Evidence (7278 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
9047 claims
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Productivity
8066 claims
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Governance
7278 claims
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Human-AI Collaboration
6912 claims
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Org Design
4439 claims
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Innovation
4359 claims
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Labor Markets
3652 claims
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Skills & Training
3018 claims
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Inequality
2160 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 | 795 | 210 | 105 | 955 | 2131 |
| Governance & Regulation | 886 | 414 | 197 | 126 | 1654 |
| Organizational Efficiency | 826 | 204 | 129 | 87 | 1257 |
| Technology Adoption Rate | 681 | 259 | 128 | 110 | 1189 |
| Research Productivity | 464 | 138 | 65 | 349 | 1028 |
| Output Quality | 503 | 196 | 61 | 53 | 813 |
| Decision Quality | 351 | 180 | 84 | 51 | 673 |
| AI Safety & Ethics | 238 | 288 | 71 | 34 | 637 |
| Firm Productivity | 455 | 58 | 92 | 20 | 631 |
| Market Structure | 186 | 172 | 123 | 25 | 511 |
| Task Allocation | 222 | 70 | 76 | 34 | 407 |
| Innovation Output | 238 | 28 | 48 | 18 | 334 |
| Skill Acquisition | 177 | 62 | 62 | 17 | 318 |
| Employment Level | 107 | 57 | 108 | 13 | 287 |
| Fiscal & Macroeconomic | 135 | 72 | 44 | 26 | 284 |
| Firm Revenue | 172 | 50 | 28 | 5 | 256 |
| Consumer Welfare | 121 | 68 | 45 | 12 | 246 |
| Task Completion Time | 183 | 33 | 10 | 13 | 240 |
| Inequality Measures | 45 | 126 | 50 | 6 | 227 |
| Worker Satisfaction | 95 | 74 | 23 | 12 | 204 |
| Error Rate | 77 | 98 | 11 | 4 | 190 |
| Regulatory Compliance | 84 | 73 | 17 | 7 | 181 |
| Automation Exposure | 61 | 61 | 27 | 14 | 166 |
| Training Effectiveness | 98 | 21 | 14 | 19 | 154 |
| Wages & Compensation | 78 | 37 | 25 | 6 | 146 |
| Developer Productivity | 105 | 18 | 14 | 6 | 144 |
| Team Performance | 87 | 17 | 28 | 10 | 143 |
| Job Displacement | 12 | 83 | 23 | 1 | 119 |
| Hiring & Recruitment | 53 | 8 | 8 | 3 | 72 |
| Social Protection | 39 | 17 | 8 | 2 | 66 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 50 | 6 | 1 | 62 |
| Labor Share of Income | 17 | 20 | 17 | — | 54 |
| Worker Turnover | 15 | 15 | — | 3 | 33 |
| Industry | — | — | — | 1 | 1 |
Governance
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The authors develop an interpretable measure of AI-consistent drafting using stylometric AI detection indicators.
Methodological claim: linking case metadata to complaint text and applying stylometric AI-detection indicators to build an interpretable AI-consistent drafting measure.
The increase in pro se filings is especially pronounced in Civil Rights and Other Statutory cases.
Subgroup analyses by case type within the full civil filing dataset; authors highlight stronger increases in these categories.
The study dataset comprises roughly 2.8 million civil filings covering FY2008–2025.
Authors state they use civil filing data from FY2008-2025 and reference ~2.8 million filings.
The federal civil pro se plaintiff rate rose from 11.33% pre-GenAI to 16.94% post-GenAI, a 5.61 percentage-point increase that persists after trend and covariate-adjusted robustness checks.
Analysis of ~2.8 million federal civil filings (FY2008-2025) comparing pre- and post-GenAI periods; authors report trend and covariate-adjusted robustness checks.
GIPs enhance urban industrial chain resilience by promoting industrial structure optimization.
Mechanism analysis in the study showing industrial structure optimization as a channel linking GIP implementation to improved UICR; based on the 281‑city panel and specified empirical tests.
GIPs enhance urban industrial chain resilience mainly by fostering green technological innovation.
Mechanism analysis reported in the paper identifying green technological innovation as a primary mediator through which GIPs improve UICR; based on empirical mediation/analysis within the panel and DML framework.
The resilience‑enhancing effect of GIPs is more pronounced in resource‑based cities.
Heterogeneity analysis reported in the study indicating larger GIP effects on UICR in cities classified as resource‑based; derived from the 281‑city panel analysis.
The resilience‑enhancing effect of GIPs is more pronounced in eastern cities.
Regional heterogeneity analysis reported in the paper showing stronger estimated impacts in eastern region cities within the 281‑city panel.
The resilience‑enhancing effect of GIPs is stronger in cities with stronger AI computing power.
Heterogeneity analysis in the study indicating larger GIP effects on UICR in cities with higher AI computing power measures; based on the same panel dataset and statistical methods.
The resilience‑enhancing effect of GIPs is stronger in cities with more advanced digital economies.
Heterogeneity analysis reported in the paper showing larger estimated impacts of GIPs on UICR in cities with more developed digital economy indicators; based on the 281‑city panel.
The resilience‑enhancing effect of GIPs is stronger in cities with higher openness.
Heterogeneity analysis reported in the study indicating larger estimated effects in subsamples or interaction models for cities with greater openness; based on the 281‑city panel (2005–2022).
The positive effect of GIPs on UICR is robust across alternative sample specifications, estimation algorithms, variable definitions, and controls for parallel policies.
Reported robustness checks in the study (alternative samples, estimation algorithms, variable definitions, and adjustments for parallel policies); based on same panel of 281 cities and DML framework.
The implementation of national Green Industrial Parks (GIPs) significantly improves urban industrial chain resilience (UICR).
Panel data analysis of 281 Chinese cities (2005–2022), treating establishment of national GIPs as a quasi‑natural experiment and estimating effects using a double machine learning approach. Statistical significance asserted in results.
Experts assigned the highest responsibility for addressing these risks to general-purpose AI developers and governance actors (including governments, regulators, and standards bodies).
Delphi ratings of actor responsibility reported in paper: highest responsibility attributed to general-purpose AI developers and governance actors by 272 experts.
Artificial intelligence significantly facilitates carbon mitigation.
Empirical analysis on prefecture-level panel data (2005–2023) showing AI development is associated with reductions in carbon emissions or improved carbon mitigation indicators (authors state 'significantly facilitates ... carbon mitigation').
Artificial intelligence significantly facilitates pollution reduction.
Empirical results from prefecture-level panel analysis (Guanzhong Plain, 2005–2023) report AI development is associated with reductions in pollution indicators (authors state 'significantly facilitates pollution reduction').
Artificial intelligence promotes the growth of urban ecological resilience through the channel of green technological innovation.
Mediation/mechanism analysis using prefecture-level panel data (2005–2023); authors identify green technological innovation as a significant mediating channel in the relationship between AI development and ecological resilience.
Artificial intelligence promotes the growth of urban ecological resilience through the channel of green finance.
Mediation/mechanism analysis in the paper using the same prefecture-level panel data (2005–2023); authors report that green finance is a statistically significant channel linking AI development to higher ecological resilience.
The development of artificial intelligence exerts a positive effect on ecological resilience.
Empirical analysis using prefecture-level panel data for cities in the Guanzhong Plain Urban Agglomeration (2005–2023); authors construct an urban ecological resilience index (three dimensions) and estimate the relationship between AI development and the index using panel econometric methods.
Addressing these issues entails building dynamic evaluation testbeds involving adaptive counterparties, treating institutions as design primitives, and preserving human agency as a structural feature of the systems we build.
Specific prescriptive recommendations listed by the authors as part of the proposed research paradigm; offered as proposed methods rather than empirically validated interventions in the excerpt.
The paper calls for a non-solipsistic research paradigm that treats interdependence as a core design principle rather than approaching cooperation as a task to solve.
Normative/research-agenda claim made by the authors; stated in the paper as a recommended change in research approach without empirical tests.
Closing this gap requires AI that participates in cooperation: the equilibrium-selection process through which multiple actors navigate their interdependence.
Prescriptive/theoretical recommendation by the authors; framed as necessary to address the earlier-claimed train-test-deploy gap, without empirical demonstration in the excerpt.
AI's central challenge is shifting from capability to coexistence.
Author's conceptual assertion in the paper; no empirical data, sample, or experiment reported.
The audit detects significant engagement premiums for three exploitation-related dimensions: performative labor, emotional bait, and privacy violations.
Reported aggregated analysis across labeled dimensions showing positive associations of these dimensions with views; privacy violations mentioned in summary of findings (specific effect size for privacy violations not reported in provided text).
Within-channel analyses indicate median view boosts of +56.0% for performative content (FDR-corrected p < 0.001), with effects holding in same-year robustness checks (p = 0.030).
Within-channel analyses for performative-content label showing median percent boost, FDR-corrected significance, and robustness check restricting comparisons to same-year videos.
Within-channel analyses indicate median view boosts of +65.6% for emotional bait content (FDR-corrected p < 0.001).
Within-channel (fixed-effects or matched) comparisons of emotional-bait-labeled vs. other videos, with multiple-testing correction (FDR); reported median percent boost and p-value.
A mixed-effects regression controlling for channel-level variation shows that a one-unit increase in exploitation score yields a 4.4× increase in views (p < 0.001).
Mixed-effects regression analysis with channel-level random effects on the full video dataset; reported multiplicative effect and p-value.
Exploitation scores correlate with view counts (Spearman ρ = 0.229, p < 10^{-50}).
Spearman rank correlation computed between exploitation scores and view counts across the study dataset (5,051 videos).
A multi-annotator validation study (N=107) shows strong agreement with human judgment: macro-average F1 = 0.911 and high sensitivity for overall exploitation risk (recall = 0.960, F1 = 0.793).
Multi-annotator validation study with 107 human annotations comparing model/weak-supervision labels to human judgments; reported classification metrics.
The effect of BDTA on improving CEE is more significant in enterprises with low market concentration.
Heterogeneity/subsample analysis on the listed manufacturing firm data (2010–2023) showing larger BDTA→CEE effects in firms operating in markets with lower concentration.
The effect of BDTA on improving CEE is more significant in high-tech enterprises.
Heterogeneity/subsample analysis reported on listed manufacturing firms (2010–2023) indicating stronger BDTA→CEE effects among high-tech enterprises.
The effect of BDTA on improving CEE is more significant in non-state-owned enterprises.
Heterogeneity analysis (subsample analysis) reported by authors using the 2010–2023 listed manufacturing firm sample, showing stronger BDTA→CEE effects in non-state-owned firms compared to state-owned firms.
BDTA improves CEE of manufacturing enterprises by enhancing internal control quality.
Theoretical channel analysis and empirical mediation/ mechanism tests on listed manufacturing firms (2010–2023) showing internal control quality is a mediator in the BDTA→CEE link.
BDTA improves CEE of manufacturing enterprises by fostering green innovation.
Theoretical channel analysis plus empirical mediation/ mechanism tests using the same sample (listed Chinese manufacturing firms 2010–2023) that show green innovation mediates the BDTA→CEE relationship.
Big data technology application (BDTA) can improve carbon emission efficiency (CEE) of manufacturing enterprises.
Empirical panel regression analysis on listed companies in China's manufacturing industry from 2010 to 2023; authors report baseline regressions showing a positive relationship between BDTA and CEE.
Together, these measures can properly establish a behavioral‑regulation model for brain‑privacy protection.
Concluding synthesis in the paper arguing that combined measures would yield the proposed regulatory model (normative conclusion without empirical validation).
Implement a 'pre‑market regulatory sandbox + post‑market tracking' regime to manage product risks.
Prescriptive policy design proposed in the paper (conceptual recommendation; no empirical pilot data reported).
Establish a compliance filing‑review mechanism for BCI privacy policies.
Policy recommendation in the paper proposing a procedural compliance mechanism (normative proposal without empirical testing).
Apply the principles of lawfulness, legitimacy, necessity and good‑faith to all brain‑privacy processing.
Policy recommendation formulated in the paper (prescriptive legal proposal; no empirical evaluation included).
A behavioral‑regulation model better reflects the multi‑interest, non‑exclusive nature of brain privacy and balances risk control with innovation.
Normative policy argument and conceptual comparison of regulatory models presented in the paper (theoretical, not empirically tested).
The machines are increasingly becoming competent.
Authorial assertion about the trend in AI capability (no metrics or studies provided in the excerpt).
The concept of co-intelligence describes a new cognitive ecology where the human and artificial minds mutually influence one another to come up with ways of comprehending, creating and making choices that neither of them could accomplish individually.
Conceptual claim attributed to Ethan Mollick (2024) and extended by the author — described conceptually rather than demonstrated empirically in the excerpt.
None of the past technologies have spread into so many aspects of human life, so fast.
Author's comparative assertion about the speed and breadth of AI diffusion relative to prior technologies (no empirical comparison provided in the excerpt).
Artificial intelligence has become a partner in our everyday activities: it dictates our emails, diagnoses our diseases, educates our young children, controls our budgets, creates our artworks, and influences the policies made by governments and corporations.
Authorial assertion listing domains of current AI use (no empirical study or quantified data provided in the excerpt).
The internet had to cope with more or less a decade before it could reach one billion users; social media did it in half times.
Comparative historical adoption claim presented by the author (no citation or empirical method given in the excerpt).
Less than a year after its debut, hundreds of millions of individuals on all seven continents were using large language models, in virtually every field of professional activity, and in most languages.
Authorial assertion summarizing global LLM adoption (no specific study, dataset, or methodology provided in the excerpt).
There were now a hundred million ChatGPT users in two months.
Authorial assertion in the text citing a user-count milestone for ChatGPT (no study or data source provided in the excerpt).
This provocation introduces fiduciary design as a guiding principle and argues that conversational AI trust and accountability could be unified into a single design and legal paradigm.
Proposal/argument presented in the paper (conceptual design + legal framing); no empirical evaluation or implementation data provided in the excerpt.
When a client hires a personal lawyer, undergoes surgery, or receives advice from an investment manager, the expert they consult often has a fiduciary duty to act in their client's best interests; conversational agents should be held to a similar standard.
Analogy to existing professional fiduciary duties used as the core normative argument in the paper; no empirical testing of legal applicability reported in the excerpt.
Conversational AI agents, designed to feel and interact anthropomorphically with human users, must be held to a standard of care commensurate with their capabilities and access.
Normative assertion/proposal laid out in the paper (argumentative reasoning); no empirical test or legal analysis with sample size provided in the excerpt.