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
Remove filter
Between 2020–2025 Russia trails the United States, China and the EU on both digitalization indicators and AI investment volumes in the mining and oil & gas sectors.
Comparative multi-country trend analysis (2020–2025) using publicly available investment and digitalization indicators: national/industry statistics, investment datasets, and sectoral digitalization indices comparing Russia, US, China and EU over 2020–2025.
Loss of control over research data impedes local capture of value (knowledge, IP, downstream services) and can create externalities when data are repurposed or commercialized without equitable benefit sharing.
Conceptual argument grounded in case observations about data flows and provider practices; no quantitative measures of value capture provided.
Dominant AI/cloud providers become de facto gatekeepers of data processing and storage; researchers and institutions, particularly in lower‑capacity jurisdictions, have limited bargaining power to enforce data‑sovereignty or transparency terms.
Mapping of third‑party dependencies and interview/observational evidence of institutional procurement constraints in the Chile case; normative discussion of market power implications.
Algorithmic opacity and cross‑border regulatory fragmentation raise monitoring, compliance, and contractual costs for collaborative research, effectively increasing the transaction costs of data‑intensive science.
Analytical inference from qualitative findings (opacity, legal fragmentation) and normative economic reasoning presented in the implications section; no quantitative transaction‑cost measurement reported.
Inequalities in infrastructure (local compute, storage, institutional procurement power) amplify these problems: researchers in weaker jurisdictions face higher risks and fewer mitigation options.
Case study observations about local infrastructure capacity, procurement practices, and institutional constraints in Chile; qualitative reports of limited mitigation choices.
Rather than shifting liability away from researchers, AI systems increase researchers' ethical responsibilities: researchers must assess third‑party tools, negotiate data flows, and manage risks despite having limited contractual leverage.
Qualitative interviews and institutional observations reporting researchers' roles in assessing tools and managing data flows; normative analysis of accountability responsibilities in the case study.
Algorithmic opacity (hidden models, undocumented data flows, proprietary cloud stacks) reduces researchers' ability to control or even know how participant data are used, transferred, or monetized.
Interview data and mapping of third‑party dependencies showing opaque provider practices and limited transparency about model/data flows in the Chile case study.
Everyday AI services used in research introduce new, diffuse points of data capture and processing that complicate informed consent and privacy management.
Observations and documented mappings of tool use and data flows (e.g., transcription services, cloud platforms, meeting assistants) reported in the case study; supported by qualitative interviews with researchers/administrators.
AI tools embedded in everyday research infrastructures intensify — rather than reduce — ethical accountability burdens: they constrain researcher autonomy and undermine data sovereignty, especially in cross‑national settings where legal protections are fragmented or weaker.
Qualitative case study centered on environmental science research in Chile that uses GDPR as a normative framework; methods reported include interviews, observation, and mapping of data flows and third‑party dependencies (sample sizes not reported).
Insufficient regulation increases risks of negative externalities (privacy harms, biased hiring/management) that can reduce labor supply attachment or lower human capital investments.
Theoretical reasoning and synthesis of documented case studies and reports referenced in the commentary; not supported by new causal empirical analysis in the paper.
Absent strong worker voice or mandated impact assessments, AI-driven surveillance, algorithmic management and task reallocation are more likely, increasing risks of deskilling, displacement, and discriminatory outcomes.
Policy synthesis identifying plausible channels from AI system use to worker harms; supported by case-study reports in the symposium but no systematic empirical quantification in this commentary.
Weakening of organized labor and stalled worker-protection legislation raises the probability that AI adoption will increase employer bargaining power, potentially depressing wages and worsening job quality for affected occupations.
Analytic inference from labor economics theory and policy review; commentary does not present causal microdata linking AI adoption to wage or job-quality outcomes.
Export controls may constrain access to advanced models and hardware, affecting productivity gains unevenly across firms and sectors.
Policy analysis of current export control instruments and their potential economic effects; no firm- or sector-level quantitative analysis presented.
A conservative Supreme Court majority increases the risk of rulings that could further constrain organized labor and weaken labor’s power to negotiate AI-related workplace rules.
Legal analysis connecting Supreme Court composition and recent jurisprudence to possible effects on labor law and collective bargaining; predictive inference rather than empirical testing.
The incoming second Trump administration is dismantling many Biden-era worker-protection initiatives (notably rescinding or undercutting the Biden Executive Order intended to hold employers accountable for AI impacts).
Policy/legal analysis referencing recent executive actions and reported rollbacks of Biden-era frameworks; synthesis of documents and news/administrative actions reviewed in the commentary; no original empirical sample.
The DoD acquisition workforce is shrinking (through retirements, buyouts, reductions in force), reducing institutional knowledge and the discretionary capacity needed to exercise the memo's expectations responsibly.
Institutional trend evidence: assessment of publicly reported and internal staffing trends (reports of retirements, voluntary buyouts, reductions in force). No precise headcount, rate, or sample size provided in the analysis; described as a documented declining acquisition workforce.
Mandated 'any lawful use' contract language shifts risk-management responsibilities toward the government, reducing contractors' incentives to constrain misuse and increasing government residual legal/operational exposure.
Primary source analysis of required contract language in the memo and contracting directives, combined with conceptual principal–agent and moral-hazard assessment (risk/scenario modeling). No empirical measurement of incentive changes provided.
The memo departs from the Department's prior lifecycle-assurance framework and substitutes different standards while elevating vague criteria (e.g., 'model objectivity') without operational definitions or evaluation methods.
Primary source comparison: close reading of the January 2026 memo versus prior DoD lifecycle-assurance documents; identification of new/changed terminology and lack of accompanying operational definitions or test methods in the policy text.
By centralizing waiver decisions in a Barrier Removal Board, the memo converts baseline governance controls into exception-driven permissions (i.e., governance becomes something to be suspended rather than enforced).
Qualitative institutional analysis and primary-source reading of the memo establishing a centralized waiver process; mapping of how waiver mechanisms interact with existing assurance processes (ATO, T&E, contracting). No quantitative measurement of waiver frequency provided.
The memo explicitly frames governance and procurement speed as a zero-sum tradeoff and labels long-standing oversight mechanisms (Authorities to Operate, test & evaluation, contracting reviews) as 'blockers' eligible for waiver.
Primary source analysis: textual interpretation of the memo and accompanying contracting directives that characterize oversight mechanisms as impediments and make them eligible for waiver. Evidence is documentary (policy text); no quantitative sample.
Regulatory fragmentation increases compliance costs and stifles cross-border scale economies; international coordination and mutual recognition of standards can lower trade costs.
Comparative governance analysis and economic reasoning about cross-border trade and compliance; no cross-country causal estimates provided in the report.
Large incumbents with data/network advantages may entrench market power.
Policy and literature review noting data/network effects, observed tendencies in tech markets; sectoral examples discussed in the report.
Without targeted policy, AI can amplify winner-take-all dynamics (market concentration, superstar firms) and spatial inequalities (urban vs. rural).
Theoretical economic arguments and review of literature on data/network effects and concentration; comparative policy analysis that raises distributional concerns.
Without international coordination, providers may relocate compute or obscure compute locations to avoid stricter regimes; harmonized rules reduce these distortions.
Regulatory mapping and economic reasoning about geographic investment, regulatory arbitrage, and compute-location disclosure incentives.
Compliance and reporting requirements will impose additional costs on firms, with small providers likely disproportionately affected unless rules are proportionate.
Policy analysis of compliance and transaction costs (qualitative assessment of administrative burden and scale effects).
The facility-level focus and training-phase emphasis of current governance limit regulators' ability to monitor and mitigate the full environmental externalities of modern AI systems.
Synthesis of empirical findings on model/inference impacts combined with regulatory mapping showing gaps between impact locus and regulatory reach.
Transparency about AI environmental impacts has declined even as deployments of generative models have accelerated, creating an information gap for regulators, users, and researchers.
Trend observations from collated operational datasets and cited empirical studies indicating reduced disclosure by providers alongside increased deployments; supported by regulatory mapping noting scant AI-specific reporting outside the EU.
The larger cumulative environmental impacts of these generative models are primarily driven by inference-phase (online serving) energy consumption rather than training-phase emissions.
Evidence synthesis and operational data analysis focusing on deployment/inference patterns and relative contribution of lifecycle phases in examined models.
Generative web-search and reasoning AI models deployed widely in 2025 impose substantially higher cumulative environmental costs than earlier AI generations, largely driven by inference at scale.
Evidence synthesis: collation of empirical studies and operational data comparing energy and emissions profiles of 2025-era model families and deployment patterns (paper-wide comparative accounting).
There is a persistent gap between policy intent (promises of ethical protection and economic opportunity) and lived experience, producing new forms of social exposure—especially for vulnerable groups.
Synthesis of qualitative findings from documents, ethics guidelines, industry statements, and stakeholder commentary indicating aspirational policy language contrasted with limited enforceable protections; specific lived-experience case data are not provided.
Lack of enforceable data-rights and accountability mechanisms strengthens incumbent platforms’ control over data markets, potentially reducing competition and hindering entry by smaller firms.
Qualitative review of regulatory texts and industry positioning showing limited enforceable data-rights provisions; theoretical market-structure inference without empirical market-share analysis.
Weak or non‑enforceable rules create conditions for negative externalities (data exploitation, discriminatory automation) that markets alone may not correct.
Argumentative synthesis from document analysis and theoretical framing (communication rights, market-failure logic); supported by examples in policy and industry discourse but not by empirical market-level measurement in the paper.
The dominant framing privileges economic imaginaries of competitiveness and development over communication rights, producing regulatory blind spots and reinforcing existing inequalities.
Interpretive analysis using communication-rights theory and SCOT applied to policy and industry discourse; comparison of economic-oriented language versus rights-oriented provisions in reviewed documents.
Regulatory attention typically overlooks vulnerable and marginalized populations (low-wage workers, women, rural communities), whose mobile communication practices and data are disproportionately exposed to harm.
Document-based qualitative analysis identifying patterns of inclusion/exclusion in regulatory texts and public debate; stakeholder commentary reviewed indicates limited consideration of these groups. (Sample count not provided.)
Indonesia’s governance of mobile-AI rests largely on soft‑law, aspirational instruments (guidelines, non‑binding ethics codes), which limits enforceability and accountability.
Qualitative discourse- and document-based analysis of key policy documents, national ethics guidelines, industry statements, and public stakeholder commentary related to mobile-AI in Indonesia. (The paper identifies dominant use of non‑binding instruments; exact number of documents reviewed is not specified.)
Fidelity-related biases risk concentrating harms among underrepresented populations, potentially increasing healthcare costs and welfare losses; economic evaluation and auditing for distributional impacts should be integrated into procurement and reimbursement decisions.
Interdisciplinary evidence synthesized from ML fairness studies, clinical validation reports, and health economics literature included in the review; the review recommends integrating distributional analysis in HTA based on documented risks of differential model performance.
Weak or absent regulation increases uncertainty and may deter investment or lead to adoption of low-quality synthetic products with negative economic and clinical externalities.
Policy literature and implementation case studies summarized in the review that link regulatory gaps to investment risk and potential for low-quality product adoption; evidence is mostly inferential and descriptive.
Without improvements in fidelity and domain adaptation, synthetic data risks introducing bias and limiting clinical and economic benefits.
Integrated assessment from machine-learning evaluations, clinical validation studies, and implementation analyses within the review which link fidelity and domain mismatch to biased model outputs and reduced clinical utility; economic implications are inferred from cost-effectiveness and procurement literature cited in the review.
There is evidence of problematic patterns in automated decision appeals and workflow interactions when AI is integrated into clinical processes.
Case studies, deployment reports, and observational analyses cited in the synthesis that document increased appeals, workflow friction, or unexpected interactions caused by automation.
Failing to retrain health workers for AI will produce structural labor-market mismatches, slow adoption, and reduce realized economic benefits.
Labor-market analysis and workforce readiness findings from the narrative synthesis and Delphi inputs; argument is inferential based on observed skill gaps and adoption barriers in the reviewed literature.
Indonesia risks technological dependency on foreign vendors if domestic capability, data governance, and procurement are not strengthened.
Market and policy assessment from the review, including procurement analyses and discussion in supplementary national reports and Delphi studies; based on observed market structures and procurement practices identified in the literature.
Approximately 58.7% of the relevant Indonesian health workforce lacks the AI competence or literacy needed for safe, scalable adoption.
Workforce readiness estimate derived from reviewed workforce assessments, Delphi consensus studies, and national reports included in the narrative synthesis; the summary does not specify sample frames or exact survey instruments that produced the 58.7% figure.
Indonesia’s AI healthcare maturity score is approximately 52/100, trailing regional peers (example comparators: Singapore ≈ 92, Malaysia ≈ 78).
Benchmarking performed in the review against regional maturity catalogues and international standards (EU AI Act, Singapore, Australia); maturity scoring method referenced in the paper but detailed scoring rubric and underlying metrics not fully reproduced in the summary.
Widespread adoption of LLMs without adequate verification increases systemic cybersecurity risks with potential economic spillovers.
Synthesis of security incident case studies and risk analyses revealing vulnerabilities in generated code and potential downstream impacts.
Models lack deep contextual reasoning and may fail on tasks requiring long-term design thinking or deep domain knowledge.
Benchmark failures and user studies in the reviewed literature demonstrating degraded performance on complex architectural/design tasks and domain-specific reasoning problems.
Use of these tools can mask gaps in foundational computational skills among novices.
Pedagogical case studies and assessments indicating reliance on AI can produce superficial solutions and lower demonstrated understanding of core concepts.
Negative externalities from synthetic media (misinformation, reputational harm, verification costs) may justify public interventions such as provenance standards, mandatory labeling, penalties for malicious misuse, and public investment in verification infrastructure.
Policy analysis and normative recommendations based on identified externalities in the reviewed literature; no empirical policy evaluation in paper.
Compliance with IP, privacy and liability regimes will impose costs (monitoring, licensing, disclosure) that may raise barriers for smaller entrants and affect prices and diffusion of generative audiovisual models.
Regulatory and economic literature synthesized in the narrative review; policy/legal case citations included but no new cost estimates provided.
Proliferation of generated content may increase information supply but lower per-item attention and willingness-to-pay, potentially reducing monetization unless intermediaries solve discoverability and trust issues.
Theoretical arguments using attention-economy literature and secondary studies; narrative reasoning without new empirical quantification.
Platforms and firms that control model training data and deployment infrastructure will gain strategic advantage, increasing risks of vertical integration and market concentration.
Market-structure and firm-strategy analysis drawn from secondary literature and conceptual arguments in the paper.