Evidence (16496 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
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 |
Employees identify ethical issues—particularly transparency and accountability of AI systems—as a notable challenge.
Survey items on ethical concerns analyzed with SPSS (descriptive and reliability analyses).
Employees have concerns regarding data privacy related to AI systems.
Primary survey data using a Likert-scale questionnaire; findings summarized with descriptive statistics and reliability analysis.
Employees report lack of AI-related skills (skill gaps) as a significant challenge to human–AI collaboration.
Survey responses from employees in AI-enabled organizations collected via a structured questionnaire and analyzed (descriptive/correlation).
Employees report fear of job displacement as a notable challenge associated with AI adoption.
Primary survey data (structured questionnaire) capturing perceived challenges; descriptive statistics reported.
The tech industry claims that its products, business models, and methods of resource extraction are unprecedented and fall outside any existing legal framework.
Descriptive claim about prevailing industry discourse referenced by the authors. (Citations or examples of industry statements not included in the excerpt.)
Exploitative working conditions violate workers' rights.
Legal assessment based on documents and the authors' interpretation of rights under applicable law (GDPR and labour rights frameworks). (Specific legal rulings or counts not provided in the excerpt.)
The results of this approach provide legally grounded evidence of the structural disadvantages faced by content moderators in the Global South, whose exploitative working conditions violate workers' rights.
Documents obtained via GDPR requests (employment contracts, NDAs, etc.) and legal interpretation are used as evidence to support claims of structural disadvantage and rights violations. (Specific documents and counts not provided in the excerpt.)
Current alignment approaches are primarily reactive rather than proactive.
Author's critique/characterization of prevailing alignment practice (conceptual observation without quantitative support).
The prevailing paradigm of alignment parallels early psychology's focus on mental illness: necessary but incomplete.
Analogy/argument presented by the authors as a conceptual critique (no empirical test reported).
Existing alignment research is dominated by concerns about safety and preventing harm: safeguards, controllability, and compliance.
Author's literature-level observation / conceptual review in the paper (no systematic review or quantitative coding reported).
Step-wise verification (verifying each stage of the reasoning chain) increases computational overhead and infrastructure requirements when deployed at scale.
Paper's structural trade-off analysis and engineering argument; no measured compute-costs, benchmarks, or sample-size reporting included in the provided text.
Process-based supervision introduces challenges regarding the sustainability of human-in-the-loop feedback loops.
Socio-technical argumentation in the paper—concern raised about ongoing human verification burden; no longitudinal or empirical data on human labor sustainability provided.
Deploying PRMs at scale introduces unique challenges regarding system latency.
Engineering and infrastructure trade-off analysis described in the paper; no measured latency benchmarks or sample-size performance tests provided in the supplied text.
Traditional outcome-based reward models, which evaluate only the final correctness of a solution, often fail to identify logical fallacies or "hallucinations" occurring within intermediate steps.
Theoretical critique and conceptual argumentation presented in the paper; no empirical study or sample size reported.
Capital-intensive sectors face structural constraints on adaptability.
Observed sectoral differences in comparative analysis (e.g., inclusion of ExxonMobil among firms) indicating lower Flexibility Index scores or slower reallocation in capital-intensive firm(s).
Cross-sectoral empirical evidence linking budget flexibility, forecasting accuracy, and institutional oversight remains limited.
Statement of literature gap in paper motivating the study; no new quantitative estimate provided.
Traditional static budgeting models are increasingly inadequate in environments marked by volatility, technological disruption, and fiscal uncertainty.
Framing claim in paper introduction; no specific empirical estimate given. Based on comparative empirical design motivation.
The findings carry direct implications for accountability, institutional integrity, and public trust in urban governance, and contribute to ongoing discourse on responsible AI adoption in cities aligned with global sustainability priorities.
Synthesis of audit results and discussion of their broader implications for public-sector adoption of LLMs in cities; inferential claim based on study outcomes (e.g., errors, fabricated sources, regulatory misinterpretation).
These failures extend beyond technical accuracy and introduce risks for governance, fiscal responsibility, and regulatory compliance.
Interpretation of audit findings (e.g., high rate of unverifiable citations, misinterpretation of regulations, degraded alignment on strategic scenarios) to argue systemic risks in governance and fiscal/regulatory domains.
Many responses misinterpreted regulatory requirements or relied on shallow justification.
Qualitative coding/analysis of LLM responses against expert rubric showing frequent misinterpretation of regulations and superficial reasoning.
Decision alignment with expert judgment degraded as scenario complexity increased, with strong agreement on operational triage but near-complete divergence on strategic capital allocation.
Comparative evaluation of LLM decisions vs. expert rubric across scenarios of varying complexity (operational triage through strategic capital allocation); qualitative and/or quantitative agreement measures reported in paper.
LLM self-reported confidence was negatively correlated with actual reasoning quality (r = -0.23), meaning the lowest-performing models projected the greatest certainty.
Statistical correlation reported between LLM self-reported confidence scores and measured reasoning quality across audited responses/models; correlation coefficient r = -0.23.
Across all models, 51.3% of cited sources were unverifiable or fabricated.
Quantitative audit of citations provided by the six commercial LLMs; proportion of cited sources judged unverifiable or fabricated as reported in paper.
Monte Carlo simulations illustrate that standard DID estimators that ignore spillovers can miss the total effect.
Monte Carlo simulation results reported in the paper comparing standard DID estimators (which ignore spillovers) to the proposed approach; simulations show standard DID can fail to capture the total effect under spillovers.
No existing AI system replicates this: conversational recommenders treat recommendation as a terminal act, while general-purpose LLMs hallucinate product claims and default to generic promotional templates that fail to engage or persuade.
Author assertion/diagnosis comparing existing conversational recommenders and general-purpose LLMs; no empirical comparisons or quantified evaluation provided in the excerpt.
Das Dokument untersucht neuere Daten zur Verbreitung von KI in den G7-Volkswirtschaften, die auf große und anhaltende Unterschiede zwischen KMU und großen Unternehmen hindeuten.
Empirical examination of recent diffusion/adoption data across G7 economies as described in the paper; no sample size or specific datasets provided in the excerpt.
Trotz der jüngsten technologischen Fortschritte bei KI-Tools, sind KMU bei der Einführung von KI im Vergleich zu anderen digitalen Technologien und größeren Unternehmen zurückhaltender.
Statement referencing 'neuere Daten zur Verbreitung von KI in den G7-Volkswirtschaften' showing differences between SMEs and large firms; implies empirical analysis of diffusion/adoption data (no sample size given in excerpt).
The analysis also identifies risks linked to exclusion, symbolic compliance, and concentration of control over compliance processes.
Theoretical risk mapping produced by the integrative review and interpretive synthesis; no primary empirical evidence presented.
Uncertainty around compliance and excessive risk avoidance reduce the space for lawful business activity.
Interpretive synthesis of evidence and arguments across the reviewed literatures (sanctions compliance, institutional voids); no original empirical test.
Firms working under such conditions often experience limited access to finance and markets.
Claim derived from literature on firm constraints in weak institutional/sanctioned contexts as reviewed in the paper; no primary empirical data reported.
Post-conflict and sanctions-affected environments are strongly affected by sanctions pressure, weak rule enforcement, and high levels of corruption risk.
Synthesis of literature on sanctions, weak institutions, and corruption risk presented in the integrative review; no new empirical sample reported.
Accuracy is not a sufficient proxy for governance in regulated AI systems.
Empirical results from synthetic banking experiments showing divergence between task accuracy and governance-quality metrics across architectures, as summarized in the abstract.
Under text-only governance, 27% of deferrals carry no decision-relevant information.
Experimental evaluation in a synthetic banking domain comparing text-only governance to mechanical enforcement; reported statistic in paper abstract. Specific sample size not stated in abstract.
Currently, systematic assessment errors cause owners of lower-valued properties to face disproportionately high tax burdens, creating regressivity in the property tax system.
Empirical analysis of property assessments and tax burdens using 26 million property sales across ~95% of U.S. counties, showing systematic errors that bias tax burdens toward lower-valued properties.
There are limits to technology‑led growth strategies in labor‑abundant contexts; such strategies do not reliably deliver inclusive employment gains.
Argument based on synthesis of theory and comparative field evidence demonstrating weak employment outcomes from technology‑led growth in labor‑abundant settings (no quantitative effect sizes reported).
Digital media play a significant role in shaping youth mobilization and political unrest in migrants' countries of origin.
Empirical observations and regional field evidence reported in the paper linking digital media use to youth mobilization and political outcomes (qualitative/comparative evidence; no numeric sample size provided).
Developing countries face macroeconomic vulnerabilities because of dependence on remittances, which are exposed by automation-driven changes in migrant labor demand.
Analytical linkage developed in the paper supported by comparative field evidence and macroeconomic reasoning; remittance dependence highlighted as a vulnerability (no quantitative estimates or sample sizes reported).
Technology adoption in core industries in advanced economies is linked with labor displacement, rising youth unemployment, and urban labor saturation in South Asia and North Africa.
Geographically grounded framework combined with comparative regional field evidence focused on South Asia and North Africa (qualitative/comparative field data referenced; no numeric sample sizes provided).
AI adoption and accelerating automation amplify employment precarity in labor‑surplus economies.
Conceptual synthesis grounded in economic geography and labor economics, supported by comparative field evidence cited for labor‑surplus contexts (no quantitative sample size reported).
Automation functions as a transnational shock that contracts demand for migrant labor in advanced economies.
Theoretical argument drawing on economic geography, labor economics, and development studies; comparative/regional field evidence referenced in the paper (no numerical sample size reported).
In algorithm-triggered emotional escalations, workers showed lower engagement: they sent fewer messages, contributed a smaller share of total chat rounds, and showed less proactivity in information seeking and solution provision.
Behavioral measures derived from chat logs in the randomized experiment comparing worker actions post-escalation across escalation types; reported differences in message counts, share of rounds, and proxies for proactivity.
Human intervention is less effective in algorithm-triggered emotional escalations (where customers express frustration or dissatisfaction).
Experimental subgroup analysis comparing intervention outcomes for algorithm-triggered emotional escalations versus technical escalations; emotional escalations showed worse post-intervention outcomes.
AI deployment substantially lowers ratings for AI-eligible chats.
Randomized field experiment measuring customer ratings for AI-eligible chats; treated condition (AI + human oversight) produced substantially lower ratings relative to control (humans only).
AI deployment reduces average chat duration.
Randomized field experiment on Alibaba's Taobao platform: workers in treatment supervised an agentic AI resolving AI-eligible chats while handling AI-ineligible chats; control workers resolved all chats without AI. Effect observed on average chat duration in experiment data.
Rather than restoring stability, this cycle intensifies anxiety, undermines mastery, and erodes professional confidence.
Theoretical claim about psychological outcomes from the conceptual reskilling loop; paper provides argumentation but no empirical measurements.
Based on Job Demands–Resources (JD-R) theory and Conservation of Resources (COR) theory, the paper conceptualizes an AI-induced reskilling loop in which ongoing technological change leads to skill erosion, continuous reskilling demands, cognitive and emotional depletion, and reinforced learning as a defensive response to perceived obsolescence.
Theoretical model/loop derived from applying JD-R and COR frameworks; no empirical test or sample reported in the paper.
The paper introduces the concept of 'reskilling fatigue' to explain the human consequences of persistent skill volatility among Established Knowledge Professionals (EKPs).
Conceptual/theoretical contribution presented by the authors; definition and argumentation rather than empirical validation.
Continuous reskilling is widely promoted as a solution to AI-driven disruption, but little attention has been paid to its cumulative psychological costs.
Argument from literature review/observation in the paper; no empirical measurement or sample reported in the paper.
Unless labour law evolves to address digitally mediated control and platform-based asymmetry, the gig economy risks normalising exploitative labour conditions under the guise of innovation and flexibility.
Predictive/theoretical claim based on the paper's synthesis of platform practices, legal gaps, and normative concerns; argued through comparative analysis and conceptual reasoning rather than quantitative forecasting.
The paper uses the concept of 'digital slavery' as a normative framework to describe labour conditions shaped by coercive algorithmic management, absence of bargaining power, and structural precarity.
Conceptual and normative framing within the paper, using the 'digital slavery' metaphor to interpret observed platform labour practices and their implications; theoretical argumentation rather than empirical measurement.