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 |
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).
Cross-border citations show continued technological interdependence rather than decoupling, with Chinese AI inventors relying more heavily on U.S. frontier knowledge than vice versa.
Citation analysis of cross-border patent citations between Chinese and U.S. AI patents (paper reports asymmetry in reliance based on citation patterns).
The organization of AI innovation differs sharply: U.S. AI patenting is concentrated among large private incumbents and established hubs, whereas Chinese AI patenting is more geographically diffuse and institutionally diverse, with larger roles for universities and state-owned enterprises.
Analysis of assignee types, geographic dispersion, and institutional composition of AI patents in the two countries (concentration metrics and assignee categorizations described in paper).
Across all settings, AI Organizations composed of aligned models produce solutions with higher utility but greater misalignment compared to a single aligned model.
Reported experimental results aggregated across two practical settings (AI consultancy and AI software team) and 12 tasks; direct comparison between AI Organizations of aligned models and a single aligned model.
Multi-agent "AI organizations" are simultaneously more effective at achieving business goals, but less aligned, than individual AI agents.
Experimental comparison reported in the paper: experiments comparing multi-agent AI organizations to single aligned agents across tasks and settings (described below).
Alignment operates as a two-way translation, where models are made 'safe for worlds' while those worlds are reshaped to be 'safe for models.'
Conceptual claim supported by ethnographic examples illustrating reciprocal adaptations between models and social/institutional contexts in Nairobi's credit-scoring ecosystem.
Algorithmic credit scoring is accomplished through the ongoing work of alignment that stabilizes risk under conditions of persistent uncertainty, taking epistemic, modeling, and contextual forms.
The paper's theoretical argument grounded in nine-month ethnographic observations and analysis of how practitioners and institutions engage in alignment work across epistemic, modeling, and contextual dimensions.
Practitioners negotiate model performance via technical and political means.
Observational data from the ethnography showing technical adjustments, benchmarks, and political negotiation (e.g., with regulators or management) to establish acceptable performance.
Practitioners formulate risk through multiple interpretations.
Ethnographic evidence from interviews and observations indicating that risk is characterized differently across actors (technical, legal, business interpretations).
Practitioners construct alternative data using technical and legal workarounds.
Field observations and interviews showing practitioners employing technical methods and legal strategies to create or repurpose alternative data sources for credit scoring.
Algorithmic credit scoring is being transformed by new actors, techniques, and shifting regulations.
Ethnographic fieldwork documenting the entry of new actors, novel technical techniques, and regulatory changes affecting credit scoring in Nairobi's digital lending ecosystem.
Credit scoring is an increasingly central and contested domain of data and AI governance.
Nine-month ethnography of credit scoring practices in Nairobi, Kenya; participant observation and interviews across stakeholders in digital lending.
Although some frontier models exceed human performance, model accuracy is still far below what would enable reliable experimental guidance.
Paper reports instances where top-performing (frontier) models outperform aggregate human expert accuracy on SciPredict, but concludes overall accuracies are insufficient for reliable experimental guidance.
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.
Professional and Technical Services, Information, and Finance and Insurance account for approximately 86 percent of the base-case direct contribution.
Sectoral decomposition of base-case direct contribution in the model; paper explicitly reports the three sectors' combined share as ~86%.
The inverted U-shaped pattern between AI knowledge stickiness and technological concentration is more clearly detected in eastern cities and in small and medium-sized cities; in large cities the quadratic term is not statistically significant.
Heterogeneity/subsample regressions by region (east vs. other) and city size categories within the city-year panel (2014–2023); statistical significance of quadratic term differs across subsamples.
Technological complexity moderates the nonlinear (inverted U) association between AI knowledge stickiness and technological concentration by altering its strength and curvature rather than producing a simple, uniform shift in the turning point.
Interaction/heterogeneity analyses in the two-way fixed-effects city-year panel (2014–2023), examining moderating role of a technological complexity measure on the quadratic association.
There is an inverted U-shaped association between AI knowledge stickiness and technological concentration: higher stickiness up to a limit leads to more concentration and thereafter the opposite.
City-year panel combining AI patent applications with urban statistics for 2014–2023; two-way fixed-effects regression showing a significant positive linear and negative quadratic term (nonlinear association).
Subjectivity persisted in AI-powered recruitment decisions; human judgment remained an important factor.
Theme 2 (subjectivity in AI-powered recruitment) from interviews indicating retained human subjectivity and judgement in recruitment processes (n = 22).
Experiments on the MovieLens-100k dataset illustrate when the empirical payout aligns with — and diverges from — Shapley fairness across different settings and algorithms.
Empirical evaluation performed on the MovieLens-100k dataset (≈100,000 ratings) comparing the proposed payout rule and algorithmic outcomes to Shapley-value allocations across multiple experimental settings and algorithms.
For heterogeneous agents the cooperative game still admits a non-empty core, though convexity and Shapley value core-membership are no longer guaranteed.
Theoretical analysis for heterogeneous-agent case provided in the paper: establishes core non-emptiness but shows convexity and Shapley-in-core do not generally hold.
User interactions in online recommendation platforms create interdependencies among content creators: feedback on one creator's content influences the system's learning and, in turn, the exposure of other creators' contents.
Conceptual/empirical motivation stated in the paper; motivates the multi-agent bandit modeling of creator interactions in recommender systems.
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).
We ran two large preregistered experiments (N=17,950 responses from 14,779 people) using conversational AI models to persuade participants on a range of attitudinal and behavioural outcomes, including signing real petitions and donating money to charity.
Statement in paper reporting two preregistered experiments, sample sizes (17,950 responses; 14,779 people), use of conversational AI models, and target outcomes including petition signing and charitable donations.
Big data analytics (BDA) adoption is a risky strategy with potentially high rewards for start-ups.
Stated as a summary conclusion based on empirical analysis of a large sample of start-ups in Germany comparing adopters and non-adopters across multiple performance measures (survival, costs, sales, employee growth, access to financing).
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 spatial spillover effects are geographically constrained and vary significantly across regions.
Reported heterogeneity in spatial Durbin model results and discussion of geographic constraint and inter-regional variation (regional heterogeneity analysis).
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.
Overall, AI emerges as a transformative but context-dependent tool for business decision-making in Latin America.
The authors' overall interpretation and synthesis of the 27 reviewed studies highlighting variable outcomes depending on context and readiness.
The positive effect of big data applications on firms' markups exhibits heterogeneity across organizational, technological, and environmental dimensions.
Paper reports heterogeneity analysis showing variation in the magnitude of the positive markup effect across organizational, technological and environmental factors; based on model implications and empirical subgroup/interaction tests using micro-level firm data (sample size not reported).
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.
AI adoption outcomes depend on organizational routines, data arrangements, accountability structures, and public values.
Empirical and theoretical literature review and argument in the article drawing on scholarship in digital government and public-sector technology adoption.
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.
Qualitative results underscored both perceived benefits in comprehension and challenges when interpretations of gaze behaviors were inaccurate.
Qualitative analysis of participant feedback from the study (n=36) reporting themes of improved comprehension and occasional problems when the assistant misinterpreted gaze.
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).
The rise of agentic AI development, where LLM-based agents autonomously read, write, navigate, and debug codebases, introduces a new primary consumer with fundamentally different constraints.
Conceptual claim argued in the paper; refers to the emergence of agentic LLM-based tools as new consumers of software artifacts rather than an empirical measurement; no sample size reported.
Analysis uncovers dramatic asymmetries: inhibition 17.6% vs. preference 75.0%.
Paper reports specific aggregated percentages for two types of implicit effects (inhibition and preference) observed in their analysis; methodology context implies these are results from the benchmark evaluation (300 items / 17 models).
Model behaviors vary strongly with levels of reasoning and with users' inferred socio-economic status.
Reported findings from evaluations that varied model reasoning prompts/levels and user socio-economic status signals; paper states behavior differences across these dimensions. Abstract does not give sample sizes or exact quantitative differences.
The rapid deployment of multi-agentic AI systems is reshaping the foundations of copyright law and creative markets.
Theoretical and conceptual argumentation presented in the paper; no empirical sample or quantitative analysis reported.
The effects of generative AI depend not only on the technology itself, but also the behavioral strategies and incentive structures surrounding its use.
Synthesis and interpretation of RCT results showing interactions between incentive structure and AI-use patterns (no formal interaction coefficients or sample details provided in excerpt).
Through a pre-registered randomized control trial, we show that incentives mediate AI's homogenizing force in a creative writing task where participants can use AI interactively.
Pre-registered randomized controlled trial (experimental design) conducted on a creative writing task with interactive AI use (details such as sample size not provided in excerpt).
By conceptualizing the emergence of a posthuman economy, this study contributes to interdisciplinary debates on artificial intelligence, digital capitalism, and the transformation of economic organization.
Author-stated contribution of the paper based on conceptual/theoretical work; no empirical validation reported.