Evidence (3308 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 |
Skills Training
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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.
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.
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".
AI plays a dual role as enhancer and eroder, simultaneously strengthening performance while eroding underlying expertise (the 'AI-as-Amplifier Paradox').
Framing claim presented in the paper's conceptual argument and grounded by the paper's stated year-long empirical study among cancer specialists (no numerical sample size reported in abstract).
The local labor market will follow a dual trajectory: low-skill, routine jobs face high automation risk while demand will rise for AI-collaborative, higher-skill roles.
Paper's analytical prediction based on distinguishing current job roles into routine/repetitive vs cognitive/non-routine and projecting likely impacts; no numeric forecasts or sample sizes provided in the excerpt.
Sensitivity analyses indicate the observed positive belief changes likely reflect recovery from carry-over effects rather than genuine training-induced shifts.
Authors' sensitivity analyses discussed in the paper that examined alternative explanations (e.g., carry-over effects) and concluded the belief-change result is likely due to recovery from such effects.
Simulations demonstrate that standard methods, such as principal components analysis and inverse covariance weighting, can generate spurious cross-study differences, whereas our approach recovers comparable latent treatment effects.
Simulation experiments reported in the paper comparing the proposed method to PCA and inverse covariance weighting; results show PCA and inverse-covariance-weighted estimators can produce spurious cross-study differences while the proposed method recovers comparable latent treatment effects (no simulation sample sizes provided in the abstract).
While AI may reduce certain traditional roles, it also enhances job quality and creates new career pathways within the commerce sector.
Reported finding from the paper's synthesis of existing studies and sectoral observations (qualitative literature synthesis).
AI exhibits a dual nature—both as a disruptor and an enabler of employment in the commerce sector.
Paper-level synthesis of contradictory findings and sectoral patterns reported across reviewed literature (qualitative literature synthesis).
Bounded agents act as an amplifying but not necessary extension to the foundation-model stack for changing work coordination.
Conceptual argument within the paper distinguishing bounded agents from the core stack; no empirical comparison or measurement reported.
The effects of generative AI on work and organisations are heterogeneous and context-dependent, shaped by job roles, skill levels, and institutional environments.
Synthesis across the included studies noting variation in outcomes conditional on role, skill, and institutional context.
Although the concurrent paradigm performs worse than the sequential paradigm in terms of immediate task performance, it is more effective in promoting users' emotional trust.
Comparison between concurrent and sequential AI-assisted decision-making paradigms in the RCT (N=120); authors report concurrent < sequential for immediate task performance, but concurrent > sequential for emotional trust.
If employment losses are relatively small and productivity gains are realised, AI adoption could boost Exchequer revenues. But if job displacement is sizeable, tax receipts fall while welfare spending rises, resulting in potentially large pressures on the public finances.
Conditional fiscal scenarios simulated in the report combining employment, wage and benefit changes with the public finance implications (tax receipts and welfare spending); reported as scenario-based outcomes.
Ireland’s tax and welfare system absorbs most of the income loss for lower income households, and roughly half of the loss for households at the top of the income distribution.
Microsimulation using SWITCH to model taxes and transfers applied to simulated income changes across income groups; reported as a finding in the report.
The productivity decomposition classifies deployments into five regimes that separate beneficial adoption from harmful adoption and identifies which deployments are vulnerable to the augmentation trap.
Model-based taxonomy produced from the analytical decomposition (classification into five regimes described in the paper).
Small differences in managerial incentives can determine which skill path a worker takes (whether they realize full potential or deskill).
Comparative statics / theoretical sensitivity analysis in the dynamic model indicating tipping behavior based on managerial incentives.
Result 3: When AI productivity depends less on worker expertise, workers can permanently diverge in skill: experienced workers realize their full potential while less experienced workers deskill to zero.
Analytical result from the dynamic model showing path-dependent divergence in skill levels under particular parameterizations (lower dependence of AI on worker expertise).
India exhibits a distinctive polarisation pattern: a shrinking middle-skill workforce alongside a persistently large low-skill labour segment.
Descriptive analysis of secondary data and official reports from 2020–2024 comparing occupational and skill distributions in India.
Mathematics (SAFI: 73.2) and Programming (71.8) receive the highest automation feasibility scores; Active Listening (42.2) and Reading Comprehension (45.5) receive the lowest.
SAFI benchmark results reported for specific O*NET skills (numerical SAFI scores provided in the paper).
These results suggest the need for AI model development to prioritize scaffolding long-term competence alongside immediate task completion.
Authors' policy/research recommendation based on experimental findings showing short-term gains but longer-term harms.
These effects are observed across a variety of tasks, including mathematical reasoning and reading comprehension.
Trials included multiple task types (explicitly naming mathematical reasoning and reading comprehension); cross-task analysis reported.
AI adoption significantly reshaped task profiles for 73% of respondents, particularly affecting routine data processing, administrative tasks, and scheduling activities.
Survey data and secondary data analysis reported in this study (sample size not stated); self-reported change in task profiles with reported percentage (73%).
AI adoption across firms is heterogeneous, varying across sectors such as finance, technology, and manufacturing.
Survey of 150 leading Nigerian firms across finance, tech, and manufacturing showing variation in AI integration; supported by qualitative interviews and policy analysis.
The rapid, heterogeneous integration of Artificial Intelligence (AI) technologies is profoundly reshaping the dynamics of work across the Nigerian business sector, generating both significant economic opportunities and acute labor market challenges.
Mixed-methods study combining a quantitative survey of 150 leading Nigerian firms across finance, tech, and manufacturing and qualitative analysis of government policy and workforce interviews.
The influence of human capital (number of specialists in scientific and technological fields) on value added varies across sectors.
Number of specialists in scientific and technological fields included as a covariate in MMQR; reported heterogeneous effects across sectors/quantiles in the results section.
The influence of R&D expenditure on value added varies across sectors.
R&D expenditure included as a core explanatory variable in panel MMQR estimations; authors report differing coefficient sizes/signs across sectors/quantiles.
AI-driven conversational coaching is increasingly used to support workplace negotiation, yet prior work assumes uniform effectiveness across users.
Background claim in paper indicating prior literature trends and assumptions (stated in introduction/motivation).
Participants were clustered into three profiles -- resilient, overcontrolled, and undercontrolled -- based on the Big-Five personality traits and ARC typology.
Paper reports clustering analysis on participants using Big-Five trait measures and ARC typology; clustering result described as three profiles. Total sample reported as N=267.
We conducted a between-subjects experiment (N=267) comparing theory-driven AI (Trucey), general-purpose AI (Control-AI), and a traditional negotiation handbook (Control-NoAI).
Stated experimental design in paper: between-subjects randomized comparison across three conditions with total sample N=267.
Practitioners see the socio-emotional gap not as AI's failure to exhibit SEI traits, but as a functional gap in collaborative capabilities.
Reported interpretation from interview data (10 practitioners) indicating practitioners framed the gap functionally rather than as missing emotional traits.
Big Data-based FinTech can contribute to financial stability only when its implementation is strategically justified, ethically grounded and supported by effective regulation, robust data governance and investment in human capital.
Normative conclusion drawn from systemic and structural analysis of literature and synthesis of empirical studies; no empirical test provided within the paper.
The effectiveness of Big Data solutions varies across the financial sphere and depends critically on data quality, regulatory alignment and organisational readiness.
Derived from comparative analysis of sector-specific applications and synthesis of findings in the reviewed literature; no quantified cross-sector sample reported.
AI intensity and employment elasticity are linked by a U-shaped relationship.
Result reported by the paper based on the authors' empirical/econometric analysis of international datasets (OECD/ILO/World Bank).
The paper analyzes AI as a continuous process using data from the OECD, ILO, and the World Bank to study job displacement, creation, and reallocation.
Empirical analysis described in the paper using datasets from OECD, ILO, and World Bank; econometric approach implied.
AI is recognized as a primary change agent that influences various aspects of economies the world over, and thus it profoundly changes not only the number of jobs but also their quality.
Stated as a high-level conclusion in the paper's introduction/abstract; based on literature synthesis of studies from 2013-2025 and references to international sources (OECD, ILO, World Bank).
Leader emotional intelligence (EI) moderates decision quality, delegation, and managerial communication when generative AI tools (Copilot/ChatGPT) are used in corporate management.
Theoretical EI-moderated human–AI model described in the paper and proposal to test it using a randomized online experiment.
The four-variable account (produced output, underlying understanding, calibration accuracy, self-assessed ability) better explains phenomena like overconfidence, over- and under-reliance on AI, 'crutch' effects, and weak transfer than the simpler claim that generative AI merely amplifies the Dunning–Kruger effect.
Argumentative synthesis in the paper comparing explanatory power of the proposed four-variable framework against the more general Dunning–Kruger metaphor; draws on examples and empirical patterns from the reviewed literature rather than a single empirical test.
A useful working model is 'AI-mediated metacognitive decoupling': LLM use widens the gap among produced output, underlying understanding, calibration accuracy, and self-assessed ability.
Conceptual synthesis and theoretical proposal grounded in reviewed empirical findings from multiple literatures (human–AI interaction, learning research, model evaluation); presented as the paper's working model rather than as a single empirical estimate.
This paper offers a forward-looking framework that emphasizes the decentralizing potential of AI on labor markets, moving beyond the traditional displacement-versus-creation dichotomy.
Paper's stated contribution; based on conceptual framework and synthesis of historical and contemporary analyses (no empirical validation presented in the abstract).
The emergence of artificial intelligence and robotics is catalyzing a profound transformation in the nature of human labor.
Stated as a central premise in the paper's abstract; supported by the paper's synthesis of economic history, contemporary labor market data, and analysis of digital platform growth (no specific datasets or sample sizes reported in the abstract).
AI agents are approaching an inflection point where the binding constraint shifts from raw capability to how work is delegated, verified, and rewarded at scale.
Conceptual argument presented in the paper's introduction/positioning; no empirical data, experiments, or sample reported.
These findings contribute to the literature by providing empirical insights from a developing economy, where unique socioeconomic and institutional factors shape the impact of AI.
Scope/claim of contribution based on the study's context (Cambodia) and its dataset (n = 351).