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 |
Operationalizing hardware-based governance must address transition realities including legacy hardware, attestation at scale, and protection of civil liberties.
Policy implementation analysis in the paper identifying practical challenges to deploying hardware-layer controls (conceptual/operational analysis; no empirical trial data provided).
For LLM agents, memory management critically impacts efficiency, quality, and security.
Statement in paper framing and motivation; supported conceptually by literature linking memory design to system properties (no specific experimental details provided in abstract).
The experimental findings are consistent with the paper's theoretical predictions.
Comparison reported in the paper between theoretical model predictions and observed outcomes from the controlled AI-agent trading experiments.
Coding patterns are bimodal: in 41% of sessions, agents author virtually all committed code ("vibe coding"), while in 23%, humans write all code themselves.
Empirical analysis of authorship attribution across the 6,000 sessions in the SWE-chat dataset; percentages derived from session-level classification.
A determinism study of 10 replays per case at temperature zero shows both architectures inherit residual API-level nondeterminism, but DPM exposes one nondeterministic call while summarization exposes N compounding calls.
Determinism experiment with 10 replays per case at temperature zero; qualitative/quantitative observation about number of nondeterministic LLM calls exposed by each architecture.
Advanced prompting methods improve accuracy on inconclusive cases but over-correct, withholding decisions even on clear cases.
Empirical comparison of prompting methods reported in paper: advanced prompts increased accuracy on inconclusive (insufficient-information) cases but led to excessive deferral/withholding on clear cases.
Multi-agent workflows and benchmark evaluation reveal current capabilities, limitations, and research frontiers in agentic AI for physical design.
The paper states it analyzes recent experience with multi-agent workflows and benchmark evaluation; the abstract does not provide specific benchmark names, metrics, or sample sizes.
Effective AI policy mixes are contingent on regional resource endowments and development conditions (i.e., variation across configurations indicates contingency on regional context).
Observed variation across the fsQCA-derived configurations; authors interpret differences as reflecting dependence on regional resources and development conditions.
The study was a preregistered experiment across seven leading LLMs and twelve investment scenarios covering legitimate, high-risk, and objectively fraudulent opportunities.
Methodological description in the paper stating preregistration, 7 LLMs, 12 scenarios; combined dataset included 3,360 AI advisory conversations and a 1,201-participant human benchmark.
There is significant heterogeneity in methodological rigor across studies.
Authors' thematic observation from quality appraisal/extraction noting wide variation in methods, validation approaches, and reporting standards among the 64 studies.
AI is increasingly being integrated into both existing and newly emerging digital infrastructures, altering their architecture, functional role, and strategic significance as these systems begin to operate as embedded cognitive infrastructures shaping knowledge production, decision-making, and institutional processes.
Conceptual and descriptive claim presented by the paper (theoretical analysis/literature-informed observation). No empirical sample size or quantitative methods reported in the provided text.
Hybrid ML+rules systems achieve partial DES-property fillability.
Result of the paper's analytic comparison across the four architectures identifying relative fillability levels for hybrid ML+rules systems.
Artificial intelligence raises the threshold at which refinement adds value.
Theoretical/analytical statement in the paper describing AI's effect on the marginal value of refinement; no empirical quantification provided in the excerpt.
Open-source versus closed-source trade-offs (including deployment architectures and competitive differentiation) are a central strategic consideration when selecting an enterprise LLM approach.
Paper's comparative analysis of open-source and closed-source alternatives and discussion of strategic implications; supported by the Bills Converter design rationale.
AI is associated with a shift toward younger, relatively less educated workers.
Reported association in the paper's baseline empirical results linking AI presence/pervasiveness to changes in workforce composition (age and education).
AI is becoming a geopolitical tool that defines trade, finance, supply chains, surveillance abilities, and diplomatic bargaining power.
Conceptual/qualitative synthesis in the paper's argument; no empirical methods or sample size reported in the abstract.
Variable importance improvements to zero-shot tabular classification produce mixed results with respect to algorithmic fairness.
Authors report experiments applying variable-importance-based adjustments to zero-shot LLM tabular classification and evaluating resulting algorithmic fairness outcomes; described as producing mixed results. (Sample size not provided in abstract.)
Targeted prompt interventions significantly alter the magnitude of market bubbles (they can amplify or suppress bubble size).
Randomized (or otherwise experimentally manipulated) prompt interventions applied to LLM agents in the simulated open-call auction, with resulting differences in measured bubble magnitude reported.
By analyzing agents' reasoning text through a twenty-mechanism scoring framework, targeted prompt interventions causally amplify or suppress specific behavioral mechanisms.
Qualitative and quantitative analysis of agents' chain-of-thought / reasoning text using a 20-mechanism scoring framework; experimental manipulations of prompts reported to change mechanism scores (interpreted causally as interventions on prompts).
Given the results, educators should revisit pair programming as an educational tool in addition to embracing modern AI.
Authors' recommendation in the paper's conclusion based on experimental findings (performance, workload, emotion, retention outcomes).
Both US and Chinese strategies depend on cross-country relationships in AI innovation.
Conceptual assertion motivating the network analysis of international collaborations and citations.
Formal network verification has made substantial progress in proving correctness properties but is typically applied in offline, pre-deployment settings and faces challenges in accommodating continuous changes and validating live production behavior.
Authors' summary of the state of the art in network verification (assertion in paper; no empirical data in abstract).
Overall, the proposed HRL framework improves learning efficiency and scalability, outperforming heuristic baselines while remaining below the perfect-information oracle bound.
Results reported in the paper from simulation experiments comparing the HRL framework to heuristic baselines and the oracle; pairwise differences analyzed (Wilcoxon tests referenced). The paper asserts better performance than heuristics but still worse than the oracle.
The proposed safety-filter outperforms a standalone deep reinforcement learning-based controller in energy and cost metrics, with only a slight increase in comfort temperature violations.
Reported experimental comparison between the safety-filter-enhanced controller and a standalone DRL controller in the paper; specific metrics and sample size not provided in the excerpt.
Results also reveal divergences between the two interaction scenario types.
Abstract statement that divergences vary across different interaction contexts / scenario types.
Results reveal divergences between purely simulated and human study datasets.
Abstract reports that findings diverge between simulation experiments and the human-subjects dataset; comparisons drawn across the two datasets (simulation N=2000, human N=290).
Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) verified correlations among educational background, gender inclusiveness, digital literacy, and perceived algorithmic fairness.
Paper reports use of CFA and SEM to test relationships among those variables; reliability/fit supported by Composite Reliability (CR), Average Variance Extracted (AVE), and model-fit indicators.
Experienced developers maintain control through detailed delegation while novices struggle between over-reliance and cautious avoidance.
Observed behaviors and accounts from the AI-assisted debugging task (10 juniors) and senior participants in ACTA/Delphi and blind review phases (5 + 5 seniors).
AI is not just changing how engineers code—it is reshaping who holds agency across work and professional growth.
Qualitative synthesis of findings across the three-phase study (Delphi with 5 seniors; debugging task with 10 juniors; blind reviews by 5 seniors).
The rapid advancement of artificial intelligence (AI) technologies, particularly generative AI and large language models, has reignited debates about the future of work and the potential for widespread labor market disruption.
Statement in the paper's introduction/abstract citing recent empirical studies, industry reports, and ongoing debates; no original sample or numerical evidence reported in the abstract.
How software developers interact with AI-powered tools, including Large Language Models (LLMs), plays a vital role in how these AI-powered tools impact them.
Based on qualitative analysis of twenty-two interviews with software developers about using LLMs for software development; asserted as a central finding in the paper's analysis.
Outcomes of AI deployment in labor-market settings depend on complementary organizational practices, workers’ access to skills, and the regulatory environment.
Synthesis-derived moderator/ mechanism claim from qualitative analysis of the 19 included studies identifying organizational practices, skill access, and regulation as contextual moderators.
Benefits of technology and data analytics are context-dependent, with emerging markets facing unique regulatory and infrastructural barriers.
Narrative synthesis of included studies noting heterogeneity by context and reports of regulatory/infrastructural constraints in emerging markets.
Cybersecurity has a moderating effect on audit data analytics.
Synthesis statement in the review summarizing included studies that report cybersecurity influences the effectiveness/usability of audit data analytics.
No aggregation mechanism can simultaneously satisfy all desiderata of collective rationality (connection to Arrow's Impossibility Theorem); multi-agent deliberation navigates rather than resolves this constraint.
Theoretical argument connecting empirical multi-agent deliberation results to Arrow's Impossibility Theorem and observations that deliberation trades off competing desiderata rather than achieving all simultaneously.
Alignment systematically shapes negotiation strategies and allocation patterns between agents.
Experimentally comparing negotiation behavior and allocation outcomes across agent pairs where one agent is aligned (via RAG) and the partner is either unaligned or adversarially prompted; patterns of strategy and allocation differences reported.
The design space articulates four configurations—No AI, Hidden AI, Translucent AI, and Visible AI—each trading off among accountability, autonomy, and coordination cost.
Conceptual taxonomy introduced in the paper (design artifact). No empirical evaluation or sample reported in the abstract; tradeoffs are argued theoretically.
Digitization is reshaping the structures of Resource Dependence Theory (RDT) instead of eliminating it completely (Yordanova & Hristozov, 2025).
Conceptual/theoretical claim supported by citation to Yordanova & Hristozov (2025); presented as an interpretive conclusion about how digitization interacts with organizational dependence structures. No empirical details provided in the excerpt.
CLARITI matches GPT-5's resolution rate on underspecified issues while generating 41% fewer questions.
Empirical evaluation comparing CLARITI and GPT-5 on a task set of underspecified software engineering issues; the result reported in the abstract indicates parity in resolution rate and a quantified reduction in questions (41%) but the abstract does not report sample size, test set composition, or statistical significance.
They can produce fluent outputs that resemble reflection, but lack temporal continuity, causal feedback, and anchoring in real-world interaction.
Descriptive claim made in the text contrasting surface-level fluency with missing properties; no empirical data or experiments provided.
A within-subject human study with 20 players and 600 games shows that our interventions significantly improve performance for low- and mid-skill players while matching expert-engine interventions for high-skill players.
Within-subject human experiment reported in the paper: N = 20 players, 600 games total; comparisons of performance under the proposed interventions versus expert-engine interventions.
This work establishes a foundation for understanding how generative AI systems not only augment cognitive performance but also reshape self-perception and perceived expertise.
Paper's stated contribution presenting theory and conceptual groundwork; no empirical validation provided in the abstract.
The LLM fallacy has implications for education, hiring, and AI literacy.
Implications and argumentation presented in the paper; these are prospective and conceptual rather than supported by empirical data in the abstract.
Further research is needed to explore the longitudinal impact of these AI deployments on local labor markets and the creation of indigenous datasets that reflect Cameroon’s unique linguistic diversity.
Authors' identified research gaps and recommendations; statement of future research needs rather than empirical result.
The analysis reveals a non-linear, U-shaped relationship between changes in frontier skill intensity and employment growth.
Statistical linkage of changes in frontier skill intensity (OTSS changes) to employment growth using administrative data from 2012–2023; reported functional form is U-shaped.
Frontier technologies remain concentrated in specialised occupations, while digital technologies are widespread.
Distributional analysis of OTSS across occupations showing concentration patterns of frontier technologies versus ubiquity of digital technologies.
For the average worker in 2023, manual technologies account for the largest share of skill content (42 per cent), followed by digital (38 per cent) and frontier technologies (20 per cent).
Computed OTSS applied to occupation-level data for Germany in 2023; reported shares for the "average worker".
Removing safety layers made the system less useful: structured validation feedback guided the model to correct outcomes in fewer turns, while the unconstrained system hallucinated success.
Qualitative and quantitative comparisons from the deployed evaluation across the three conditions (observations about turn counts, validation-feedback loops, and model hallucinations in unconstrained condition over the 25 scenario trials).
The results show how non-IID data, competition intensity, and incentives shape organizational strategies and social welfare.
Findings from the paper's experiments and analyses that vary non-IIDness, competition intensity, and incentive parameters; no numeric sample sizes provided in abstract.
Outcomes are shaped not only by benchmark quality but also by competitive pressure, including user switching, routing decisions, and operational constraints.
Argument/assertion in paper framing motivations for Marketplace Evaluation; conceptual reasoning listing mechanisms (user switching, routing, operational constraints); no empirical tests or sample size reported.