Evidence (8066 claims)
Adoption
5586 claims
Productivity
4857 claims
Governance
4381 claims
Human-AI Collaboration
3417 claims
Labor Markets
2685 claims
Innovation
2581 claims
Org Design
2499 claims
Skills & Training
2031 claims
Inequality
1382 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 417 | 113 | 67 | 480 | 1091 |
| Governance & Regulation | 419 | 202 | 124 | 64 | 823 |
| Research Productivity | 261 | 100 | 34 | 303 | 703 |
| Organizational Efficiency | 406 | 96 | 71 | 40 | 616 |
| Technology Adoption Rate | 323 | 128 | 74 | 38 | 568 |
| Firm Productivity | 307 | 38 | 70 | 12 | 432 |
| Output Quality | 260 | 71 | 27 | 29 | 387 |
| AI Safety & Ethics | 118 | 179 | 45 | 24 | 368 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 75 | 37 | 19 | 312 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 74 | 34 | 78 | 9 | 197 |
| Skill Acquisition | 98 | 36 | 40 | 9 | 183 |
| Innovation Output | 121 | 12 | 24 | 13 | 171 |
| Firm Revenue | 98 | 35 | 24 | — | 157 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 87 | 16 | 34 | 7 | 144 |
| Inequality Measures | 25 | 76 | 32 | 5 | 138 |
| Regulatory Compliance | 54 | 61 | 13 | 3 | 131 |
| Task Completion Time | 89 | 7 | 4 | 3 | 103 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 33 | 11 | 7 | 98 |
| Wages & Compensation | 54 | 15 | 20 | 5 | 94 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 27 | 26 | 10 | 6 | 72 |
| Job Displacement | 6 | 39 | 13 | — | 58 |
| Hiring & Recruitment | 40 | 4 | 6 | 3 | 53 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 11 | 6 | 2 | 41 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 6 | 9 | — | 27 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
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.
When positives are rare, the prevalence effect induces systematic cognitive biases that inflate misses and can propagate through the AI lifecycle via biased training labels.
Analysis of prior experimental evidence cited and discussed in the paper (literature review / synthesis). Specific prior studies and their methods are analyzed in the paper (sample sizes and individual study details not provided in the supplied excerpt).
Raising fertility actually worsens the fiscal picture in the medium term, since it takes decades for newborns to grow up and join the workforce.
Model scenario simulations that raise fertility rates and project fiscal outcomes over time, showing medium-term deterioration due to added dependents before working-age entry.
These demographic trends squeeze public finances from both sides—fewer people paying taxes and more people drawing on pensions and healthcare.
Conceptual linkage implemented in the integrated system dynamics model that couples demographic cohorts to tax revenue and age-linked public spending (pensions, healthcare).
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).
The shadow digital economy poses risks to national security.
Argumentative discussion and reviewed examples linking SDE activities to national security risks (method: conceptual/legal/institutional analysis; no national-security incident count or quantified risk assessment provided).
SDE activity extends beyond direct financial loss, eroding consumer trust and damaging brand reputation through data breaches, fraud, and counterfeiting.
Claim is supported by literature review and illustrative examples/case discussions in the paper (methods: qualitative synthesis; no aggregated empirical measurement of trust or reputational loss reported).
Institutional traps that sustain shadow employment exist and the SDE perpetuates informal and illicit labor arrangements.
Analytic argument and institutional analysis presented in the paper identifying mechanisms ('institutional traps'); evidence appears to be conceptual and drawn from reviewed literature and examples rather than stated empirical longitudinal data.
The shadow digital economy (SDE) is a growing phenomenon amid digital transformation and rising information costs.
Framing and literature review presented in the paper; descriptive synthesis of prior definitions and trends (no empirical sample size reported).
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.
Psychological barriers — specifically algorithm aversion, AI-induced job insecurity, technostress, and diminished occupational identity — impede effective AI integration across U.S. industries.
Literature synthesis of empirical and theoretical work in AI–HRM and organizational psychology cited in the paper (summary does not report primary-study sample sizes).
Workforce psychological readiness, rather than technological capability alone, constitutes the critical bottleneck in organizational AI adoption.
Synthesis of emerging empirical AI–HRM research and theoretical integration (paper reports 'findings' from this synthesis; no primary-sample-size details provided in the summary).
The integration of AI into U.S. workplaces represents a profound organizational psychology challenge that extends well beyond mere technology adoption.
Conceptual/theoretical argument based on literature synthesis; draws on established theories (Technology Acceptance Model, Human–AI Symbiosis Theory, Job Demands–Resources Model, Organizational Trust Theory) and cited empirical AI–HRM studies (no specific sample sizes or primary data reported in the summary).
What remains needed is rigorous advice to policymakers concerned about rapid increases in labor churn, scientific development, labor–capital shifts, or existential risk.
Normative conclusion drawn by the author from gaps identified in the seven-book review (qualitative assessment of unmet policy-relevant analysis); sample = 7 books.
The reviewed works offer little guidance regarding the transformative scenarios considered plausible by many AI researchers.
Author's evaluative judgment based on the content and emphases of the seven books (qualitative gap analysis); sample = 7 books.
AI heightens job insecurity, particularly in organisations lacking structured reskilling programs.
Stated finding derived from the mixed-method study and Scopus database analysis; framed with a conditional modifier pointing to organisations without structured reskilling programs. (Summary does not provide sample size, effect sizes, or statistical significance.)
Reliance on H-2A has limitations, including requirements to provide housing and training and higher mandated wages compared with local seasonal help.
Paper's qualitative assessment of H-2A program constraints; no empirical measures or comparative wage data provided in the excerpt.
Declining US birth rates may not alleviate the nursery labor problem in the coming decades.
Projection/interpretation based on demographic trend (declining birth rates) noted in the paper; no demographic model or quantitative projection provided in the excerpt.
Despite high overall employment (80% for ages 25–54), nurseries reported they were prevented from hiring new workers due to high wages and unqualified workers.
Reported responses from nurseries (survey/industry responses) referenced in the paper; sample size and survey details not provided in the excerpt.
The US nursery industry faces a labor deficit.
Statement in the paper based on industry reporting; specific methodology or sample size not provided in the excerpt.
Regulatory uncertainty is a significant barrier to GenAI adoption.
Regulatory uncertainty included as an environmental/TOE variable in the PLS-SEM model showed a significant negative association with GenAI adoption in the survey results (n = 312).
There are significant implementation challenges for Material Passports, particularly for existing buildings.
Aggregate findings from included studies highlighting technical, data-collection, legacy-information, and workflow barriers when applying MPs to existing building stock.
Circular economy (CE) adoption in the Architecture, Engineering, and Construction (AEC) industry is hampered by data scarcity.
Synthesis of included literature and authors' framing in the introduction and analysis sections indicating repeated identification of data scarcity as a barrier to CE adoption in AEC.
Selection of a human-LLM archetype brings important risks and considerations for the designers of human-AI decision-making systems.
Analytic discussion and synthesis of evaluation results and literature review; tradeoffs surfaced in the paper (e.g., decision control, social hierarchies, cognitive forcing strategies, information requirements).
The stability and patience that define long-term investors can breed strategic inertia.
Introductory assertion in the paper (conceptual observation). The paper does not present empirical data or sample analysis to substantiate this causal claim in the provided excerpt.
Conventional thinking often frames AI uncritically as just a tool for efficiency, which is a narrow perspective that overlooks AI's transformative role.
Critical/theoretical argument presented in the paper (conceptual observation). No empirical data, sample, or statistical analysis reported to support this claim.
Gendered perceptions of AI's social and ethical consequences, rather than access or capability, are the primary drivers of unequal GenAI adoption.
Comparative model results from the 2023–2024 nationally representative UK survey showing perceptions (societal-risk index) have greater explanatory/predictive power than measures of access (e.g., device/internet access) or capability (digital literacy, education).
Intersectional analyses show the largest gender disparities in GenAI use arise among younger, digitally fluent individuals with high societal risk concerns, where gender gaps in personal use exceed 45 percentage points.
Subgroup (intersectional) analysis of the nationally representative 2023–2024 UK survey data stratified by age, digital fluency, and societal-risk concern levels; reported gender gap >45 percentage points in specified subgroup.
The societal-risk concerns index ranks among the strongest predictors of GenAI adoption for women across all age groups, surpassing digital literacy and education for young women.
Multivariable models and predictor ranking using the 2023–2024 UK survey data showing relative predictive strength of the concerns index versus measures of digital literacy and education, with subgroup (age × gender) comparisons.
The societal-risk concerns index explains between 9 and 18 percent of the variation in GenAI adoption.
Regression/statistical models using the composite concerns index as a predictor of GenAI adoption in the nationally representative 2023–2024 UK survey; reported explained variation (9–18%).
Women adopt GenAI less often than men because they perceive its societal risks differently.
Statistical analysis linking a constructed composite societal-risk concerns index (mental health, privacy, climate impact, labor market disruption) to GenAI adoption, using the UK 2023–2024 survey; models compare explanatory power of perceptions versus access/capability variables.
Women adopt GenAI substantially less often than men.
Analysis of the 2023–2024 nationally representative UK survey data comparing personal use/adoption rates by gender.
Across survey and experimental evidence, perceptions that AI will replace labor—regardless of actual labor-market outcomes—may decrease democratic legitimacy and public engagement in shaping AI's future.
Synthesis of correlational findings from the large European survey (N = 37,079) and causal evidence from two preregistered experiments (UK N = 1,202; US N = 1,200).
Controlling for technology-related, political, and sociodemographic factors, perceiving AI as labor-replacing (vs. labor-creating) is associated with lower political engagement with technology.
Multivariable regression analyses on the large European survey (N = 37,079) with controls for technology-related, political, and sociodemographic factors.
Controlling for technology-related, political, and sociodemographic factors, perceiving AI as labor-replacing (vs. labor-creating) is associated with lower satisfaction with democracy.
Multivariable regression analyses on the same large survey (N = 37,079) including controls for technology-related attitudes, political variables, and sociodemographic covariates.
In abundant-resource conditions, emergent tribe formation slightly increases system overload (i.e., makes the near-zero overload slightly worse).
Empirical observations reported in the paper indicating a modest increase in overload when tribes form under abundant resources.
When resources are scarce, AI model diversity and reinforcement learning increase dangerous system overload.
Empirical results from the paper's AI-agent population experiments (simulations/real-agent trials) combined with mathematical analysis indicating increased overload under scarcity when model diversity and individual RL are present.
There are ethical concerns surrounding AI and automation including algorithmic decision-making, workforce exclusion, and inequality in access to reskilling opportunities.
Raised as an ethical analysis within the paper's conceptual framework; no empirical study, surveys, or quantified measures of these ethical issues are reported in this paper.
AI is eliminating repeated (routine) jobs.
Stated as part of the paper's argument about AI's dual impact; supported by conceptual analysis rather than new empirical evidence in this manuscript (no sample size or empirical method reported).
Artificial intelligence and automation are reshaping jobs, transforming them from a steady source of income to a dynamic process highly influenced by technology, flexibility, and uncertainty.
Central analytical claim made in the paper based on conceptual reasoning; the paper does not report empirical measures, datasets, or sample sizes to support the transformation quantitatively.