Evidence (2290 claims)
Adoption
5187 claims
Productivity
4472 claims
Governance
4082 claims
Human-AI Collaboration
3016 claims
Labor Markets
2450 claims
Org Design
2305 claims
Innovation
2290 claims
Skills & Training
1920 claims
Inequality
1286 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 373 | 105 | 59 | 437 | 982 |
| Governance & Regulation | 366 | 172 | 114 | 55 | 717 |
| Research Productivity | 237 | 95 | 34 | 294 | 664 |
| Organizational Efficiency | 364 | 82 | 62 | 34 | 545 |
| Technology Adoption Rate | 290 | 115 | 66 | 27 | 502 |
| Firm Productivity | 274 | 33 | 68 | 10 | 390 |
| AI Safety & Ethics | 116 | 177 | 44 | 24 | 363 |
| Output Quality | 231 | 61 | 23 | 25 | 340 |
| Market Structure | 107 | 121 | 85 | 14 | 332 |
| Decision Quality | 158 | 68 | 33 | 17 | 279 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Fiscal & Macroeconomic | 74 | 52 | 32 | 21 | 183 |
| Skill Acquisition | 88 | 31 | 38 | 9 | 166 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 105 | 12 | 21 | 11 | 150 |
| Consumer Welfare | 66 | 29 | 35 | 7 | 137 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 66 | 31 | 4 | 125 |
| Task Allocation | 68 | 8 | 28 | 6 | 110 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 11 | 16 | 94 |
| Worker Satisfaction | 42 | 32 | 11 | 6 | 91 |
| Task Completion Time | 74 | 5 | 4 | 1 | 84 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Wages & Compensation | 38 | 13 | 19 | 4 | 74 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 15 | 9 | 5 | 47 |
| Job Displacement | 5 | 29 | 12 | — | 46 |
| Developer Productivity | 27 | 2 | 3 | 1 | 33 |
| Social Protection | 18 | 8 | 6 | 1 | 33 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 8 | 4 | 9 | — | 21 |
Innovation
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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.
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.
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.
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.
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.
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.
Performance expectancy is a negative factor related to the company's decision to adopt AI (attributed to initial implementation challenges reducing perceived ease of use).
PLS-SEM analysis of survey data from 207 firms; the paper reports a negative association between performance expectancy and AI Adoption and offers a rationale about 'reality check' and initial implementation difficulties.
Digital transformation raises challenges related to privacy, inequality, and regulatory scrutiny.
Identified as a key challenge in the paper; the abstract provides no details on how privacy concerns, inequality measures, or regulatory incidents were documented or quantified.
Interpreting the literature through a socio-technical lens reveals a persistent misalignment between GenAI's fast-evolving technical subsystem and the slower-adapting social subsystem.
Authors' conceptual interpretation of the reviewed studies (28 papers) using socio-technical theory to integrate technical and social themes from the literature.
Skills mismatch and SME adoption constraints constitute a binding bottleneck for inclusive digital–green upgrading.
Synthesis of studies on skills, firm capabilities, and SME adoption of digital and green technologies (review-level evidence; no single dataset or sample size provided).
Absent complementary institutions and infrastructure, digitalization may increase electricity demand, widen inequality, and incentivize strategic disclosure (greenwashing).
Literature review drawing on empirical studies of energy consumption from digital systems, labor-market studies, and analyses of ESG disclosure practices (review-level synthesis; no single sample size reported).
The review identifies highly heterogeneous modeling approaches with limited convergence toward shared benchmark tasks.
Comparative assessment across the 42 studies indicating a wide variety of modeling choices and an absence of commonly adopted benchmark tasks for direct comparison.
The literature reveals constraints, including challenges in processing long financial documents, limited availability of labeled datasets, and strong geographic and linguistic concentration.
Synthesis of methodological limitations and practical constraints reported across the reviewed studies (issues repeatedly mentioned in the corpus of 42 studies).
Embedding-based representations and end-to-end deep learning architectures appear only sporadically.
Review observations that only a small subset of the 42 studies used embedding representations or end-to-end deep learning models, i.e., these approaches are uncommon in the sample.
Less attention has been given to how sentiment-based textual features obtained from corporate reports are integrated into machine learning pipelines to predict firms' financial outcomes.
Synthesis from the systematic review of 42 studies indicating relatively few studies use corporate report–derived sentiment or explicitly address integration of such textual features into ML pipelines for firm-level financial predictions.
The system forces many children to age out at 21, creating deportation risks for those who are American in every meaningful sense except paperwork.
Policy consequence of long backlogs: derivative status rules cause dependents to 'age out' at 21; deportation risk implication is a legal/administrative outcome. The excerpt does not quantify the number affected or present a dataset.
The backlog traps H-4 dependent spouses, over 90% of whom hold bachelor's degrees, in years-long employment prohibition, removing skilled labor from the workforce.
Claim combines (a) an asserted >90% college-degree rate for H-4 spouses—presumably from ACS/DHS or authors' survey analysis—and (b) immigration policy facts that many H-4 spouses lack work authorization for extended periods; the excerpt does not provide the underlying dataset, sample size, or citations.
Constrained mobility suppresses H-1B wages by 12.2%.
Empirical estimate asserted in the paper (likely from econometric analysis comparing wages under constrained vs. unconstrained mobility); the excerpt does not cite the specific study, dataset, sample size, or methods that produced the 12.2% figure.
Employer-specific sponsorship combined with high switching costs—$5,000+ in fees and multi-year delays—concentrates labor-market power among employers.
Policy/mechanism claim supported by typical filing fee estimates and observed multi-year adjudication/porting constraints; the excerpt does not report a formal empirical test or sample size demonstrating employer market power concentration.
These provisions have generated wait times as extreme as 195 years for Indian nationals in the EB-2 category.
Projection based on visa bulletin/backlog dynamics and issuance rates for EB-2 India; the paper does not show the step-by-step projection or assumptions in the excerpt.
The U.S. employment-based immigration system traps over 1.8 million skilled workers and their families in legal limbo.
Paper's aggregate/backlog calculation presumably using Department of State visa bulletin backlogs, USCIS pending adjustment of status (I-485) inventories, and derivative family counts; the paper does not provide the detailed method or sample breakdown in the excerpt.
When policy uncertainty is high, the market's pricing of AI-intensive firms becomes less anchored to real economic performance.
Interpretation of model results that show a reduced linkage between labor productivity growth and equity valuations during high EPU periods, as estimated by the smooth-transition local projection model on U.S. data.
Economic policy uncertainty disrupts how stock markets value fundamental productivity in the AI-intensive (AI and robotics) sector.
Inference from the same smooth-transition local projection estimates showing a change in the productivity→valuation relationship across EPU regimes, based on U.S. productivity and EPU series used in the paper.
Economic policy uncertainty (EPU) weakens the positive effect of labor productivity growth on equity valuations in the AI and robotics sector.
Estimated from a smooth-transition local projection model using U.S. labor productivity and EPU data; the paper reports that the positive productivity→valuation effect 'weakens significantly' as EPU rises (statistical significance claimed). Python code and data for replication are provided in the appendix.
BT adoption reduces the level of earnings management practice.
Additional empirical tests on the same sample (27,400 firm-years, 2013–2021) comparing firms' earnings management measures before/after or between adopters and non-adopters of BT (earnings management measured by standard accrual-based metrics—details in paper).
Compensation-based frameworks for personal data may advantage those better able to monetize data, potentially worsening inequality.
Theoretical argument and literature synthesis on distributional effects of markets and bargaining power; paper does not present empirical distributional simulations or data.
Data markets tend to concentrate benefits and rents in large platforms while externalizing harms onto individuals and society.
Argument based on descriptive facts about platform business models and literature on market concentration in digital markets; no original econometric concentration analysis provided in the paper.
Standard market-failure fixes (better information, pricing, contracting) are insufficient to address the moral and social-structural harms of commodifying privacy.
Philosophical argument drawing on noxious-markets literature and limitations of informational/contractual remedies; supported by conceptual examples rather than empirical testing.
Harms from data commodification are often externalized, diffuse, and long-term (e.g., profiling, algorithmic discrimination, chilling effects on behavior).
Normative and descriptive synthesis of existing literature on algorithmic harms and privacy externalities; no original longitudinal or causal empirical evidence presented.
Consent in data markets is frequently weak, uninformed, or coerced (due to information asymmetries, complexity, and behavioral biases), undermining the ethical legitimacy of transactions.
Argumentative claim grounded in literature on privacy notice problems, behavioral economics, and descriptive reports on digital consent practices; no new empirical study included in the paper.
Commodifying personal information poses distinctive harms to individuals and social practices, including exploitation, corruption of personal autonomy, distributional injustice, and information asymmetries.
Conceptual analysis supported by literature review across ethics, political philosophy, and descriptive facts about digital-era data practices; uses illustrative examples and secondary sources rather than original empirical data.
Creating a market for personal data is equivalent to making the right to privacy a tradeable right, and such a market should be treated as a 'noxious market' in the sense articulated by Debra Satz.
Normative, conceptual argument applying Satz's noxious-markets framework to personal data; literature review and philosophical argumentation; no original empirical sample or econometric analysis.
Family- and purpose-driven entrepreneurs (motivated by social stability) experienced larger declines in innovation following income shocks than wealth-driven entrepreneurs.
Subgroup quantitative analysis comparing self-reported post-shock innovation activity across identity-defined groups (family/purpose-driven vs. wealth-driven) within the survey sample; outcome measured conditional on reported income shocks.
Inflation and geopolitical fragmentation can raise the cost of AI deployment (hardware shortages, supply constraints) and complicate cross-border data flows, slowing diffusion or creating regionalized AI ecosystems.
Conceptual argument linking macroeconomic and geopolitical constraints to AI deployment costs; no empirical cost-accounting or cross-country diffusion analysis provided in the paper.