Evidence (5539 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 |
Adoption
Remove filter
There has been an increase in the level of concern regarding the ethical implications arising from the automation of tasks and the subsequent job displacement due to AI.
Author statement based on a review of (unspecified) novel studies and existing literature; no empirical sample size, instrumentation, or quantitative measure of 'concern' reported in the provided text.
Over-reliance on data-driven insights without adequate human oversight can worsen market uncertainty.
Reported in the study's qualitative case studies and interpretive analysis as a potential negative consequence of improper AI/Big Data use (no quantified examples provided in the summary).
Algorithmic bias is a potential pitfall of using AI and Big Data that can exacerbate market uncertainty.
Identified as a risk in the paper's qualitative analysis and discussion of pitfalls (no incident counts or empirical quantification provided in the summary).
External pressures (e.g., pandemics, extreme weather, geopolitical conflicts) disproportionately affect peripheral suppliers in the construction supply chain network.
Mapping of challenge categories to network positions in the study showed external pressures concentrating at peripheral supplier nodes; based on interview reports and network coding (quantitative support not detailed in abstract).
Relationship and contract issues accumulate at high-centrality brokers, which exhibit a reported degree centrality of 0.818.
Result reported in the paper linking the thematic category (relationship/contract issues) to network nodes identified as high-centrality brokers; a numeric degree centrality value (0.818) is reported for these brokers. Underlying network constructed from thematic coding of interviews; sample size not provided in abstract.
Six main challenge categories (comprising 16 open codes) concentrate systematically at specific network positions.
Results reported: thematic grouping produced six challenge categories and 16 open codes, and these were mapped to positions in the network showing systematic concentration; underlying data derive from coded interviews and network mapping (sample size not given in abstract).
Alignment interventions (e.g., fine-tuning, instruction-following adjustments) can systematically reshape or obscure the cultural regularities learned during pretraining.
Analytical distinction drawn between base models and fine-tuned/aligned systems in the paper; claim based on conceptual analysis of how adaptation changes model behavior rather than on specific experimental results in the provided text.
The limitations of systems that prioritize academic pathways constrain workforce adaptability and inclusive labor market development.
Argument based on synthesis of empirical studies and secondary data connecting education pathway composition to workforce adaptability and inclusiveness (presented as a policy-relevant conclusion rather than a quantified causal estimate).
Skills mismatch in the labor market is structural and linked to education systems that prioritize academic pathways without adequate support for vocational and continuing training.
Integrated interpretation of comparative evidence and secondary data showing imbalances between academic and vocational provision and associated labor-market frictions (paper frames this as a structural conclusion; specific causal tests not described in the summary).
Expansion of intermediate vocational skills has been limited relative to the expansion of higher education.
Comparative evidence and secondary data showing smaller increases in intermediate vocational qualifications compared with higher education attainment (specific metrics/country coverage not provided in the summary).
Short-run labor market disruptions raise concerns regarding wage inequality and workforce adaptation.
Claims based on observed short-run labor market adjustments in publicly available data and theoretical implications for inequality and adaptation; specific empirical measures, time horizons, and sample sizes are not reported in the excerpt.
AI simultaneously increases adjustment pressures for routine tasks.
Argument and cited observations from publicly available labor market data indicating displacement or adjustment in routine-task-intensive occupations (no specific empirical estimates or samples provided).
The Cautious are held in organizational stasis: without early adopter examples they don't enter the virtuous adoption cycle, never accumulate the usage frequency that drives intent, and never attain high efficacy.
Comparative analysis of archetype subgroups in the survey (N=147) showing the 'Cautious' group has lower reported usage frequency, lower intent to increase usage, and lower self-reported efficacy relative to 'Enthusiasts' and 'Pragmatists'.
Adoption of AI testing tools lags that of coding tools, creating a 'Testing Gap'.
Within-sample comparison of reported adoption rates for coding-oriented AI tools versus testing-oriented AI tools among 147 developers, showing lower adoption for testing tools.
Security concerns remain a moderate and statistically significant barrier to adoption.
Survey-derived security-concern metric (N=147) that shows a statistically significant negative association with future adoption intention (reported as moderate in effect size).
Current national and regional approaches to AI governance are often fragmented, focusing narrowly on industrial competition, piecemeal regulation, or abstract ethical principles.
Asserted in abstract; implies a review/comparison of existing policies but the abstract does not detail methods or sample beyond later comparative analysis.
AI deepens inequality.
Asserted in abstract; the abstract does not state empirical methods or data backing this claim.
AI's current trajectory exacerbates labor market polarization.
Asserted in abstract; no study design or empirical sample specified in the abstract.
When ERM is implemented merely as a formal compliance mechanism, firms do not realize the same benefits as when ERM is embedded in culture and daily decision-making.
Synthesis from reviewed empirical and conceptual studies indicating differences in outcomes depending on the nature of ERM implementation; underlying studies appear to include comparative observations but are not detailed in the summary.
Traditional silo-based risk management approaches are inadequate for MSMEs in increasingly volatile and uncertain business environments.
Conceptual arguments and literature reviewed in the article contrasting silo-based approaches with integrated ERM frameworks; based on theoretical and empirical critiques in the reviewed literature.
Traditional human resource management (HRM) approaches in hospitals rely on manual processes that are prone to errors, lack adaptability, and fail to adequately balance staff preferences with patient care requirements.
Background/positioning statement in the paper; asserted based on literature and authors' motivation for proposing an AI-driven framework (no specific dataset or quantitative analysis provided for this claim).
There are concerns that AI may undermine the right to privacy in India.
Legal and policy analysis in the paper discussing privacy risks associated with AI and data-driven governance (review of privacy frameworks and potential conflicts). No empirical sample size; based on normative/legal analysis.
There are concerns that AI has the potential to further increase economic inequality in India.
The paper raises this as a policy/legal concern using theoretical and analytical argumentation (literature/policy review); no primary empirical study or sample size reported in the summary.
Simulations project measurable reductions in defect rates under AI-HRM scenarios.
Regression-based simulations of the counterfactual model include defect reduction as an organizational outcome and project decreases in defect rates when HR processes are AI-supported.
Simulations show notable reductions in absenteeism under the AI-HRM scenario.
Predictive estimation and regression-based simulations projecting absenteeism rates under counterfactual AI-supported HR processes using the industrial firm dataset.
Employers that understand their largeness may act strategically when hiring and setting wages, generating misallocation and harming workers.
Theoretical argument made by the authors; no micro-econometric estimates, experiments, or sample descriptions are provided in the excerpt to substantiate degree or prevalence of strategic behavior.
This micro approach is at odds with the reality of labor markets in which monopsony potentially matters most.
Interpretive claim by the authors contrasting model assumptions with observed market structure; no empirical data, sample size, or specific markets cited in the excerpt.
The number of granted AI-related patents is negatively associated with GDP growth in the model.
Panel econometric analysis using OLS, Fixed Effects, Difference GMM and System GMM estimators; AI innovation proxied by the number of granted AI-related patents; reported negative association across the applied estimators (sample of countries and time span not specified in the provided summary).
Digital intelligence significantly reduces carbon dioxide emissions.
Empirical results from the paper using panel VAR and DID analyses on the three-country sample; specific effect sizes, statistical significance levels, and time period not provided in the summary.
E-commerce has significant environmental impacts due to its large carbon footprint.
Background/literature motivation stated in the paper (qualitative claim); no specific sample size or quantitative estimate provided in the summary.
AI intensifies asymmetries of power and creates 'algorithmic hierarchies' that reinforce digital dependence, especially in the Global South.
Analytic finding derived from document review and comparative analysis; no quantitative measures or empirical case sample reported in the text to substantiate scale or prevalence.
Technological variations contribute to limiting sustainability efforts.
Highlighted in the paper's analysis of governance challenges (listed alongside corruption and administrative inefficiencies) and referenced in international examples; no specific empirical measurement or sample size is provided in the summary.
Deep-rooted governance issues — specifically corruption, administrative inefficiencies, policy gaps, and technological variations — restrict sustainability efforts, particularly in developing and transition economies.
Analytical emphasis in the paper drawing on global governance frameworks and case illustrations from international instances; the summary does not report empirical sample sizes or quantitative measures.
AI integration into resort-to-force decision-making organizations raises important concerns.
Conceptual claim discussed by the author; the paper does not present empirical data, incident analyses, or quantified risk assessments supporting this claim within the provided excerpt.
Governing the complexity introduced by military AI integration is urgent but currently lacks clear precedents.
Authorative claim grounded in argumentation and review-style reasoning; no systematic review or empirical mapping of precedents is provided in the text.
We can expect increased organizational complexity in military decision-making institutions as AI proliferates.
Theoretical inference presented by the author; no empirical methods or measurements (e.g., complexity metrics, case studies, or sample sizes) are reported.
These findings challenge optimistic narratives of seamless workforce adaptation and demonstrate that emerging economies require active pathway creation, not passive skill matching.
Synthesis and interpretation of the quantitative results from the knowledge graph analysis (percent at risk, percent with viable pathways, number of feasible transitions, skill-leverage findings) used to draw policy implications about workforce adaptation strategies.
The remaining 75.6% of at-risk workers face a structural mobility barrier requiring comprehensive reskilling rather than incremental upskilling.
Complement of the 24.4% with viable pathways (i.e., 100% - 24.4% = 75.6%) derived from the knowledge-graph transition analysis; interpretation that lacking the viability thresholds implies need for comprehensive reskilling.
Current research in this area has a primary focus on methodology and computer science rather than applied occupational health questions.
Authors' synthesis from the review of existing studies (the paper reports that reviewed studies emphasize methodological and computer science aspects; exact counts or proportions not provided in the excerpt).
The application of machine learning in occupational mental health research remains in its preliminary stages.
Claim stated by the paper based on the authors' literature review of the field (review methodology referenced in the paper; number of studies or specific inclusion criteria not provided in the provided excerpt).
Many core university functions can now be achieved through AI-powered alternatives, potentially rendering conventional models obsolete for many learners.
Analytical assessment by the authors, without reported empirical testing or quantified methodology; based on review of AI capabilities and extrapolation.
Universities' core value proposition is challenged and potentially displaced by AI technologies as they alter how knowledge is accessed, created, and validated.
Authors' analytical argument drawing on technological, economic, and social drivers; presented as synthesis rather than empirical proof (no sample size or empirical method reported).
Robotics reduce labor dependence in greenhouse operations.
Study conclusions drawn from modeled impacts on employment composition and labor requirements when comparing robotics investments to traditional greenhouse investment scenarios (I–O modeling, IMPLAN 2022).
Technology companies, service providers, and civil society share responsibility for protecting children online, but current measures by these actors are insufficient.
Argument in the book summary based on evaluation of stakeholder roles; likely supported by case studies or policy analysis in the full text, but no specific methods, cases, or sample sizes are provided in the excerpt.
Current regulations fall short in effectively protecting children in an evolving digital landscape; there are persistent gaps and a growing need for internationally coordinated approaches.
Conclusion presented in the book's comparative legal analysis; implies review of EU (and US) legal frameworks and identification of gaps, but the excerpt does not list the analytical method, jurisdictions reviewed in detail, or specific legal provisions examined.
Europe has emerged as a major hub for hosting child sexual abuse material (CSAM), including newer forms such as deepfake abuse content and AI-generated 'DeepNudes.'
Asserted in the summary; would be supported by law-enforcement takedown data, hosting statistics, or forensic analyses of seized material, but the excerpt provides no specific datasets, agencies, or sample sizes.
Violations of privacy, exposure to disturbing content, unwanted sexual approaches, and cyberbullying are becoming more common.
Trend claim made in the book summary; would be supported by longitudinal or comparative prevalence data on online harms, but no specific studies, methods, or sample sizes are cited in the provided text.
Nearly one in three reports feeling unsafe.
Specific prevalence statement included in the summary; implies self-report survey data on perceived safety among youth, but the excerpt does not identify the survey instrument, population, timeframe, or sample size.
Traditional IT service hiring will be displaced by expansion of product-focused roles and Global Capability Centres (GCCs).
Synthesis of industry reports and workforce data indicating shifts in hiring patterns; the abstract does not report sample sizes or exact metrics.
The scalability of the Photo Big 5 enables new academic insights into the role of personality in labor markets, but its growing use in industry screening raises important ethical concerns regarding statistical discrimination and individual autonomy.
Argument in the paper based on the methodological scalability (AI + large LinkedIn microdata) and observed predictive links to labor-market outcomes; authors raise normative concerns about industry adoption and implications for discrimination and autonomy.