Evidence (7560 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 |
Human Ai Collab
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Generative AI-powered tools like ChatGPT are reshaping market skill demands while also offering new forms of on-demand learning support to meet those demands.
Framed in paper as background/motivation; asserted from prior literature and the paper's motivating claims rather than reported as a quantified result in this study.
Susceptibility to visual priming varies across state-of-the-art VLMs.
Comparative experiments run across multiple state-of-the-art vision-language models showing differential changes in IPD behavior when exposed to the same visual primes and color cues. (Paper notes variation in susceptibility and mitigation effectiveness across models; specific model list and per-model sample sizes not given in the abstract.)
Color-coded reward matrices alter VLM decision patterns.
Experimental condition varying the visual presentation of the IPD payoff matrix (color-coding of rewards) and measuring resulting decision patterns of multiple VLMs in IPD trials. (Reported as part of the experimental setup across models; exact counts not provided in abstract.)
VLM behavior can be influenced by image content depicting behavioral concepts (kindness/helpfulness vs. aggressiveness/selfishness).
Experimental manipulation in the Iterated Prisoner's Dilemma (IPD): VLMs were exposed to images labeled/connoting 'kindness/helpfulness' versus 'aggressiveness/selfishness' and subsequent choices in IPD rounds were recorded across multiple state-of-the-art VLMs. (Paper reports experiments across multiple VLMs; exact sample sizes per model/condition not stated in the abstract.)
AI adoption leads both to job displacement and job creation, including the emergence of new occupational categories.
Abstract states the review examines empirical evidence on both job displacement and creation and the emergence of new occupations; no numeric counts or sample sizes provided in abstract.
The study identifies short-term transitional risks and long-term productivity gains associated with AI integration in the workforce.
Abstract states the paper evaluates both short-term risks and long-term productivity gains from AI integration based on the reviewed literature; no empirical quantification given in abstract.
AI-driven automation and augmentation are reshaping employment landscapes, with emphasis on sector-level disruption, skill transformation, and socioeconomic consequences.
Abstract states this as a conclusion of the review drawing on interdisciplinary empirical literature; no specific studies or sample sizes cited in abstract.
The accelerating deployment of artificial intelligence across industries has fundamentally altered the structure of global labour markets.
Statement in abstract summarizing a systematic review of interdisciplinary literature (economics, computer science, organizational behaviour, public policy); no specific sample size reported in abstract.
Failures are structured by task family and execution surface, with HR, management, and multi-system business workflows as persistent bottlenecks and local workspace repair comparatively easier but unsaturated.
Error-mode analysis across the 105 tasks and evaluated models reported in experiments; authors identify task-family-level patterns (HR, management, multi-system workflows) and relative ease of local workspace repair.
Whether LLM-based assistants improve or degrade code quality remains unresolved: existing studies report contradictory outcomes contingent on context and evaluation criteria.
Review finds mixed/contradictory findings across included studies regarding code quality effects.
Differences between models are large enough to shape outcomes in practice, so reliability should be incorporated alongside average performance when assessing and deploying LLMs in high-stakes decision contexts.
Authors' interpretation of empirical differences in funding decisions, scores, confidence, and reliability across models in the controlled experiment; presented as an implication/recommendation.
Demographic characteristics intersect with AI exposure—i.e., exposure varies by demographic groups.
Paper reports that it examines how demographic characteristics intersect with exposure based on recent empirical studies; no demographic breakdowns or sample sizes provided in the abstract.
Recent studies combine task-level exposure metrics with employment and usage data to assess AI exposure and impacts.
Paper notes that it draws on studies that use task-level exposure metrics alongside employment and usage data; methodological claim rather than a quantitative result.
Generative large language models (LLMs) present organizations with a transformative technology whose labor market implications remain nascent yet consequential.
Statement in paper synthesizing emerging empirical research; no specific study, method, or sample size reported in the abstract.
Objectives, constraints, and prompt guidance affect reliability and generalization.
Authors' analysis and discussion based on experiments and ablations described in the paper (qualitative/empirical observations about sensitivity to objectives, constraints, and prompts).
The architect's role is shifting, but the human remains central.
Authors' discussion and interpretive analysis about the role of humans in agentic AI-driven design processes.
Across evolved designs, components often correspond to known techniques; the novelty lies in how they are coordinated.
Authors' qualitative analysis of evolved architectures and components reported in the paper (design inspection and interpretation of evolved solutions).
We identify significant differences between human and AI negotiation behaviors, finding that humans favor lower-complexity deals and are significantly less reliable partners compared to LM-based agents.
Results from the user study comparing human vs LM-based agent negotiation behavior (statements in the results section).
The distribution of complementary (non-AI) skills across the workforce shapes whether AI improvements generate productivity bottlenecks or concentration-driven inequality.
Derived from the task-based model analysis described in the article; framed as a theoretical mechanism with reference to empirical patterns but without specific empirical study details in the excerpt.
The paper extends paradox theory to conceptualise the Creativity Paradox in the context of GenAI.
Theoretical extension and conceptual development within the paper (no empirical tests reported).
Delegating tasks to genAI can be individually beneficial in the short term even as widespread adoption degrades future model performance (creating a social dilemma).
Result of the paper's behavioral model showing an individual-level incentive to use genAI versus a collective cost from adoption (theoretical/model-based; no empirical sample reported in abstract).
ASC (adaptive stopping criterion) halts harmful refinement but incurs a 3.8 pp confidence-elicitation cost.
Reported experiment with ASC showing that it prevents harmful iterative refinement yet causes a measured cost described as 3.8 percentage points due to confidence elicitation.
Only o3-mini (+3.4 pp, EIR = 0%), Claude Opus 4.6 (+0.6 pp, EIR ~ 0.2%), and o4-mini (+/-0 pp) remain non-degrading under self-correction; GPT-5 degrades by -1.8 pp.
Reported measured changes in accuracy (percentage-point changes) and measured EIR values for the named models after applying iterative self-correction across the experiment suite.
Across 7 models and 3 datasets (GSM8K, MATH, StrategyQA), we find a sharp near-zero EIR threshold (<= 0.5%) separating beneficial from harmful self-correction.
Empirical experiments reported across 7 LLMs and 3 benchmark datasets (GSM8K, MATH, StrategyQA) comparing outcomes of iterative self-correction as a function of measured EIR.
AI influences innovation performance in organizations.
Discussion and synthesis of studies and reports on AI adoption and innovation performance presented in the review.
AI adoption is producing organizational implications, including changes in project management practices.
Findings synthesized from conference papers, case studies and industry reports included in the review.
Automation, generative AI, and intelligent systems are reshaping task structures, leading to both job displacement risks and the creation of new AI-driven roles.
Synthesis of empirical studies, conference findings, and industry reports reporting both displacement risks and new role emergence (review paper).
AI is rapidly transforming the nature of work, the demand for skills, and the professional roles of Information Technology (IT) practitioners.
Stated as a synthesis result from a narrative review of recent empirical studies, conference findings, and industry reports (review paper).
AIGC is reshaping the rights and obligations of platforms and workers.
Argument in the paper describing legal and practical impacts of AIGC on platform-worker relationships; based on doctrinal/legal analysis and discussion of platform practices rather than reported quantitative empirical data.
The study explores implications of algorithmic enterprises for competitive advantage, labour markets, and regulatory policy.
Declared scope of the paper in the abstract; exploration is conceptual and analytical rather than reporting empirical findings or quantified effects.
Survey evidence suggests public attitudes towards AI combine optimism with apprehension, and most respondents oppose granting AI systems final authority over hiring and dismissal decisions.
Review cites multiple public opinion and survey studies reporting mixed (optimistic and apprehensive) attitudes and opposition to AI final authority in employment decisions (survey evidence summarized).
There are important regional differences—especially in developing contexts—that necessitate context-specific approaches to improving women’s participation in AI-enabled work.
Observation reported in the review drawing on geographically diverse studies and policy analyses; the abstract does not quantify differences or report sample sizes for cross-region comparisons.
Social, cultural, and ethical considerations influence women’s engagement in AI-centric workplaces.
Claim made in the review, based on interdisciplinary literature that includes sociocultural analyses and ethical discussions; the abstract does not provide empirical effect estimates or sample sizes.
AI applications—ranging from recruitment algorithms to workplace automation—can either reinforce gender disparities or promote equitable employment outcomes.
Stated in the review based on collated findings from multiple studies and analyses that document both harms (e.g., biased recruitment algorithms) and potential benefits (e.g., tools designed to reduce bias); no single empirical study or pooled effect size provided in the abstract.
Artificial Intelligence (AI) is rapidly transforming workplaces across the globe, offering both novel opportunities and unique challenges for women in technology-driven industries.
Stated in the paper's introduction/abstract as a summary conclusion based on a narrative literature review of peer-reviewed studies, policy analyses, and preprint research; no specific sample size or primary empirical method reported in the abstract.
Uncertainty-aware exploration (in algorithms) alters fairness metrics compared to policies that ignore uncertainty.
Results from simulation experiments compare uncertainty-aware exploration policies to baseline policies and report changes in fairness metrics (as described in the abstract and results).
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.
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.
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).
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).
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).
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).
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.