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 |
The paper is primarily discursive and invitational: it opens a dialogue and proposes a research agenda rather than providing definitive empirical answers.
Stated methodological stance and limits: conceptual/philosophical analysis, interdisciplinary literature synthesis, qualitative/illustrative examples, and explicit note of no systematic empirical evaluation.
Operators and regulators should prioritize independent model audits, disclosure of data use, fairness/error rates, and field experiments to quantify causal impacts and heterogeneous effects.
Policy recommendations and research priorities summarized in the review based on identified methodological and governance gaps.
Research gaps include the need for robust causal evaluations (RCTs, field experiments), standardized metrics, transparency/interpretability, fairness analysis, and cross‑jurisdictional studies.
Review's recommendations and identified gaps, noting scarcity of RCTs/longitudinal work and calls for standardized outcomes and fairness checks.
Heterogeneous study designs, outcomes, and measures across the literature hinder quantitative meta‑analysis and synthesis of effectiveness.
Review states heterogeneity of designs and outcome measures as a limitation preventing meta‑analysis.
Typical data used in studies are platform behavioural logs (bets, stakes, timestamps, session durations), account metadata, and in some cases limited self‑report measures.
Review summary of data sources across included studies listing platform logs and metadata as primary inputs to algorithms.
Evaluation approaches in the reviewed literature varied widely, with many studies using retrospective accuracy metrics (AUC, precision/recall) rather than causal impact measures on harm reduction.
Methods synthesis in review: prevalence of supervised/unsupervised ML with retrospective performance reporting; few RCTs or field experiments reported.
Four primary application areas were identified: (1) behavioural monitoring and feedback, (2) predictive risk modelling, (3) decision support and AI classifiers, and (4) limit‑setting and self‑exclusion tools.
Thematic synthesis of included studies categorizing described applications into four main areas (review taxonomy).
Searches were performed in Web of Science, PubMed, Scopus, EBSCO and IEEE, plus manual searches, following PRISMA guidelines.
Methods section of the review specifying databases searched and PRISMA-guided review process.
The review included 68 empirical and methodological studies on deep technologies in online gambling.
Systematic review following PRISMA; searches of Web of Science, PubMed, Scopus, EBSCO, IEEE and manual searching produced 68 included studies (count reported in paper).
The collection includes a mix of methodological papers, empirical applications demonstrating ecological insight, and translational work focused on policy or conservation practice.
Study-types categorization provided in the paper (descriptive tally/characterization of the kinds of contributions in the collection).
Methods in the collection span from automated image and signal processing for routine tasks to integrated modelling that couples ecological theory with data‑driven methods.
Methods-scope summary in the paper describing the range of AI/ML approaches used across the collection (descriptive across studies).
The collection uses large ecological observational datasets such as camera‑trap imagery, sensor streams, biodiversity surveys, and other high‑volume ecological monitoring data.
Data & methods section listing the data types represented across the reviewed papers (descriptive inventory of dataset types used in the collection).
Recommendation (research): Future research should link AI adoption to objective performance metrics (profitability, default rates, processing times) and use longitudinal or quasi-experimental designs to identify causal effects.
Authors' suggested research directions noted in the summary, motivated by limitations of cross-sectional, self-reported data.
The summary omits important reporting details: p-values, standard errors, model control variables, and exact variable operationalizations are not provided.
Explicit reporting gap noted in the paper summary (absence of p-values, SEs, controls, and operationalization details).
Because the data are cross-sectional and self-reported, the design limits causal inference about AI adoption causing the observed outcomes.
Study design (cross-sectional survey, self-reported measures) and explicit limitation noted in the paper summary.
Key measures are self-reported Likert scales for AI adoption/usage and the dependent outcomes (financial decision-making efficiency, operational efficiency, financial resilience, and AI-based analytics effectiveness).
Measurement description in Methods: independent and dependent variables reported as self-reported Likert measures collected in the cross-sectional survey.
The study is a cross-sectional quantitative survey of 312 professionals in banks, fintechs, and financial service firms.
Study design and sample description reported in Data & Methods; sample size explicitly given as N = 312 and composition described as professionals across financial institutions, fintech organizations, and financial service companies.
The SKILL.md used in the with-skill condition encodes workflow logic, API patterns, and business rules as portable domain guidance for agents.
Paper description of the with-skill intervention specifying the content and intended role of SKILL.md.
We evaluated open-weight models under two conditions: baseline (generic agent with tool access but no domain guidance) and with-skill (agent augmented with a portable SKILL.md document encoding workflow logic, API patterns, and business rules).
Experimental design in paper describing the two agent conditions; SKILL.md described as the injected domain guidance artifact.
Each scenario is grounded in live mock API servers with seeded production-representative data, MCP tool interfaces, and deterministic evaluation rubrics combining response content checks, tool-call verification, and database state assertions.
Methods/benchmark design described in paper specifying environment: live mock APIs, seeded data, MCP tool interfaces, and deterministic evaluation combining content checks, tool-call verification, and DB assertions.
SKILLS comprises 37 telecom operations scenarios spanning 8 TM Forum Open API domains (TMF620, TMF621, TMF622, TMF628, TMF629, TMF637, TMF639, TMF724).
Framework specification in the paper; explicit statement of scenario count (37) and list of 8 TMF Open API domains.
We introduce SKILLS (Structured Knowledge Injection for LLM-driven Service Lifecycle operations), a benchmark framework for telecom operations.
Paper describes the design and release of the SKILLS benchmark framework as the contribution; methods section outlines framework components and usage.
The paper identifies three core mechanisms underlying calibrated trust and complementarity: (1) calibrated trust balancing reliance and oversight, (2) complementarity–trust interaction for optimal performance, and (3) dynamic feedback loops producing reinforcing learning cycles.
Explicit identification of mechanisms claimed in the paper's synthesis; this is a descriptive claim about the paper's content rather than an empirical finding—no sample or empirical test reported in the abstract.
AI-adopting firms do not increase capital expenditures following adoption.
Firm-level capex analysis showing no significant change in capital expenditures for adopters versus nonadopters post-adoption in the paper's empirical framework.
It remains unclear how developers' general programming and security-specific experience, and the type of AI tool used (free vs. paid), affect the security of the resulting software — motivating this study.
Paper's stated research gap/motivation: the authors identify uncertainty in the literature regarding interactions between developer experience, AI tool tier (free vs. paid), and resulting code security.
Participants were assigned a security-related programming task using either no AI tools, the free version, or the paid version of Gemini.
Experimental design described in the paper: random/conditional assignment of participants into three groups (no AI, free Gemini, paid Gemini) performing the same security-related programming task.
We conducted a quantitative programming study with software developers (n = 159) exploring the impact of Google's AI tool Gemini on code security.
Explicit methodological statement in the paper: a quantitative study with 159 participating software developers assigned to experimental conditions to evaluate Gemini's impact on security-related programming tasks.
The authors surveyed workers and developers on a representative sample of 171 tasks and used language models (LMs) to scale ratings to 10,131 computer-assisted tasks across all U.S. occupations.
Study methodology reported in the paper: surveys of 'workers and developers' on 171 tasks, plus LM-based scaling to 10,131 tasks (coverage claims across U.S. occupations).
SWE-Skills-Bench is available at https://github.com/GeniusHTX/SWE-Skills-Bench.
Repository URL provided in the paper for the benchmark's code/data.
SWE-Skills-Bench provides a testbed for evaluating the design, selection, and deployment of skills in software engineering agents.
Benchmark design pairs skills, repositories, and deterministic verification tests; intended use stated by authors as a testbed for evaluation of skills.
39 of 49 skills yield zero pass-rate improvement.
Empirical evaluation over 49 skills and ~565 task instances reporting that 39 skills produced no improvement in test pass rate when injected.
The authors introduce a deterministic verification framework that maps each task's acceptance criteria to execution-based tests, enabling controlled paired evaluation with and without the skill.
Method: creation of a deterministic verification framework that converts acceptance criteria into executable tests; used to perform paired evaluations (with skill vs. without skill).
SWE-Skills-Bench pairs 49 public SWE skills with authentic GitHub repositories pinned at fixed commits and requirement documents with explicit acceptance criteria, yielding approximately 565 task instances across six SWE subdomains.
Benchmark construction: 49 public skills, repositories pinned to fixed commits, requirement documents with acceptance criteria, producing ~565 task instances spanning six SWE subdomains (as reported by the paper).
The article introduces a novel Bayesian Item Response Theory framework that quantifies human–AI synergy by separately estimating individual ability, collaborative ability, and AI model capability while controlling for task difficulty.
Methodological contribution described in the paper: development and application of a Bayesian Item Response Theory model that includes separate parameters for individual ability, collaborative ability, AI model capability, and task difficulty (method section of the paper).
The Planner is trained via Supervised Fine-Tuning (SFT) to internalize diagnostic capabilities and then aligned with business outcomes (conversion rate) via Reinforcement Learning (RL).
Method description in the paper specifying SFT initialization followed by RL alignment targeting conversion rate (UCVR) as reward signal.
EASP's Offline Data Synthesis stage: a Teacher Agent synthesizes diverse, execution-validated plans by diagnosing the probed environment.
Method description in the paper detailing the Teacher Agent's role in synthesizing execution-validated plans during offline data synthesis.
The Probe-then-Plan mechanism uses a lightweight Retrieval Probe to expose the retrieval snapshot, enabling the Planner to diagnose execution gaps and generate grounded search plans.
Methodological description in the paper: design and implementation of Retrieval Probe and Planner; validated through synthesized data and downstream evaluations (offline and online).
Descriptive statistics, reliability tests, regression analysis, and structural equation modelling (SEM) were employed to analyse the relationships between AI adoption and entrepreneurial outcomes.
Methods section reporting use of descriptive statistics, reliability tests, regression analysis, and SEM to evaluate relationships between AI adoption and measured outcomes.
The study used a quantitative research design and collected data from 350 entrepreneurs and managers of small and medium-sized enterprises (SMEs) who had adopted AI in their business operations.
Methods section of the paper specifying a quantitative design and a sample size of 350 AI-adopting SME entrepreneurs/managers.
The study used portfolio-level analysis to compare the financial outcomes of portfolios constructed using AI-driven ESG indicators with those based on conventional ESG ratings.
Methodological statement in the paper: portfolio-level analysis and comparative design. The summary does not specify the number of portfolios, asset universes, time frame, or construction rules.
A quantitative methodology was employed, utilizing a structured questionnaire administered to 400 small business owners.
Explicit methodological statement in the paper: structured questionnaire survey with sample size N=400 small business owners.
The study uses a game-theoretic model involving a foundation model provider and two competing downstream firms to analyze how policy interventions affect consumer surplus in the AI supply chain.
Methodological description in the paper: a formal game-theoretic model with one upstream provider and two downstream competing firms; equilibrium analysis and comparative statics are performed on model outcomes (prices, qualities, profits, consumer surplus).
Foi realizada etnografia organizacional orientada ao SCF, com roteiro e triangulação de evidências.
Método qualitativo divulgado no resumo: etnografia organizacional com roteiro e triangulação; o resumo não fornece número de organizações, duração ou amostragem.
Foi construído e validado um instrumento psicométrico (escala SCF-30) e calculado um índice 0–100, com modelagem por Equações Estruturais (SEM) e testes de confiabilidade/validade.
Descrição metodológica explícita no resumo: construção e validação da escala SCF-30, uso de SEM e testes de confiabilidade e validade. O resumo não detalha estatísticas, amostra ou resultados numéricos.
O SCF é operacionalizado por três vetores centrais: Percepção de Complexidade (PC), Aversão ao Risco Institucional (AR) e Inércia Cultural (IC).
Estrutura conceitual e operacional apresentada no artigo; especificação explícita dos três vetores como componentes do construto SCF.
This research conducts a critical analysis of the ethical implications of artificial intelligence in terms of job displacement during the fifth industrial revolution.
Author-declared methodology: a literature-based critical analysis drawing on novel studies and the existing body of literature; no further methodological details (e.g., inclusion criteria, databases searched) provided in the excerpt.
This study uses panel data on agricultural firms listed on the Shanghai and Shenzhen A-share markets from 2007 to 2023 and applies a multidimensional fixed-effects model to estimate the impact of AI on firms’ total factor productivity (TFP).
Methodological statement in the paper: dataset = panel of listed agricultural firms (Shanghai and Shenzhen A-share markets), time period 2007–2023; empirical approach = multidimensional fixed-effects model.
Degree, betweenness, and eigenvector centrality metrics were used to identify structural vulnerabilities and leverage points in the construction supply chain network.
Paper reports calculation of degree, betweenness, and eigenvector centrality to outline vulnerabilities; specific metrics and interpretations are reported (e.g., degree centrality value for brokers).
Thematic coding translated reported interactions into nodes and edges of a complex network and grouped challenges into thematic categories.
Methods described: thematic coding applied to interview data to create network structure and to generate challenge categories (six main categories, 16 open codes reported).
This study combines empirical, semi-structured interviews with social network analytics to map construction supply chain relationships and vulnerabilities.
Methods reported in the paper: use of semi-structured interviews plus social network analysis (thematic coding to create nodes/edges, calculation of network metrics). Sample size not specified in the abstract.