition communicated a single explicit leak probability of 30%; ambiguity condition communicated the leak probability as a range (10–50%).
Paper's methods section describing the manipulations used in the randomized experiment (N = 610); these specific probability framings were the core independent-variable manipulations.
Experimental design: study used a 2 × 3 between-subjects design with N = 610, crossing information environment (Risk vs Ambiguity) with privacy-treatment conditions (including privacy-threatening vs neutral and different data-type labels).
Methodological description reported in the paper: participants (N = 610) randomized across 6 experimental arms derived from the 2 (Risk vs Ambiguity) × 3 (privacy treatments) factorial design; tasks included choosing between a standard product basket and an AI-personalized basket.
When leak probabilities are known (risk condition: explicit 30% leak probability), adoption of personalization is about 50% and is not significantly affected by privacy-threatening versus neutral information.
Same randomized experiment (N = 610) with a risk manipulation that explicitly stated a single 30% leak probability. Measured adoption rates showed roughly 50% uptake and no statistically significant difference between privacy-threatening and neutral conditions under risk.
Many apparent inter-domain differences vanish once measurement uncertainty is accounted for.
Bootstrap confidence intervals and repeated-sample comparisons showing that differences in citation share or prevalence observed in single-run snapshots are often not statistically significant when uncertainty from repeated sampling is included.
Falsifiability condition for intermediation-collapse: If intermediary margins remain stable despite measurable declines in information frictions, the intermediation-collapse mechanism is falsified.
Stated empirical test in the paper that compares measured intermediary markups/margins to proxies for information frictions and AI-driven automation across affected sectors.
Falsifiability condition for Ghost GDP: If monetary velocity does not decline (or instead rises) as the labor share falls, the Ghost GDP channel is unsupported by the data.
Explicit falsification condition provided in the paper based on the model link labor share -> velocity -> consumption; suggested empirical test using monetary-velocity proxies and labor-share series from FRED.
Empirically, top-quintile households account for roughly 47–65% of U.S. consumption.
Calibration and reported quantitative scenarios in the paper using U.S. consumption concentration data (constructed from U.S. consumption/income micro- and macro-data sources referenced in the methods section).
Economy & Finance threads contained no self-referential content, suggesting agents can engage in market discussion without representing themselves as agents.
Topic-model-derived topical category labeling and tagging for self-referential themes showing zero instances of self-reference in posts categorized as Economy & Finance in the dataset; counts derived from the 361,605 posts.
Because the sample is small and purposive and the design is qualitative, insights are rich but not statistically representative or quantified across the broader research landscape.
Authors' stated study limitations in the paper acknowledging small purposive sample (n=16) and qualitative design.
The study's data come from semi-structured interviews with 16 expert practitioners across biosecurity, cybersecurity, education, and labor.
Study methods reported in the paper: qualitative data source explicitly stated as 16 semi-structured interviews across listed domains.
The authors released their code and data for reproducibility at https://github.com/blocksecteam/ReEVMBench/.
Statement in the paper indicating public release of code and dataset at the provided GitHub URL.
Crystallization Efficiency (CE) is defined as Useful_Crystallized_Knowledge / (Human_Effort × Time).
Operational formalism and metric definitions presented in the paper (explicit formula provided). This is a proposed metric, not an empirically validated measure.
The paper proposes operational patterns (Dual-Workspace Pattern separating live interaction workspace and persistent knowledge workspace) and a Spiral Development Model (iterative interaction → crystallization → validation → redeployment).
Operational framework section describing patterns and workflows; illustrated in the case study implementation.
The Knowledge Crystallization Cycle formalizes operations (extract, synthesize, validate, integrate) and proposes efficiency and quality metrics including Crystallization Efficiency (CE), Fidelity, Reuse Rate, and Freshness/Volatility Score.
Operational formalism section of the paper presenting metric definitions and proposed calculations (e.g., CE = Useful_Crystallized_Knowledge / (Human_Effort × Time)). These are proposed metrics, not validated at scale.
The paper introduces a Three-Layer Cognitive Architecture that organizes agent knowledge by volatility and degree of personalization (stable/core knowledge; institutionalized heuristics/patterns; volatile/session-level tacit details).
Architectural specification presented in the paper (conceptual design document). No experimental validation beyond the illustrative case study.
Nurture-First Development (NFD) reframes agent creation from a one-time engineering task into a continuous, conversational growth process.
Conceptual formalization in the paper (architectural and operational descriptions). No large-scale empirical test reported; supported by theoretical argumentation and illustrative examples.
Findings are based on a student sample rating decontextualized messages, so external validity to industry communication or real project logs is uncertain and requires replication.
Study sample consisted of 81 students in team-based software projects labeling decontextualized statements; authors explicitly note this limitation as a caveat.
Many apparent correlations between predictors and sentiment labels do not remain significant after global multiple-testing correction.
Correlation analyses across many predictors with explicit application of multiple-testing correction procedures; many initial signals failed to survive correction.
The paper does not provide quantitative estimates of time saved per report, cost reductions, or effects on employment/wages; such economic impacts remain to be quantified.
Caveats noted in the paper: absence of quantitative estimates for time/cost/employment effects and a call for field trials and economic modeling. This is explicitly stated in the summary.
The paper used a clinically grounded, multi-level evaluation framework that separately assessed raw AI drafts (automatic metrics + clinician review) and radiologist-AI collaborative final reports (how radiologists edit and downstream clinical effects), including comparisons across radiologist experience levels.
Methodology section summarized in the paper: multi-level assessment covering AI drafts and radiologist-edited collaborative reports; combination of automatic metrics and radiologist-/clinician-centered evaluations; experience-level stratified analyses (novice/intermediate/senior).
CBCTRepD is a report-generation system trained on this curated paired dataset to produce bilingual CBCT radiology draft reports intended for radiologist-in-the-loop (co-authoring) workflows.
System description in the paper: CBCTRepD built using the curated dataset; authors state purpose is to generate clinically usable drafts for radiologist editing. (Model architecture and training hyperparameters are not specified in the provided text.)
The authors curated a paired CBCT–report dataset of approximately 7,408 CBCT studies covering 55 oral and maxillofacial disease entities that is bilingual and includes diverse acquisition settings.
Data curation described in the paper: stated dataset size (~7,408 studies), coverage of 55 disease entities, bilingual reports, and inclusion of a range of acquisition settings to increase heterogeneity and clinical realism. (Exact languages, provenance of studies, and dataset split details are not specified in the provided text.)
The workshop identifies specific research directions for AI economics: cost–benefit and ROI analyses of shared infrastructure; market design for procurement of co-designed systems; models of innovation incentives under different IP/data-governance regimes; labor market impact assessments; and empirical studies of how validation ecosystems affect adoption rates and pricing.
Explicitly listed research directions in the workshop summary and roadmap produced by consensus at the NSF workshop (Sept 26–27, 2024).
The workshop's findings are based on qualitative synthesis of expert judgment and stakeholder inputs rather than primary empirical data or controlled experiments.
Explicitly stated in the Data & Methods section of the workshop summary; methods: expert panels, thematic breakout sessions, cross-disciplinary discussions, consensus-building.
The workshop convened researchers, clinicians, and industry leaders to address co-design across four thematic areas: teleoperations/telehealth/surgical operations; wearable and implantable medicine; home ICU/hospital systems/elderly care; and medical sensing/imaging/reconstruction.
Workshop agenda and participant list from the two-day NSF workshop (Sept 26–27, 2024); methods included thematic breakout sessions focused on these four areas. Documentation at https://sites.google.com/view/nsfworkshop.
Evaluation was performed on five different material setups.
Experimental evaluation described in the summary: performance reported as averaged across five material setups. The summary does not list per-setup names or trial counts.
The simulation models samples as collections of spheres with per-sphere procedurally generated dislodgement-force thresholds derived from Perlin noise to introduce spatial heterogeneity and diversity.
Simulation/modeling description in the paper: discrete-sphere representation of sample; each sphere assigned a dislodgement threshold; spatial variation produced via Perlin noise. This is a concrete modeling choice reported in the methods.
The paper uses a mixed-methods approach combining a systematic literature review with an empirical practitioner survey to assess perceptions, adoption, and impact of AI-driven tools.
Methodological statement in the paper; survey design covers tool usage, perceived benefits, challenges, and expectations.
Empirical work (experiments and measurements) is needed to quantify how much value interpretive traces add to downstream outputs, how RATs affect platform incentives, and what governance frameworks fairly allocate resulting rents.
Concluding recommendation in the paper stating the research gaps; not an empirical claim but a stated need.
The current presentation of RATs is speculative and illustrative; empirical validation, scalability, and ethical safeguards remain to be developed.
Limitations section of the paper explicitly states the speculative nature and lack of empirical evaluation.
Implementation of RATs requires instrumentation at the browser/platform level or via plugins and must address privacy/consent, storage/ownership, sharing controls, and interoperable trace formats.
Design and implementation considerations enumerated in the paper; this is a requirements statement rather than an empirical claim.
Analytical approaches compatible with RATs include sequence/trajectory mining, network analysis of associations/co-read graphs, embedding/clustering of trajectories, qualitative inspection of reflections, and experimental (A/B or RCT) evaluation of downstream effects.
Methods section of the paper listing suggested analytical techniques; these are proposed methods rather than applied analyses.
The approach shifts some computational burden to obtaining MCMC samples of the parameter posterior, requiring access to (or ability to compute) MCMC samples before surrogate training.
Method description: training data are MCMC-drawn parameter vectors; the paper notes this practical requirement and trade-off (MCMC cost vs. avoiding repeated expensive forward-model evaluations).
More theoretical work is needed to establish guarantees (consistency, asymptotic behavior, and frequentist coverage) for these networks when applied in economic settings.
Stated research need/caveat in the paper; no new theoretical proofs are provided in the summary to establish these properties.
The Boson Sampling Born Machine (BSBM) is a generative model whose model distribution is the output probability distribution of a linear-optical (bosonic modes) circuit.
Definition and constructive specification in the paper: model architecture described as linear-optical circuits with outputs given by bosonic-mode measurement probabilities (the paper's formal definition/construction). The claim is definitional/theoretical (no empirical sample size).
Because this is a conceptual/systems-architecture paper, it does not present new empirical performance benchmarks.
Explicit statement in the paper's Data & Methods section that no new empirical benchmarks are presented.
The evaluated models consist of an MLP baseline and a GNN tailored to exploit relational/spatial structure among beams/antennas.
Model descriptions provided in the methods section: two supervised-learning architectures (MLP and GNN) used for beam prediction experiments.
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.