Evidence (7198 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).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
8921 claims
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Productivity
8002 claims
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Governance
7198 claims
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Human-AI Collaboration
6864 claims
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Org Design
4398 claims
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Innovation
4286 claims
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Labor Markets
3629 claims
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Skills & Training
3001 claims
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Inequality
2141 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 | 790 | 208 | 103 | 950 | 2117 |
| Governance & Regulation | 869 | 411 | 195 | 126 | 1630 |
| Organizational Efficiency | 817 | 202 | 126 | 87 | 1243 |
| Technology Adoption Rate | 675 | 258 | 128 | 106 | 1178 |
| Research Productivity | 462 | 138 | 64 | 347 | 1023 |
| Output Quality | 501 | 193 | 61 | 52 | 807 |
| Decision Quality | 346 | 180 | 84 | 51 | 668 |
| AI Safety & Ethics | 235 | 285 | 70 | 34 | 630 |
| Firm Productivity | 452 | 58 | 91 | 20 | 627 |
| Market Structure | 184 | 171 | 123 | 24 | 507 |
| Task Allocation | 221 | 65 | 76 | 34 | 401 |
| Skill Acquisition | 176 | 62 | 62 | 17 | 317 |
| Innovation Output | 207 | 28 | 48 | 18 | 303 |
| Fiscal & Macroeconomic | 135 | 72 | 44 | 26 | 284 |
| Employment Level | 105 | 56 | 108 | 13 | 284 |
| Consumer Welfare | 121 | 67 | 45 | 11 | 244 |
| Firm Revenue | 160 | 50 | 28 | 4 | 242 |
| Task Completion Time | 182 | 33 | 10 | 13 | 239 |
| Inequality Measures | 45 | 126 | 50 | 6 | 227 |
| Worker Satisfaction | 94 | 73 | 23 | 12 | 202 |
| Error Rate | 76 | 98 | 11 | 4 | 189 |
| Regulatory Compliance | 81 | 73 | 17 | 7 | 178 |
| Automation Exposure | 61 | 59 | 26 | 14 | 163 |
| Training Effectiveness | 97 | 21 | 14 | 19 | 153 |
| Wages & Compensation | 78 | 37 | 25 | 6 | 146 |
| Developer Productivity | 105 | 18 | 14 | 6 | 144 |
| Team Performance | 87 | 17 | 28 | 10 | 143 |
| Job Displacement | 12 | 83 | 21 | 1 | 117 |
| Hiring & Recruitment | 52 | 8 | 8 | 3 | 71 |
| Social Protection | 39 | 17 | 8 | 2 | 66 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 49 | 6 | 1 | 61 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 15 | 14 | — | 3 | 32 |
| Industry | — | — | — | 1 | 1 |
Governance
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Diverse architectural models such as multi-agent systems and cloud-based frameworks enable scalable, adaptive agentic AI deployments in financial services.
Synthesis of architecture-focused studies and framework descriptions within the reviewed literature (architectural benchmarking across papers).
Findings reveal substantial productivity gains and operational efficiencies predominantly in banking and investment.
Systematic review synthesizing multidisciplinary qualitative, quantitative, and bibliometric studies of agentic AI applications in financial services published up to mid-2024 (review-level synthesis).
The study points to the need for longitudinal, experimental, or platform-log-based designs to establish causality and measure heterogeneity across platforms.
Authors' methodological recommendations and proposed empirical agenda built on limitations of their cross-sectional survey (N = 450) and literature gaps.
Policy and practice interventions (media literacy, platform design changes, mandated diversity, etc.) are recommended to increase informational diversity and mitigate polarization.
Policy recommendations derived from study findings and literature discussion; not evaluated experimentally in the paper (authors propose interventions as implications).
Algorithmic recommendation (structural) and user selective consumption (behavioural) jointly reinforce ideological positions in digital spaces.
Interpretation based on observed associations between selective exposure and polarization plus reported heterogeneity in perceived algorithmic influence from the N = 450 survey; authors frame results as indicating interacting structural and behavioural mechanisms.
Higher levels of selective exposure are positively associated with increased ideological polarization.
Correlational analyses (reported associations / regression-style tests) using survey measures of selective exposure and measures of opinion/political polarization in the same cross-sectional sample (N = 450).
A large majority of respondents reported frequent exposure to content aligned with their preexisting views (widespread echo chambers / filter bubbles).
Quantitative cross-sectional survey of N = 450 active social media users; self-reported measures of content consumption and indicators of selective exposure; descriptive statistics showing most respondents frequently encounter ideologically consonant content.
Hukum diharapkan tidak hanya berfungsi sebagai alat perlindungan, tetapi juga sebagai instrumen strategis dalam mengelola transisi menuju masa depan kerja yang lebih inklusif, adil, dan berkelanjutan di era kecerdasan buatan.
Kesimpulan dan rekomendasi normatif penulis berdasarkan analisis perundang-undangan dan literatur yang dikaji.
Pengakuan 'hak atas pengembangan keterampilan berkelanjutan' (right to lifelong learning) penting dan perlu dimasukkan sebagai bagian integral dari perlindungan pekerja di era digital.
Klaim normatif dan rekomendasi kebijakan yang muncul dari studi konseptual dan tinjauan literatur komparatif.
Diperlukan reformasi hukum yang lebih progresif dan adaptif, termasuk penguatan sistem jaminan sosial dan pembaruan kebijakan fiskal untuk menangani dampak AI.
Rekomendasi kebijakan yang disimpulkan dari analisis normatif dan komparatif serta tinjauan literatur dalam penelitian.
Diperlukan dasar hukum bagi penerapan model kompensasi inovatif seperti Universal Basic Income (UBI), pajak otomasi, dan skema distribusi manfaat produktivitas AI.
Rekomendasi kebijakan hasil analisis normatif dan komparatif yang dikemukakan penulis berdasarkan tinjauan literatur.
The case for mutually beneficial industrial policy is stronger for product innovation than for process innovation, because product innovation directly affects demand and triggers stronger network effects while process innovation operates indirectly through supply.
Model variants distinguishing product vs. process R&D within the two-country framework; comparative analysis showing larger demand-driven network effects for product innovation (theoretical model results; no empirical sample).
Under sufficiently strong network externalities and weak substitutability (or weak complementarity) of the goods, industrial policy competition can make both countries simultaneously better off compared to the laissez-faire outcome because of a mutual business-enhancement effect.
Theoretical demonstration within the two-country model: parameter regions (strength of externality, degree of product differentiation) where simultaneous welfare improvements occur relative to laissez-faire (analytical/model results; no empirical sample).
Social security solutions must be adapted to evolving human-technology interactions to secure social justice and cohesion.
Normative conclusion/recommendation from the paper's discussion; advanced as a necessary policy direction without reported empirical validation in the provided text.
Establishing contributory frameworks based on technology-generated income will ensure the sustainability of social protection in the era of labor displacement.
Presented as a novel policy proposal in the paper; stated as a solution with the asserted effect of ensuring sustainability rather than demonstrated via empirical testing or simulation within the text provided.
The Internet of Things (IoT) represents a transformative force, integrating digital intelligence with the physical world and catalyzing new relationships across economic sectors.
Stated as a conceptual assertion in the paper's introduction/overview; presented as a high-level literature-informed claim (no empirical sample or quantitative analysis reported).
Frontier models (Claude Haiku 4.5, GPT-5-chat, GPT-5-mini) achieve statistically indistinguishable semantic closeness scores above 4.6 out of 5.0.
Reported semantic closeness scores from the LLM-as-Judge evaluation on the 15-proposal dataset; the paper states frontier models scored above 4.6/5.0 and were statistically indistinguishable from each other.
Autor et al. (2024) show that the majority of current employment is in job specialties that did not exist in 1940, with new task creation driven by augmentation-type innovations.
Citation reported in the paper summarizing Autor et al. (2024); no sample size provided in excerpt.
Firms may not sufficiently account for non-monetary aspects of technological progress (well-being, safety, quality of work); a planner would include such considerations in steering technological progress.
Normative conclusion based on theoretical analysis comparing firm objective functions (profits) vs social planner objectives (including non-monetary utility).
The planner can raise social welfare by focusing technological progress on making goods cheaper that are disproportionately consumed by relatively poorer agents, thereby raising their real income.
Extension of the baseline model to multiple goods showing distributional gains via composition of price changes (real income channel).
When capital and labor are gross complements, a planner concerned with workers' welfare would favor capital-augmenting innovations to raise wages.
Analytical result from the model analyzing factor-augmenting technological progress and complementarity between capital and labor.
A planner with sufficient welfare weight on workers will impose positive robot taxes, with the tax rate increasing in the planner's concern for workers' welfare.
Application of the baseline model to robot taxation; analytical derivation of optimal robot tax under planner preferences.
As labor's economic value diminishes, steering progress focuses increasingly on enhancing human well-being (non-monetary aspects) rather than labor productivity.
Theoretical discussion and model results in the paper showing planner's shifting objective when labor is devalued.
The welfare benefits of steering technology are greater the less efficient social safety nets are.
Analytical result from the paper's theoretical model comparing a planner who can/cannot perform transfers and evaluating steering as second-best when redistribution is costly.
IMDPs lower ESG rating uncertainty.
The paper constructs measures of ESG rating uncertainty and finds IMDP participation reduces rating uncertainty.
IMDPs reduce greenwashing.
The paper constructs measures of greenwashing and reports that IMDP participation lowers those greenwashing measures.
The positive effect of IMDP participation on ESG performance is stronger in capital-scarce industries.
Heterogeneity analysis by industry capital-scarcity reported in the paper indicating larger IMDP effects in capital-scarce industries.
The positive effect of IMDP participation on ESG performance is stronger for firms at the growth stage.
Heterogeneity analysis by firm life-cycle stage reported in the paper showing larger effects for growth-stage firms.
The positive effect of IMDP participation on ESG performance is stronger for firms under intense competitive pressure.
Heterogeneity analysis reported in the paper that splits the sample by measures of competitive pressure and finds larger effects for firms facing more intense competition.
The effect of IMDP participation on ESG performance operates through improved cost management, consistent with capability upgrading and resource reallocation toward sustainability-related activities.
Mechanism analyses reported in the paper linking IMDP participation to measures of cost management and interpreting this as capability upgrading/resource reallocation.
The effect of IMDP participation on ESG performance operates through higher innovation efficiency.
Mechanism analyses reported in the paper (mediation/decomposition analyses linking IMDP participation to measures of innovation efficiency).
IMDP participation increases ESG ratings by approximately 0.14 rating levels relative to comparable non-participating firms.
Quasi-natural experiment exploiting staggered rollout of IMDPs; propensity score matching combined with a multi-period difference-in-differences design using panel data on Chinese listed manufacturing firms from 2009 to 2022 (as reported in the paper).
The paper studies principal-agent alignment using revealed preference techniques.
Stated methodological approach in the abstract; implies analytical use of revealed-preference methods for identification.
The AI's alignment (similarity of human and AI preferences) can be generically identified in the field setting, where only AI choices are observed.
Analytical/theoretical identification result presented in the paper using revealed preference techniques (as stated in abstract); no empirical sample reported in the abstract.
The AI's alignment (similarity of human and AI preferences) can be generically identified in the laboratory setting, where both human and AI choices are observed.
Analytical/theoretical identification result presented in the paper using revealed preference techniques (as stated in abstract); no empirical sample reported in the abstract.
The paper introduces the Luce Alignment Model, where the AI's choices are a mixture of two Luce rules, one reflecting the human's preferences and the other the AI's.
Paper proposes and defines a new theoretical model (model specification described in abstract).
Human decision makers increasingly delegate choices to AI agents.
Stated as motivation in the abstract; no empirical data or sample described in the provided text.
AI methods improve sustainability disclosure (disclosure to sustainability).
Stated in the review as an outcome of employing AI for ESG analytics and sustainability reporting; specific supporting studies or sizes are not provided in the excerpt.
AI methods improve risk management (managing risk) in sustainable finance.
Claim synthesized from literature reviewed on AI applications in climate risk analytics and risk modeling; no numerical sample details provided in the excerpt.
AI methods improve portfolio management (managing portfolio) in sustainable finance contexts.
Asserted by the review as part of the assessment of AI effectiveness for managing portfolios and risk in sustainable investing; no quantitative sample size or effect estimate reported in the excerpt.
AI methods (including machine learning, natural language processing, predictive analytics) improve ESG measurement.
Paper claims this as a conclusion from its review of studies applying AI techniques to ESG scoring and analytics; no primary sample sizes or effect estimates presented in the excerpt.
AI facilitates the real-time tracking of environmental and social risks.
Claim reported in the paper as a synthesized finding from reviewed literature on AI applications in sustainability and climate/ESG analytics; no numeric sample size provided.
AI drastically enhances the ESG performance analysis, sustainable investment plan, and transparency of the companies.
Statement in the paper summarizing results from a literature review of studies on AI/ML, NLP, predictive analytics, and sustainability reporting (systematic review synthesis). No specific primary study sample size reported in the excerpt.
Robustness checks using clustered standard errors confirm the stability of all key coefficients.
Abstract states robustness checks were performed using clustered standard errors and that these confirm stability of key coefficients (no additional statistics reported in abstract).
Time effects are pronounced, with positive and significant shifts in 2020 (+7.02) and 2022 (+8.10) relative to the baseline year, reflecting acceleration of digital public administration in the post-pandemic period.
Reported time-effect coefficients in the panel specification (years relative to baseline). Abstract gives +7.02 for 2020 and +8.10 for 2022. No p-values shown in abstract but described as positive and significant.
Random effects (RE) models show a positive cross-country correlation between AI readiness and e-government development, with a coefficient of 0.35 (p < 0.001).
RE model reported in abstract for AI readiness (presumably GAIRI) vs EGDI. Reported RE coefficient = 0.35 (p < 0.001). Sample for GAIRI–EGDI reported as 170 countries (2020–2024).
Random effects (RE) models show a positive cross-country correlation between the AI Vibrancy Score and e-government development, with a coefficient of 2.55 (p < 0.001).
RE model reported in abstract for the AIVS–EGDI relationship. Sample for AIVS–EGDI reported as 36 countries (2018–2022). RE coefficient reported = 2.55 (p < 0.001).
Within-country improvements in AI readiness (Government AI Readiness Index) are positively and robustly associated with higher levels of e-government development, with the FE estimate equal to 0.17 (p < 0.001).
Panel data analysis using fixed effects (FE) on the GAIRI–EGDI sample (Government AI Readiness Index vs E-Government Development Index). Reported FE coefficient = 0.17 with p < 0.001. Sample referred to in abstract for GAIRI–EGDI: 170 countries (2020–2024).
With calibrated oversight that aligns accountability to real-world risks, AI can secure the profession’s future.
Normative/prognostic claim in the Article (argument that appropriate governance will preserve or strengthen the legal profession).
With calibrated oversight that aligns accountability to real-world risks, AI can improve service quality in legal services.
Normative/prognostic claim in the Article (argument that governance plus AI yields quality improvements). No empirical effect sizes reported in the excerpt.