Evidence (8807 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).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
Filter claims →
Productivity
8807 claims
Filtered →
Governance
7870 claims
Filter claims →
Human-AI Collaboration
7560 claims
Filter claims →
Org Design
4892 claims
Filter claims →
Innovation
4781 claims
Filter claims →
Labor Markets
4004 claims
Filter claims →
Skills & Training
3308 claims
Filter claims →
Inequality
2332 claims
Filter claims →
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 |
Productivity
Remove filter
The causal effect of adoption on architectural smell density (ASD) was estimated using a staggered difference-in-differences design and the Borusyak imputation estimator.
Methodological claim describing the causal identification and estimation strategy applied to the 151-repository panel.
We mined 151 open-source Java repositories, 74 with detectable agentic AI adoption (identified via configuration files and Co-Authored-By commit trailers) and 77 propensity-matched controls, across a 13-month per-repository window yielding 1,811 monthly Arcan snapshots.
Descriptive dataset and methods statement in paper: 151 repositories (74 treated, 77 matched controls), 13-month windows, producing 1,811 monthly Arcan snapshots.
Causal evidence on the effect of AI coding tool adoption on software architecture is scarce; prior causal work has focused on code-level outcomes (complexity, static analysis warnings) and whether such degradation propagates to architecture-level outcomes remains unknown.
Literature/background statement in abstract asserting gaps in prior work; not an empirical result from this paper's dataset.
The superior zero-shot forecasting accuracy of foundation models does not inherently translate into better decision utility for resource consolidation.
Empirical analysis in the paper mapping forecasting outputs to downstream consolidation decisions and utility metrics, showing lack of improvement in decision utility despite better forecast accuracy.
We analyze 15,549 agentic PRs from 148 projects in the AIDev dataset.
Descriptive statement of dataset and sample used in the study (paper reports analysis of 15,549 agentic PRs from 148 projects).
From 2024 to 2026, more than 130 articles were submitted to this Special Issue (SI), and only 18 papers were accepted after rigorous peer review.
Editorial report in the paper describing CFP submissions and acceptance counts.
We conduct a qualitative study on a representative sample of 306 non-merged pull requests created or co-authored by the agents mentioned earlier, followed by a quantitative analysis of the reasons for rejection.
Authors' reported methods: qualitative study of a sample of 306 non-merged PRs and subsequent quantitative analysis.
Professional radiologists analyzed chest X-rays with access to state-of-the-art machine learning predictions in this replication setting.
Description of the experimental/contextual setting in the paper: professional radiologists using ML predictions on chest X-rays drawn from Collab-CXR.
The replication uses radiologist assessments from repeated-case designs, which include 68 radiologists and 11,420 paired radiologist–patient–pathology observations.
Direct reporting of study sample and design in the paper (repeated-case design; counts of radiologists and paired observations).
This note leverages the public Collab-CXR data repository described by Moehring et al. (2025) and first analyzed for human-AI collaboration by Agarwal et al. (2023).
Explicit statement in the paper identifying the data source and prior analyses (references provided).
In a production switchback experiment, the offline-trained policy reduces courier-side time costs without degrading customer-facing delivery quality.
Empirical claim supported by production switchback experiment described in the paper; asserts no degradation in customer-facing delivery quality concurrent with courier-side time improvements (no numerical metrics or sample sizes provided in excerpt).
The study uses a qualitative, mixed-methods design combining a systematic literature review, secondary evidence from an industry MRO digital survey, five semi-structured expert interviews, and two technical case studies (neural networks for aircraft retirement and an AI-based digital twin for a Power Electronics Cooling System).
Methods description provided in the paper (explicit counts: 5 interviews, 2 case studies); method = author-reported study design.
After screening, 35 studies were included in the thematic synthesis and supplemented by official regulatory and industry documents.
Review screening result reported in the paper: number of included studies = 35; supplementation by regulatory and industry documents stated.
A structured search protocol was designed for Scopus, Web of Science, PubMed, IEEE Xplore, and Google Scholar covering January 2016 to May 2026, English-language records only.
Methods statement in the review describing the databases, date range, and language restriction used for the systematic search.
The implementation literature on AI for pharmacy inventory and pharmaceutical supply chains remains dispersed across pharmacy operations, operations research, health informatics, and supply chain analytics.
The review's thematic synthesis of the searched literature (review methods described below) identified studies across these disciplinary areas.
Devil's Advocate (DA) is an AI assistant that critiques the human's initial ideas, whereas Dialectical Inquiry (DI) provides alternatives and synthesizes a resolution.
Conceptual/definitional claim in the paper describing the operationalization of DA and DI for the experiments.
This research empirically compares DA and DI in AI contexts.
Paper reports experimental comparison between AI behaviors implementing Devil's Advocate (DA) and Dialectical Inquiry (DI) across the studies.
Both studies examine benefit (information elaboration) and cost (cognitive load) pathways when AI supports SDM.
Paper explicitly frames both studies to measure information elaboration as a benefit pathway and cognitive load as a cost pathway; stated measurement plan in methods.
Study 2 tests mind-shaping interventions through user strategy training.
Study design described in the paper: a second experiment (Study 2) manipulating user strategy training (mind-shaping) to evaluate effects on SDM processes and outcomes.
Study 1 tests tool-shaping interventions by comparing three AI bot prototype conditions (Information-only, DA, DI) against a control treatment.
Study design described in the paper: randomized/controlled experiment (Study 1) with four conditions (three AI prototype conditions plus control).
The 'do no harm' property is confirmed empirically.
Abstract states empirical confirmation in simulations and applications; specifics (e.g., datasets, sample sizes) not included in abstract.
Including AI predictions as covariates has a 'do no harm' property: the adjusted estimator reverts to the unadjusted difference in means when predictions are uninformative.
Stated theoretical property in the paper and described as empirically confirmed in simulations and applications (per abstract).
Raw blind-panel decision quality is similar for A and B (7.01 vs. 6.96).
Blind-panel scoring of generated reports from agents A and B; panel size and panel methodology not specified in abstract.
Self-evaluated creative performance remained unchanged when using GenAI.
Same experiment with 82 participants; authors report no significant difference in self-evaluated creative performance between GenAI users and controls.
Each of the four published papers used in the experiments contained an error that I helped identify or correct.
Author statement that the 4 papers each contained an error; author involvement in identification/correction is asserted.
I conducted experiments in which I asked several AI models (Gemini, Refine, Claude, and ChatGPT) to check the correctness of four published papers in economic theory.
Author reports running direct experiments: prompted listed models to check 4 published economic-theory papers.
We evaluate the system on operator feedback and a question set collected from production usage, graded by human and automated panels.
Paper's stated evaluation methodology: operator feedback + production question set, graded by humans and automated panels.
This study benchmarks Algeria’s readiness to adopt AI against Morocco, Egypt, and Turkey using data from the World Bank (2022), the Oxford Insights Government AI Readiness Index, and sector-specific studies.
Methodological statement in the paper specifying data sources used for the comparative assessment (World Bank 2022, Oxford Insights index, sector studies).
The article aims to provide systematic literature support for subsequent research and adaptive policy formulation.
Statement of the paper's stated objective; methodological and policy-intent claim from the authors.
This article is based on a systematic literature review and summarizes the four core theoretical mechanisms of substitution, complementarity, new task creation, and skill mismatch.
Methodological claim from the paper: the authors conducted a systematic literature review and identified these four theoretical mechanisms.
Traditional software and agentic systems are distinct: in traditional software code is the carrier of decision logic, whereas in agentic systems code is ephemeral tooling used by an LLM-driven reasoning loop.
Formalization and conceptual definitions developed in the paper (first-principles formal distinction; no empirical sample size reported).
For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into static code, and manually adapt that code as requirements evolve.
Historical/descriptive claim presented in the paper's framing and literature review; citation of longstanding software engineering practices (qualitative, no empirical sample size reported).
We implement a two-stage processing architecture separating document-level extraction (Stage 1) from claim-level synthesis (Stage 2).
Implementation description in paper: architecture design and pipeline stages described by the authors.
In neither unit did internal control mechanisms identify any information-security incident, sensitive-data leakage, or formal compliance challenge from external oversight bodies during the period examined.
Author reports absence of recorded incidents in internal control mechanisms and no external oversight challenges for both units over the study period; based on internal records and SEI-GDF auditable indicators.
The research is grounded in the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT) to explain how technological and managerial resources contribute to organizational performance.
Author statement in the paper describing the theoretical framework (RBV and DCT) used to frame the study.
The study adopts a quantitative research design and analyzes collected data using Partial Least Squares Structural Equation Modeling (PLS-SEM).
Author statement in the paper describing research design and analytical method.
Digital Leadership did not demonstrate a statistically significant direct effect on Employee Productivity (β = -0.094, p = 0.275).
Reported quantitative result from the study using PLS-SEM; β and p-value provided in the paper showing a non-significant direct effect. Sample size not reported in the excerpt.
We scored over 2.1 million twin responses on 500 participants and 183 held-out questions.
Reported evaluation counts in the paper: 2.1M responses, 500 participants, 183 held-out questions.
The construction-method grid covers three open-weight LLMs, five cumulative information depths ranked by normalized Shannon entropy, two embedding methods, and two reasoning modes.
Paper's experimental design specification (methods section).
We construct detailed individual-level twins from the German Socio-Economic Panel (SOEP) and evaluate them across a 3 × 5 × 2 × 2 construction-method grid.
Methodological description of the study: experimental construction and evaluation on SOEP data.
A large-scale empirical study on Harvey LAB used 12,510 agent trajectories.
Paper states an empirical study run on Harvey LAB with a sample described as 12,510 agent trajectories.
The paper analyzes multiple dimensions of scientific creativity and impact, specifically recombinant novelty, object novelty, 3-year short-run citation impact, and 10-year long-run citation impact.
Methodological description in paper listing the specific dependent variables and time horizons used to measure novelty and impact.
The analysis draws on over one million publications from OpenAlex.
Descriptive statement in paper specifying dataset source (OpenAlex) and sample size of publications used for analysis.
Experts rated 24 AI risks on harm probability and severity, sector and actor vulnerability, actor responsibility, and overall concern.
Study design described in paper: set of 24 defined AI risks rated across several dimensions by Delphi panel participants (n=272).
We conducted a three-round Delphi study conducted late 2025 with 272 international AI experts.
Methodological description in the paper: three-round Delphi study, timing reported as late 2025, sample size reported as 272 international AI experts.
Total (aggregate) unemployment is statistically insignificant in explaining sustainable development, indicating aggregate measures mask critical distributional differences across skill groups.
ARDL estimation results reported in the paper showing an insignificant coefficient for total unemployment; discussion emphasizing distributional masking.
The empirical analysis is based on panel data of new energy vehicle firms in the Yangtze River Delta from 2001 to 2023.
Dataset description provided in the paper's abstract/introduction indicating the time span and regional coverage.
R&D expenditure does not constitute a significant mediating channel between artificial intelligence and firms' new quality productive forces.
Mediation analysis using the panel data and constructed indicators; reported nonsignificant mediation effect of R&D expenditure (no sample size or statistics reported in excerpt).
The system was evaluated on OMH-Polyglot, a multilingual coding benchmark spanning Turkish, Arabic, Chinese, and code-switched specifications.
Experimental evaluation reported in the paper using the OMH-Polyglot benchmark.
The study developed a manufacturing value chain resilience (MVCR) index system based on three dimensions: Readiness, Response, and Recovery, using the CSMAR database.
Methodological description: construction of MVCR index using CSMAR microdata and a three-dimension framework (Readiness, Response, Recovery).