Evidence (6869 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Governance
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Governance quality becomes negative and statistically significant at the 0.90 quantile (τ = 0.90), which the paper interprets as evidence of institutional rigidity in advanced financial systems.
MMQR results showing a negative, significant coefficient for governance quality at τ = 0.90; interpretation provided by the authors linking this sign to institutional rigidity.
AI use also poses risks, including systemic discrimination, privacy invasion, and commodification of talent.
Qualitative synthesis and documented instances in the reviewed literature (n=85) reporting discriminatory outcomes, privacy concerns, and labor commodification effects associated with algorithmic HR tools.
Qualitative synthesis reveals a 'gray zone' in labor relations and a 'black box' in algorithmic data processing, both exposing businesses to procedural injustice risks.
Thematic/qualitative synthesis of findings from the reviewed literature (n=85) highlighting issues of labor relations and algorithmic opacity leading to procedural fairness concerns.
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.
We lack frameworks for articulating how cultural outputs might be actively beneficial.
Authors' identification of a gap in evaluation theory and practice (conceptual analysis); no systematic literature review details provided in the excerpt.
Current AI evaluation practices show a critical asymmetry: while AI assessments rigorously measure both benefits and harms of intelligence, they focus almost exclusively on cultural harms.
Authors' review/ critique of existing evaluation frameworks and metrics (qualitative analysis in the paper); the excerpt does not list the reviewed studies or their number.
The field of AI is unprepared to measure or respond to how the proliferation of entertaining AI-generated content will impact society.
Authors' assessment of current evaluation practices and frameworks (qualitative analysis presented in the paper); no empirical metrics or sample sizes provided in the excerpt.
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.
Evidence strength is inversely correlated with intervention complexity.
Cross-domain synthesis reported in the paper that formalises an inverse evidence–complexity relationship based on the reviewed literature. The abstract does not quantify the correlation or list the domains/intervention types used to derive it.
Per-capita elderly care costs running 3–5 times those of working-age cohorts.
Cost comparisons reported in sources included in the 81-paper review. The abstract reports a 3–5x multiple but does not specify which cost categories, countries, or methodological adjustments were used.
Conventional policy instruments have failed to resolve pressures that include severe long-term care workforce shortfalls across leading ageing economies.
Synthesis of findings from the structured narrative review of 81 sources (2020–2025) indicating persistent workforce shortfalls. The abstract does not provide quantitative workforce shortfall magnitudes or country-specific data.
Demographic ageing is projected to reduce annual GDP growth by 0.3–1.2 percentage points by 2035.
Projection estimates referenced in the review literature (2020–2025). The abstract reports the 0.3–1.2 p.p. range but does not specify which models or studies generated these projections.
Ageing-related expenditure already absorbs up to 18% of GDP in the most affected economies.
Spending estimates drawn from the reviewed literature (2020–2025). The paper states 'up to 18% of GDP' for the most affected economies but does not list which economies or the original data sources in the abstract.
Advanced economies face a compounding demographic crisis: populations aged 65 and over will reach 30–40% in several nations by 2050.
Demographic projection claims cited in the paper's background literature (sources from the structured narrative review). No specific datasets or country-by-country breakdown provided in the abstract.
Current literature has primarily focused on automation-based views of decision support and lacks insight into systematic human–AI coordination aided by analytics.
Literature review and conceptual critique within the paper. No systematic mapping study or bibliometric counts reported.
Most organizations have difficulties converting algorithmic results into sustainable managerial decisions due to low levels of trust, lack of explanation, and poor integration between AI systems and human judgment.
Synthesis of existing literature presented in the conceptual paper (literature review). No empirical study or sample provided to quantify 'most organizations.'
AI adoption has augmented complexity, uncertainty in decision-making, and accountability stresses for managers.
Claim supported by conceptual argument and literature integration (qualitative synthesis). No empirical sample size or quantitative testing reported.
Traditional methods for assessing and developing employees' skills often fail to provide real-time feedback.
Statement supported by literature review cited by the authors; the abstract does not provide empirical comparisons, metrics, or sample sizes.
Existing research on AI-driven decision-making remains fragmented and often framed through substitution-oriented narratives that position AI as a replacement for human judgment.
Assessment based on the author's interdisciplinary literature synthesis (conceptual meta-analysis); descriptive evaluation of research framing rather than new empirical testing.
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.
AI causes job loss due to the automation of repetitive tasks.
Narrative literature review and synthesis of recent economic studies presented in the paper; no original empirical sample or primary data collection reported.
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).
Developing economies are more vulnerable where employment is concentrated in routine or informal tasks and where reskilling, mobility, and institutional buffers are limited.
Comparative consideration of advanced vs developing economies drawing on macro/sectoral indicators, labor market structure discussions, and existing empirical studies cited conceptually.
Creation of new jobs often lags displacement, producing transitional unemployment and reallocation frictions in the short- to medium-term.
Dynamic/task-based theoretical framing and synthesis of empirical evidence on technology adoption episodes showing delayed job creation relative to displacement.
AI disproportionately automates routine and many middle-skill tasks (both manual and cognitive), displacing corresponding occupations.
Synthesis of occupation- and task-level exposure studies and task-based automation literature referenced in the paper (no new empirical sample provided).
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.
The emergence and promotion of these theories acted as a 'Trojan horse' of ideological persuasion: technically framed economic scholarship advanced political messages that ran counter to the expected normative defense of markets and democracy.
Interpretive synthesis from archival and textual analysis showing alignment between the technical content of certain economic arguments and political narratives; analysis of institutional and funding contexts that plausibly facilitated persuasive deployment.
A strand of influential 20th‑century Western economic theory concluded that democracy and market institutions are dysfunctional.
Case‑study historical and textual analysis of Cold War‑era economic literature and influential works (including canonical publications and writings by prominent economists); close reading of papers/books and contemporaneous debates as reconstructed from archival and publication materials.
Stronger internal corporate governance weakens the AI → executive pay relationship, consistent with governance limiting managerial rent capture during technological change.
Moderation analysis in the paper interacting the firm AI indicator with corporate governance measures; results show a smaller AI effect on pay in firms with stronger governance (same sample and regression framework).
Traditional extrapolation-based employment forecasting (as used in current BLS/standard practice) is inadequate for capturing AI-driven labor market change.
Conceptual argument in the paper highlighting limitations of extrapolation methods (failure to distinguish automation vs augmentation, inability to capture rapid nonlinear adoption dynamics and demographic heterogeneity). No empirical test or sample is reported; critique is supported by theoretical considerations and examples rather than an applied dataset.
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.
Mandel's account—that capitalist production relations, class struggle, and global imbalances shape the course and consequences of waves—implies that crises expose and amplify supply-chain fragilities and bargaining conflicts that affect profitability.
Theoretical interpretation of Mandel's political-economy literature and historical examples (qualitative).
Platforms optimized for engagement can produce externalities that distort lived temporality (loss of presence and meaning) beyond standard attention‑capture harms.
Argument synthesizing platform literature and phenomenological concerns; no new empirical analysis of platform effects provided.
Contemporary transhumanist and neurotechnology developments (BCIs, neural digital twins, human–AI collaboration) have advanced technologically but lack a robust conceptual core focused on lived experience and temporality.
Survey and synthesis of existing literatures reported in the paper (conceptual review); no systematic empirical content analysis or coded sample size provided.
High PIGRS scores associate with genomic instability (higher tumor mutational burden and MATH heterogeneity scores) and immune‑escape signatures.
Association analyses within the PIGRS study linking high risk scores to higher TMB, elevated MATH scores, and immune evasion markers (multi‑omics and immune gene set analyses reported).
LLM-generated participants are particularly risky in strategic and game-theoretic settings because they may misrepresent incentives, dynamic strategic thinking, and bounded rationality.
Review highlights examples and theoretical concerns from multiple studies indicating misrepresentation of strategic behavior; grouped under risks for strategic settings.
The price-of-transparency quantifies how increased observability (e.g., from disclosure or regulation) can reduce the effectiveness of deception-based defenses, informing policy tradeoffs.
Formal definition of price of transparency and analytical results showing its effect; policy implication drawn in discussion (theoretical analysis, no empirical policy case studies).
High upfront and maintenance costs create scale advantages for larger institutions or centralized providers, potentially concentrating market power among well-resourced curriculum developers.
Economic inference from cost structure described in paper; no market concentration empirical data provided.
Disadvantages and risks include significant resource investment, complexity aligning multiple standards, and a high demand for continuous updates and audits.
Paper's risks section (author assertion); no quantified cost or burden data.