Evidence (7870 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
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Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Governance
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Perkembangan AI mengotomatisasi tugas rutin sekaligus menciptakan peluang pekerjaan baru berbasis digital.
Sistematis studi literatur yang menelaah 33 sumber ilmiah, laporan lembaga internasional, dan kebijakan terkait (n=33).
The comparative evaluation shows differences in economic inclusiveness between ML, DL, and Generative AI.
Abstract states differences in economic inclusiveness found in the review; no quantitative inclusiveness metrics or sample sizes provided in abstract.
The comparative evaluation shows differences in explainability among ML, DL, and Generative AI.
Abstract notes comparative differences in explainability as part of review findings; no empirical measures of explainability included in abstract.
The comparative evaluation shows differences in patterns of substituting labor across ML, DL, and Generative AI.
Abstract states comparative differences in labor-substitution patterns based on the systematic review of literature; no empirical counts or sizes in abstract.
The comparative evaluation shows differences in scale of impact across ML, DL, and Generative AI.
Abstract reports a comparative evaluation highlighting scale differences across AI phases; no quantitative scale measures given in abstract.
Generative AI brings innovative disruption with profound effects on the structure of employment, knowledge-based ecosystems, and high-skill industries.
Synthesis claim in abstract based on reviewed peer‑reviewed literature; no specific studies, sample sizes, or quantitative effects reported in abstract.
While net-zero targets for 2050 may be achieved, critical emission risks may appear in intermediate years and the EU may compromise its carbon‑neutral goals unless policies adapt to the accelerating digital transformation.
Scenario trajectories from the optimisation model indicating that 2050 net-zero remains attainable in some scenarios but with interim emissions overshoots; policy conclusions drawn by the authors.
After 2030, the geography of AI infrastructure will be shaped more by firm power and system flexibility than by the mere abundance of clean energy.
Modelled spatial deployment outcomes across the 21 AI growth scenarios indicating determinant factors for infrastructure siting after 2030.
The economic consequences of generative AI in financial markets depend critically on institutional context (regulatory and governance capacity).
Synthesis of heterogeneous treatment effects and interaction results across markets with varying governance/regulatory quality in the cross-market panel analysis.
The paper characterises the Glassbox architecture and grounds it in a benefit eligibility scenario, identifying foundational challenges — semantic alignment, dynamic model construction, probabilistic grounding, and human governance — that must be solved to realise it at scale.
Descriptive summary of the paper's contributions and identified research/engineering challenges; based on the authors' conceptual analysis and scenario exposition.
Both risk perception and guilt play a role in GenAI adoption (they are relevant predictors of employees' intention to continue using the technology).
Empirical finding reported from the vignette experiment linking risk perception and guilt to GenAI adoption intention (paper states 'highlight the role of both risk perception and guilt in GenAI adoption').
The effect of embeddedness (GenAI being integrated into internal software environments) on employees depends on the presence of organizational authorization.
Reported empirical result from the vignette experiment indicating an interaction effect between embeddedness and organizational authorization (text states 'the effect of embeddedness depends on the presence of organizational authorization').
This research employed a vignette experiment to investigate how the embeddedness of GenAI and organizational authorization impact employees' negative emotion (specifically guilt) and risk perception.
Stated method in paper: a vignette experiment was used to test effects on guilt and risk perception. (No sample size reported in the provided text.)
The Recuse Signal behaves as a cooperative rather than absolute signal: an explicit operator-authorization framing flips the most capable model to proceed, while other agents continue to defer to the on-host policy.
Observation from the pilot experiment (SSH) with multiple deployed agents (GPT-4o, GPT-4o-mini, Claude Code); experiment included alternate framing where operator authorization was explicit.
The same practice input carries opposite signs depending on whether the environment screens for it.
Synthesis of empirical patterns: in unscreened CF environment AI-style practice predicts smaller rating gains (for non-affiliated users) while in screened ICPC environment it predicts higher non-AI-aided scores.
In open Codeforces contests a stronger AI-style signature predicts smaller rating gains for users with no ICPC/IOI affiliation, but not for those who qualified for the AI-prohibited contests.
Comparative empirical analysis of CF contest rating gains by users' affiliation (ICPC/IOI qualification status) and individual AI-style signature strength; methods likely regression/heterogeneity analysis—sample sizes not reported in abstract.
The paper provides a consolidated, theory‑driven synthesis of the mechanisms through which AI‑mediated platforms simultaneously create opportunities and reproduce disadvantage for women.
Originality/value statement in the paper describing its contribution as a consolidated, theory‑driven synthesis and actionable insights for researchers, policymakers, and platform designers.
National AI development can be interpreted as a controlled balance between information injection and entropy dissipation.
Theoretical mapping using HCLM; paper presents this dynamical framing and definitions of the two processes; no empirical sample.
Advanced economies have integrated AI technologies at scale, while emerging economies such as Algeria face structural and institutional challenges that limit the potential impact of AI on productivity growth.
Asserted in the paper with supporting literature citations (Agrawal et al., 2019; Acemoglu & Restrepo, 2020) and comparative use of World Bank and Oxford Insights indices; no specific sample-size based causal estimate provided.
A safety monitor condition reduces sabotage success, but 56% of participants still accept the malicious code, ignoring its warnings.
Experimental manipulation: one condition included a safety monitor. Authors report that the monitor reduced sabotage success (no absolute reduction magnitude reported here) and that 56% of participants in that context accepted malicious code despite warnings.
These examples show an important shift in the governance of wealth chains – the creation of new forms of infrastructural power through which algorithmic models may become central nodes in tax governance.
Synthesis/interpretive conclusion in the abstract that the illustrative examples imply a governance shift and new infrastructural power; presented as interpretive argument rather than empirically demonstrated in the abstract.
This signals a transformation of the assumed information asymmetries between suppliers, clients, and regulators that sits at the heart of the Global Wealth Chains framework.
Conceptual claim in the abstract linking technological change to shifts in information asymmetries within the Global Wealth Chains framework; presented as interpretive argument rather than supported by reported empirical data in the abstract.
A key development is a move away from deliberate opacity for secrecy purposes into systems that search for the optimal exploitation of legal affordances.
Analytic/interpretive claim made in the abstract about a shift in practices; presented as an argument based on the authors' reflection and examples rather than empirical measurement in the abstract.
Collective practices that emerge in response (from shared prompt strategies to jailbreaking techniques) represent vernacular knowledge formations that, while often exhibiting magical thinking, contain resources for 'revolutionary prompting' and the transformation of individual prompt anxiety into collective political critique.
Qualitative/interpretive claim based on observed user practices and collective responses to LLM behaviour; no systematic survey or sample sizes reported in the abstract.
Aggregate AI metrics (the composite AI Vibrancy Score) obscure heterogeneous pillar-level effects on tourism’s economic contribution.
Comparison of null result for the aggregate AI Vibrancy Score with significant positive effects for specific pillars (R&D, Policy and Governance, lagged Talent) in the same fixed-effects analyses on 33 countries (2017–2023).
Grounding the concept of defensive AI governance in organisation-level evidence from the Global South contributes to debates on platform power, journalistic agency, and AI governance in journalism.
Theoretical/interpretive claim based on the study's case of Al-Masry Al-Youm and its empirical insights; presented as a contribution to scholarly debates. Sample size not reported in the excerpt.
The authors introduce the concept of 'defensive AI governance' to describe how AI adoption is managed through organisational practices of limitation, supervision, and infrastructural self-protection.
Conceptual contribution grounded in organisation-level qualitative evidence from interviews and analysis of Al-Masry Al-Youm's practices; the concept is derived from the study's empirical findings. Sample size not reported in the excerpt.
The newsroom adopts, adapts, and governs AI across data journalism, fact-checking, and generative applications.
Empirical observations and interview data from Al-Masry Al-Youm detailing specific domains of AI integration (data journalism, fact-checking, generative tools). Sample size not reported in the excerpt.
Human and algorithmic actors jointly influence strategic outcomes, motivating the concept of 'hybrid upper echelons' in which executive influence increasingly shifts from making decisions to configuring and governing AI-enabled decision processes.
Theoretical contribution based on integration of management and IS literature in the concept-centric review; proposition of a new conceptual framework ('hybrid upper echelons') rather than primary empirical validation.
AI reconfigures UET through discretion reconfiguration: AI enables delegation and embedding of decision authority, redistributing managerial discretion.
Concept-centric literature review synthesizing studies on delegation/automation of decision authority and managerial discretion (no primary empirical sample reported).
AI reconfigures UET through evaluation reconfiguration: AI partially substitutes human judgment with algorithmic decision logic and thereby shapes how alternatives are evaluated.
Conceptual synthesis from the literature review integrating findings from management and IS studies on algorithmic decision logic and judgment substitution (no primary empirical sample reported).
AI reconfigures upper echelons theory (UET) through cognition reconfiguration: AI mediates information and attention, expanding analytical capacity while introducing new constraints on executive cognition.
Synthesis of management and IS research in a concept-centric literature review; conceptual argument drawing on prior studies about information mediation and attention (no primary empirical sample reported).
AI-mediated financial decisions are reflexive: they reshape organizational workflows, prices, liquidity, credit allocation, and the future data on which subsequent decisions rely.
Conceptual argument supported by literature across finance and related fields (review-level synthesis; no single empirical sample size reported).
Human–AI complementarity in finance is conditional rather than automatic, depending on task structure, private information, feedback quality, incentives, explanation design, and governance.
Synthesis of literature from finance, management, HCI, and AI showing moderating factors for complementarity (conceptual integration; no unified empirical sample size reported).
The paper's contribution includes an estimand distinction, an inspectable ABM/RL mechanism, and a reproducible artifact demonstrating that transparent behavioral assumptions are sufficient to generate gaming-like boundary dynamics without implying that computable regulation is inherently undesirable.
Author-stated contributions in the abstract describing methodological and reproducibility outputs (estimand distinction, inspectable model, reproducible artifact).
AI-flagged complaints are geographically unevenly distributed.
Geographic analysis of AI-flagged complaint shares across jurisdictions using case metadata; authors report uneven distribution.
As a representative of new quality productive forces, brain–computer interface (BCI) technology raises high expectations but also acute concerns about brain‑privacy protection.
Statement in paper's introduction/abstract; conceptual observation based on literature and contextual analysis (no empirical study reported).
AI will have social, economic, and political impacts on work, inequality, democracy and power.
Author's projection of the domains affected by AI (stated as a subject of later chapters; no empirical evidence provided in the excerpt).
The opportunities of AI in human good are real and vast; and the opportunities in human ill, in human society, in human institutions of government, and in the longer term in the environment in which humanity thrives are real and underestimated.
Author's evaluative judgment asserting both substantial benefits and substantial underestimated harms of AI (normative claim without empirical substantiation in the excerpt).
The limitations in the audit reports reflect symbolic compliance (per institutional theory), while stewardship theory highlights potential for deeper accountability.
Theoretical interpretation using institutional theory and stewardship theory presented in the paper (argumentative rather than empirical).
Adaptive governance conditions how AI-driven capabilities translate into sustainability and risk outcomes.
Comparative analysis across the three jurisdictions (China, US, UK, 2022–2025) integrating quantitative indicators and qualitative documentary evidence, with the abstract highlighting the 'conditioning role of adaptive governance'.
Our findings show qualitative and enduring differences between hyperscaler-based platforms and non-hyperscaler providers.
Stated as a conclusion based on the paper's taxonomy and comparative analysis; phrasing indicates interpretive/qualitative evidence rather than longitudinal empirical demonstration (no temporal sample or size reported in abstract).
Non-hyperscaler providers embody distinct value-creation logics beyond hyperscaler efficiency.
Claim arises from the taxonomy and comparative analysis contrasting hyperscaler-based platforms with non-hyperscaler alternatives; evidence appears qualitative and conceptual as presented in the paper summary (no empirical sample size reported in abstract).
No single LLM dominates across engine types, highlighting the importance of specific tasks and tradeoffs between speed and accuracy.
Empirical observation from cross-engine evaluations reported in the paper; descriptive conclusion without numeric dominance metrics or sample sizes in the excerpt.
The evaluations implemented by the initiative demonstrate that AI enabled modeling tools perform better at discussion and basic qualitative tasks than with causal reasoning and quantitative error fixing.
Result reported from the implemented evaluations comparing relative performance across task categories (discussion/qualitative vs causal reasoning/quantitative error fixing); no quantitative effect sizes or sample sizes provided in the excerpt.
When engines from the sd ai project are coupled with different LLMs, their performance on these evaluations reveals variability across different AI tools.
Empirical statement in the paper based on applying the implemented evaluations to different engine+LLM combinations; no numeric performance metrics or sample sizes reported in the excerpt.
We illustrate this transition through examples in consumer markets, education, news, and coding.
Authors state they use sectoral examples to illustrate the framework; this is a claim about the paper's contents rather than an empirical finding.
We offer a three-stage lens: Augmentation, Automation, and Reconstruction.
Conceptual framework proposed by the authors; presented as a taxonomy in the paper (no empirical validation reported in the excerpt).
AI maturity moderated the effects of governance exposure on adaptation (p ≤ 0.035).
Reported moderation analysis: 'with AI maturity moderating these effects (p ≤ 0.035)'.
AI is changing skill requirements—some skills become obsolete and new skills are required.
Paper identifies changing skill requirements as a key area of examination (abstract). This is stated as an asserted trend based on the paper's review rather than a quantified empirical finding in the provided text.