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).
Browse by theme
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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A critical narrative literature review of 30 sources (2019–2026) was conducted.
Self-reported study method: critical narrative literature review; sample_size = 30 sources published between 2019 and 2026.
The experiment used stratified randomization across 32 strata with 255 treatment firms and 260 control firms; baseline characteristics are well balanced across groups.
Experimental design description: stratification by geography, traction score, and baseline AI use; reporting of allocation counts and balance tests in Table 2.
Attrition from the accelerator was low (1.6%, eight ventures) and balanced across treatment and control.
Program enrollment and retention records for the 515 firms in the randomized accelerator; 8 firms attrited.
The gains from treatment are broad-based: there are no significant differential effects by baseline firm performance or founder technical background.
Heterogeneity/subgroup analyses in the randomized sample (515 firms) comparing treatment effects across strata defined by baseline traction and founder technical background.
Treated firms' demand for labor remains unchanged.
RCT with 515 firms; firms reported labor demand/changes, comparison between treatment and control groups showed no significant change.
AIGC and HGC exhibit distinct creation behaviors and consumption behaviors.
Descriptive comparisons in the longitudinal dataset showing differences in production rates, content volumes, and consumption patterns between AIGC and HGC.
The paper uses a comprehensive longitudinal dataset comprising tens of millions of users from a leading Chinese video-sharing platform.
Statement in paper summarizing data source: a longitudinal dataset covering 'tens of millions of users' from a major Chinese video-sharing platform; used for descriptive and comparative analyses of creation and consumption behavior.
These chats were committed to public repositories as part of routine development, capturing in-the-wild behavior.
Data collection method: analysis of chat transcripts that were committed to public repositories (authors state collected from repos and describe them as routine commits).
We analyze 74,998 developer messages from 11,579 chat sessions across 1,300 repositories and 899 developers using Cursor and GitHub Copilot.
Reported dataset counts in the paper (message, session, repository, developer counts) drawn from public commit histories of chats.
Conventional microeconomic models often treat interactions between algorithmic platforms and workers as static principal-agent problems.
Literature statement in paper (conceptual framing / literature review); no empirical sample reported.
The study sample comprises 21,428 firm-year observations from Chinese A-share listed manufacturing companies over 2010–2022.
Data description provided in the paper's abstract/introduction specifying the sample frame and time period.
This paper employs a staggered difference-in-differences (DID) model using data from Chinese A-share listed manufacturing companies from 2012 to 2023 and uses the National Artificial Intelligence Innovative Application Pioneer Zone (AIIAPZ) policy as a quasi-natural experiment.
Staggered DID empirical design; sample described as Chinese A-share listed manufacturing firms, 2012–2023; AIIAPZ policy used as treatment assignment (quasi-natural experiment).
This study uses semi-structured interviews with 10 practitioners to examine perceptions of collaborating with human versus AI teammates.
Methods statement in the paper: semi-structured interviews; sample size explicitly reported as 10 practitioners.
The study is based on a qualitative analysis of recent academic literature, comparative analysis of sector-specific applications of Big Data technologies, and synthesis of empirical findings from international studies using a systemic and structural analysis approach.
Methodological statement within the paper describing data sources and analytic approach; not an empirical claim about outcomes.
Society 5.0 and Industry 5.0 call for human-centric technology integration, but the concept lacks an operational definition that can be measured, optimized, or evaluated at the firm level.
Motivating claim grounded in literature gap analysis presented in the paper (argument that normative frameworks lack formal, operational metrics at firm level).
We propose the Workplace Augmentation Design Index (WADI), a 36-item theory-grounded instrument for diagnosing human-centricity at the firm level.
Instrument design/proposal presented in the paper (36 items mapped to the five workplace-design dimensions); no validation sample reported in the abstract.
We conducted a PRISMA-guided systematic review of 120 papers (screened from 6,096 records) to map the evidence base for each workplace-design dimension.
Systematic literature review using PRISMA protocol; final sample = 120 papers; initial records screened = 6,096.
Existing models of human-AI complementarity treat the augmentation function phi(D) as exogenous and thus ignore that two firms with identical technology investments can achieve radically different augmentation outcomes depending on workplace organization.
Argument based on literature review of prior models (the paper contrasts its approach with existing complementarity models). No new empirical sample reported for this specific claim.
A subset of four datasets included settings in which the AI provided explanations of its decision.
Paper states that four of the datasets involved AI explanations (explicitly stated in abstract).
The study compared HCT to the AI-as-advisor approach using 10 datasets from various domains, including medical diagnostics and misinformation discernment.
Paper reports an empirical comparison across 10 datasets spanning multiple domains (explicitly stated in abstract).
The hybrid confirmation tree (HCT) elicits a human judgment and an AI judgment independently; if they agree that decision is accepted, and if they disagree a second human breaks the tie.
Description of the HCT method in the paper (procedural/design specification).
We implement a rigorously controlled execution-based testbed featuring Git worktree isolation and explicit global memory to evaluate agent coordination frameworks.
Methodological description in the paper indicating the testbed design choices (Git worktree isolation, explicit global memory) used to ensure controlled, reproducible execution of agent-generated code.
We benchmark a single-agent baseline against two multi-agent paradigms: a subagent architecture (parallel exploration with post-hoc consolidation) and an agent team architecture (experts with pre-execution handoffs) using a rigorously controlled, execution-based testbed.
Description of experimental setup in the paper: an execution-based testbed with Git worktree isolation and explicit global memory; experiments explicitly compare single-agent, subagent, and agent-team architectures under fixed computational time budgets.
Methods combine targeted literature synthesis, comparative conceptual analysis, and framework building (with recent scholarly and institutional sources reviewed).
Explicit methodological statement in the paper describing the review and analytic approach; no primary-data methods used.
AI coding assistants are a high-visibility class of corporate AI and are given special attention as an illustrative case in the paper.
Paper specifically calls out AI coding assistants as a focal example in the conceptual analysis and discussion; based on literature review rather than original measurement.
The Article translates these insights into risk-sensitive guideposts for modernizing governance of AI-enabled tools and emerging modalities, from agentic systems to blockchain-deployed smart contracts.
Prescriptive/conceptual policy guidance presented in the Article (normative recommendations; governance framework).
The Innovation Frontier traces LegalTech’s evolution from 2000s-vintage e-discovery to generative AI.
Historical/chronological analysis in the Article (literature review/history of LegalTech provided by authors).
The Legal Services Value Chain disaggregates the lifecycle of a legal matter into five distinct nodes of activity.
Model description in the Article (conceptual architecture; decomposition of legal work).
The Article develops two core organizing models: the Legal Services Value Chain and the Innovation Frontier.
Explicit claim in the Article describing conceptual/model contributions (theoretical/model-building).
This Article provides a practical framework for navigating the shifting terrain of legal innovation and AI.
Statement of purpose in the Article (conceptual contribution; framework development). No empirical validation reported in the excerpt.
Three interlocking threads characterize AI for science: (1) AI as research instrument, (2) AI for research infrastructure, and (3) the reshaping of scholarly profiles and incentives by machine-readable metrics.
Conceptual framework presented in the paper; organization of topics rather than empirical measurement. The paper indicates these threads are followed through historical and contemporary examples.
The history of artificial intelligence for scientific discovery is not a two year story about chatbots learning to write papers; it is a sixty year story beginning with DENDRAL (1965).
Historical narrative / literature review citing early systems such as DENDRAL (1965) and subsequent developments in scholarly infrastructure (arXiv, Google Scholar, ORCID). No empirical sample or statistical test reported.
Both the positive (approach) and negative (avoidance) AI job crafting pathways failed to significantly affect life satisfaction, indicating domain specificity of AI-related psychological mechanisms.
Analysis of the same multi-source, multi-wave dataset of 287 employee–leader dyads; tests of effects on life satisfaction showed non-significant results for both pathways.
For readers less familiar with the Bayesian and decision-theoretic language, key terms are defined in a glossary at the end of the article.
Statement about the article's structure and supporting material (presence of glossary noted in the article).
The gap between a continuously updated belief state and your frozen deployed model is 'learning debt.'
Terminology/definition introduced by the author in the article (glossary and definitional exposition).
Model retraining is usually treated as an ongoing maintenance task.
Author's descriptive claim in the article; presented as an observation about prevailing practice (no empirical sample or data reported).
The study was conducted by the Mohammed bin Rashid School of Government’s Future of Government Center, in collaboration with global AI pioneers.
Authorship and collaboration statement in the report.
The report highlights the key findings of a field study covering ten Arab countries to explore the realities and challenges of AI governance.
Report statement describing the geographic scope of the field study (explicitly: ten Arab countries).
The recommendations are based on regional research that included hundreds of leaders active in the AI domains, from the public and private sectors.
Report statement claiming participant base of the underlying research (described as 'hundreds of leaders').
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).
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 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.
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).
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.
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.
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.