Evidence (8974 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).
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Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
10085 claims
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Productivity
8974 claims
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Governance
8062 claims
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Human-AI Collaboration
7749 claims
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Org Design
5057 claims
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Innovation
4896 claims
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Labor Markets
4088 claims
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Skills & Training
3372 claims
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Inequality
2377 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 | 882 | 244 | 117 | 1097 | 2424 |
| Governance & Regulation | 1010 | 469 | 229 | 135 | 1875 |
| Organizational Efficiency | 977 | 235 | 149 | 90 | 1462 |
| Technology Adoption Rate | 781 | 299 | 143 | 128 | 1362 |
| Research Productivity | 506 | 155 | 74 | 363 | 1110 |
| Output Quality | 555 | 219 | 71 | 70 | 915 |
| Decision Quality | 395 | 200 | 95 | 54 | 751 |
| Firm Productivity | 523 | 67 | 101 | 27 | 724 |
| AI Safety & Ethics | 262 | 309 | 75 | 36 | 688 |
| Market Structure | 195 | 201 | 135 | 30 | 566 |
| Task Allocation | 248 | 77 | 96 | 38 | 464 |
| Innovation Output | 300 | 34 | 55 | 20 | 411 |
| Skill Acquisition | 207 | 75 | 65 | 21 | 368 |
| Employment Level | 138 | 67 | 119 | 24 | 350 |
| Fiscal & Macroeconomic | 156 | 80 | 53 | 33 | 329 |
| Task Completion Time | 211 | 38 | 13 | 16 | 280 |
| Firm Revenue | 183 | 52 | 29 | 5 | 270 |
| Consumer Welfare | 131 | 77 | 48 | 13 | 269 |
| Inequality Measures | 50 | 141 | 54 | 9 | 254 |
| Worker Satisfaction | 104 | 85 | 25 | 13 | 227 |
| Error Rate | 87 | 112 | 11 | 5 | 215 |
| Automation Exposure | 69 | 69 | 37 | 20 | 198 |
| Wages & Compensation | 102 | 49 | 31 | 11 | 193 |
| Team Performance | 115 | 30 | 30 | 11 | 187 |
| Regulatory Compliance | 88 | 74 | 17 | 7 | 186 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 116 | 21 | 15 | 8 | 161 |
| Job Displacement | 12 | 92 | 26 | 1 | 131 |
| Hiring & Recruitment | 57 | 12 | 9 | 5 | 83 |
| Skill Obsolescence | 6 | 59 | 10 | 2 | 77 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 23 | 17 | 1 | 59 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Productivity
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This paper investigates societal applications of AI across domains such as healthcare, education, accessibility, environmental management, emergency response, and civic administration.
Descriptive statement of the paper's scope and methods (literature review / cross-domain analysis implied); the abstract lists the domains but does not specify empirical procedures or sample sizes.
Chatbot suggestions were artificially varied in aggregate accuracy across treatment conditions from low (53%) to high (100%).
Paper describes experimental manipulation of chatbot suggestion accuracy with aggregate accuracies ranging from 53% to 100%; manipulation method (how suggestions were generated or sampled) described in methods (not fully detailed in excerpt).
Caseworkers in the control condition (no chatbot suggestions) had a mean accuracy of 49%.
Reported experimental outcome: mean accuracy for control group = 49%; based on the randomized experiment using the 770-question benchmark.
We conducted a randomized experiment with caseworkers recruited from nonprofit outreach organizations in Los Angeles.
Paper describes a randomized experiment recruiting caseworkers from nonprofit outreach organizations in Los Angeles; sample size and recruitment details not given in the excerpt.
The benchmark questions have corresponding expert-verified answers.
Paper states benchmark questions have expert-verified answers; verification method and number/credentials of experts not specified in the excerpt.
We created a 770-question multiple-choice benchmark dataset of difficult, but realistic questions that a caseworker might receive.
Paper reports creation of a benchmark dataset containing 770 multiple-choice questions described as difficult and realistic; questions and dataset construction described in methods (no sample-of-questions or external validation details provided in the excerpt).
The study's conclusions draw on three complementary evidence bases: (a) task-level evidence on what generative AI can already do in practice; (b) occupational exposure and complementarity analysis using Philippine labor force data; and (c) firm- and worker-level evidence on AI adoption.
Description of methods and data sources in the paper: task-level capability testing/assessment, analysis of national labor force/occupation data for exposure/complementarity, and firm/worker surveys or qualitative adoption evidence.
Extensive experiments were conducted using both synthetic and real hospital datasets to evaluate the framework.
Statement in the paper indicating experiments on synthetic and real datasets; exact sizes, sources, and composition of these datasets are not provided in the excerpt.
The paper explains the main legal frameworks that currently regulate AI in India, as well as proposals for future legislation.
Author's legal and policy analysis / document review of existing statutes and proposed laws (qualitative review). No quantitative sample size; based on review of legal texts and policy proposals cited in the article.
The machine-learning based analytical approach used in the study captures complex, nonlinear relationships among emotional, psychological and economic variables.
Methodological claim: authors used machine learning (including ensembles) to model nonlinear and complex relationships. The excerpt does not provide algorithmic details, tuning, validation strategy, or sample size.
Work environment and digital/AI intensity were incorporated as contextual moderators in the analysis to reflect contemporary labor market conditions.
Methodological description in the excerpt states these variables were included as moderators; no details on measurement, operationalization, or sample size are provided.
Most evidence came from retrospective studies or meta-analyses, with limited prospective or randomized controlled trials.
Summary of study designs across the 40 included studies as reported in the review.
The impact of AI on patient outcomes (e.g., mortality, rebleeding) was rarely addressed.
Statement in results indicating few included studies reported patient-centered outcomes such as mortality or rebleeding.
This systematic review adhered to PRISMA 2020 guidelines.
Methods statement in the paper specifying adherence to PRISMA 2020; the review included 40 studies.
The review focuses on AI applications within small‑scale business environments, with a special focus on women‑owned micro firms in Jaipur, India.
Scope and aim articulated in the paper; geographic and demographic focus explicitly stated by the authors.
The systematic review follows PRISMA 2020 guidelines.
Methodological statement in the paper indicating adherence to PRISMA 2020 for the review process.
After screening and eligibility filtering, 55 open‑access journal articles were included for in‑depth analysis.
PRISMA‑guided screening and eligibility process reported in the review; final included sample explicitly stated as 55 open‑access journal articles.
A Scopus search identified 265 records using keywords related to women’s entrepreneurship and AI.
Systematic literature search reported in the paper following PRISMA 2020; search executed in Scopus with specified keywords; initial yield stated as 265 records.
This study draws on a critical AI media literacy framework to analyze user-generated discussions in the two largest higher education subreddits on Reddit.com.
Author-reported study design: application of a critical AI media literacy theoretical framework to a qualitative dataset consisting of user-generated discussions from the two largest higher-education subreddits. (Sample size/number of posts/threads not specified in the provided excerpt.)
The study used a mixed-methods design incorporating surveys from 150 LEP immigrants, interviews with 50 employers, and interviews with 20 translation service providers in various linguistically diverse U.S. cities, with quantitative analysis performed in SPSS Version 28 and qualitative thematic coding in NVivo 14.
Reported study design and sample: survey n=150 LEP immigrants; employer interviews n=50; translation provider interviews n=20; analytic software specified as SPSS v28 (quantitative) and NVivo 14 (qualitative).
The framework was evaluated on 2,847 queries across 15 task categories.
Paper reports an evaluation dataset consisting of 2,847 queries spanning 15 task categories; used as the sample for reported empirical results.
Non-text processing paths use SLM-assisted modality decomposition.
Paper reports that non-text queries are decomposed using SLM-assisted modality decomposition; described as the non-text routing approach in the framework.
For text-only queries, the framework uses learned routing via RouteLLM.
Paper states text-only routing is handled by a learned model named RouteLLM; presented as part of the system architecture.
A central Supervisor dynamically decomposes user queries, delegates subtasks to modality-appropriate tools (e.g., object detection, OCR, speech transcription), and synthesizes results through adaptive routing strategies rather than predetermined decision trees.
Methodological description in the paper of a Supervisor component that performs dynamic decomposition, delegation to modality-appropriate tools (examples given), and adaptive routing; supported by the framework's implementation details.
We present an agentic AI framework for autonomous multimodal query processing that coordinates specialized tools across text, image, audio, video, and document modalities.
Paper describes the framework design and components (Supervisor, modality-specific tools) and states support for text, image, audio, video, and document modalities; no external benchmark cited for this capability beyond the paper's own implementation.
The study employs an input–output (I–O) modeling framework using IMPLAN 2022 data to estimate direct, indirect, and induced impacts of investments in greenhouse and robotics sectors for Northwest Indiana as part of Project TRAVERSE.
Explicit methodological statement in the paper: use of IMPLAN 2022 I–O model; geographic scope NWI; linkage to EDA Project TRAVERSE.
The essay reviews seven books from the past dozen years by social scientists examining the economic impact of artificial intelligence (AI).
Qualitative book-review performed by the author; sample size explicitly stated as seven books published within the last ~12 years; method = synthesis/assessment of those seven books.
This systematic review follows PRISMA guidelines to examine the evolution, advancements, and state-of-the-art AI applications for GS-BESS optimization.
Methodological statement in the paper indicating the use of PRISMA guidelines for the review process. The excerpt does not include the PRISMA flow diagram or the exact article selection numbers.
The experimental sample underlying the statistical tests comprised 20 observations (implied by ANOVA degrees of freedom: df between = 1, df within = 18).
Interpretation of the reported one-way ANOVA degrees of freedom (F(1,18) for multiple outcomes) indicating total N = 20 observations.
Field experiments at the Al‐Ra'id Research Station in Baghdad during the 2025 season compared conventional diesel‐based irrigation with AI‐assisted irrigation using soil moisture sensors, IoT controllers, and predictive weather algorithms.
Reported field experiment design in the paper (Al‐Ra'id Research Station, Baghdad, 2025 season) specifying two treatments: conventional diesel irrigation vs AI-assisted irrigation using soil moisture sensors, IoT controllers, and predictive weather algorithms.
Definitions and scopes of Material Passports vary among authors.
Content analysis of the 46 included studies showing differing definitions and scope treatments for MPs reported by the authors.
Among the included studies, 65% focused primarily on Material Passports (MPs), while 35% addressed MPs within the broader context of a circular economy (CE).
Quantitative categorization of the 46 included studies reported in the paper (percentages attributed to focus areas).
A total of 54 peer-reviewed articles and book chapters were screened from the Scopus database, of which 46 were included for in-depth analysis in April 2025.
Reported screening and inclusion counts from the Scopus search (54 screened, 46 included); date of in-depth analysis given as April 2025.
This article presents a Systematic Literature Review (SLR) following the PRISMA methodology.
Stated methodology in the paper: SLR using PRISMA; literature search performed in Scopus; review process and inclusion/exclusion described (screening and inclusion counts reported).
Future research could strengthen causal identification by exploiting exogenous policy shocks rather than relying solely on matching methods like PSM.
Authors' methodological suggestion for future work, based on limitations of current causal inference strategy (PSM and observational panel regression).
Propensity Score Matching (PSM) and other robustness checks were used to mitigate selection bias and support the causal interpretation of AI's effects.
Paper reports use of Propensity Score Matching in robustness analyses on the panel of A-share-listed design firms (2014–2023).
The paper operationalizes firm-level AI exposure by constructing an AI lexicon via natural language processing and applying text analysis to annual reports and patents to generate enterprise-level AI indicators.
Described methodology: NLP to generate an AI lexicon and text-analysis of annual reports and patents to build AI measures for each listed design enterprise in the 2014–2023 panel.
The paper presents relevant tradeoffs and design choices across human-LLM archetypes, including decision control, social hierarchies, cognitive forcing strategies, and information requirements.
Qualitative analysis and discussion in the paper synthesizing insights from the literature review and empirical evaluation. Method: thematic synthesis and design analysis. Sample size: based on the review of 113 papers and the clinical-case evaluation (details in full text).
We describe 17 human-LLM archetypes derived from a scoping literature review and thematic analysis of 113 LLM-supported decision-making papers.
Scoping literature review and thematic analysis method; corpus size = 113 LLM-supported decision-making papers (as reported in the paper).
The paper introduces the concept of human-LLM archetypes, defined as re-occurring socio-technical interaction patterns that structure the roles of humans and LLMs in collaborative decision-making.
Conceptual contribution presented in the paper (definition and framing). Method: theoretical/conceptual description in the manuscript. Sample size: not applicable.
Battery and motor performance were evaluated (in laboratory tests).
Laboratory tests assessing battery and motor performance are reported in the methods/results; no quantitative battery/motor metrics provided in the summary.
A composite index capturing concerns about mental health, privacy, climate impact, and labor market disruption was constructed to measure societal risk perceptions of AI.
Author-constructed composite index derived from survey items on mental health, privacy, climate, and labor market disruption concerns in the 2023–2024 UK survey.
The study treats AI-agent populations as a system in which four key variables governing collective behaviour can be independently toggled: nature (innate LLM diversity), nurture (individual reinforcement learning), culture (emergent tribe formation), and resource scarcity.
Study design described in the paper (experimental setup allowing independent manipulation of the four variables: model diversity, individual RL, emergent tribe formation, and resource scarcity).
The analysis is framed through the integrated lens of the Technology-Organization-Environment (TOE) framework and Institutional Theory to provide a multi-faceted understanding of adoption dynamics.
Stated theoretical framing and analytical approach in the study (methodological claim).
The research synthesizes evidence from a wide array of sources, including recent academic literature by Nigerian scholars, NPA official performance reports, policy documents, and international trade facilitation reports (e.g., UNCTAD).
Explicit description of data sources in the study methodology; method: secondary data synthesis (no sample size applicable).
This study investigates the current state of adoption, the prevailing barriers, and the resultant performance outcomes of digital and AI-driven logistics within Nigeria’s maritime supply chain.
Stated study aim and scope; method: rigorous secondary data analysis drawing on multiple documentary sources (Nigerian academic literature, NPA reports, policy documents, UNCTAD).
This study uses a conceptual and analytical approach to examine the impact of AI and automation on work.
Stated methodology in the paper's abstract/introduction: methodological description that the study is conceptual and analytical; no empirical sample or quantitative data reported.
The study uses a recently developed firm-year measure of investment in AI-related human capital, applied to a broad sample of U.S. nontechnology firms between 2010 and 2018.
Methodological statement in the abstract describing the independent variable and the sample years and population (U.S. nontechnology firms, 2010–2018).
The paper's findings are based on a combination of literature review, data analysis, and an empirical study involving HR professionals.
Methodological description given in the paper's summary (no further methodological details, sample size, instruments, or statistical methods provided in the summary).
The adoption and implementation of AI in entrepreneurial firms is an under-studied area of research.
Paper's literature review and motivation statement asserting limited empirical research on AI adoption in entrepreneurial contexts.