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).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9047 claims
Filter claims →
Productivity
8066 claims
Filter claims →
Governance
7278 claims
Filtered →
Human-AI Collaboration
6912 claims
Filter claims →
Org Design
4439 claims
Filter claims →
Innovation
4359 claims
Filter claims →
Labor Markets
3652 claims
Filter claims →
Skills & Training
3018 claims
Filter claims →
Inequality
2160 claims
Filter claims →
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
Remove filter
FP supports native multi-party organization and event-based collaboration.
Feature/architecture claim in the paper describing native support for multi-party organization and event-driven collaboration; no empirical evaluation or user studies provided.
FP unifies heterogeneous entities, including agents, tools, resources, humans, institutions, and organizations.
Design specification/feature claim in the paper describing FP's data and entity model; no empirical interoperability study reported.
This paper introduces the Foundation Protocol (FP), a graph-first coordination layer for an emerging human-AI society.
Claim of authorship/introduction in the paper; architectural/design proposal rather than an evaluated system.
Agents need to form reliable relationships, organize multi-agent work, exchange value, support an AI economy, and stay safe and accountable under real-world oversight.
Normative/requirements statement in the paper describing necessary capabilities for scaled multi-agent systems; no empirical validation or experimental data provided.
Autonomous agents are moving from tools into a layer of social infrastructure: they browse, purchase, deploy software, manage systems, and increasingly interact with one another.
Statement in the paper's introductory/abstract text presenting an observed trend; conceptual/qualitative claim without empirical data or measured sample.
European AI companies increasingly face differing regulatory expectations across global markets, and European institutions should provide structured support (advisory mechanisms, regulatory guidance, dialogue with partner jurisdictions) to help companies navigate emerging compliance requirements abroad.
Combined descriptive claim and policy recommendation; the text asserts increasing regulatory asymmetry faced by firms but provides no empirical data or firm-level survey evidence.
Systematic monitoring of global regulatory developments (for example through foresight functions within the European Commission or the AI Office) would help anticipate regulatory divergence and support future adjustments to European governance frameworks.
Policy recommendation advocating institutional monitoring mechanisms; argumentative justification rather than empirical demonstration in the text.
European regulators should monitor whether conversational systems begin to assume intermediary or gatekeeping roles within digital ecosystems and consider how existing platform governance frameworks might apply.
Policy recommendation advocating monitoring and potential regulatory application; no empirical study in text demonstrating current gatekeeping behavior.
Risk assessments and auditing standards should explicitly examine interaction design, including engagement optimisation mechanisms, recommendation loops, and other features that may encourage behavioural influence or dependency.
Normative recommendation arguing current frameworks focus mainly on outputs; no empirical evaluation or sample reported.
European institutions (in particular the European AI Office) should issue guidance on how systems designed for sustained social or emotional interaction should be assessed in the implementation of the AI Act.
Policy recommendation contained in the text; prescriptive argument rather than an empirical finding; no supporting data or empirical evaluation provided.
Existing regulatory frameworks will need to consider risks that arise not only from system outputs but also from longer-term patterns of human–AI interaction.
Normative recommendation based on the document's argument that conversational AI generates risks through sustained interaction; no empirical method or data reported.
The paper proposes five evaluation dimensions for AutoResearch systems: novelty, validity, impact, reliability, and provenance.
Paper explicitly proposes these five dimensions as an evaluation rubric; conceptual proposal.
The field can be organized around five workflow conditions: literature and research grounding; hypothesis formation and planning; experimentation and tool use; feedback, validation, and review; and reporting and knowledge communication.
Authors propose this five-condition organizational framework as part of their survey and synthesis; conceptual contribution.
Vibe Research denotes the human-steered region of prompt-based assistance and human-verified execution within AutoResearch.
Paper-introduced terminology and conceptual delineation of a sub-region of the AutoResearch spectrum; definitional statement.
AutoResearch is defined as the developmental spectrum of AI-powered scientific workflow automation.
Paper provides an explicit definitional framing (terminology introduced by authors); conceptual contribution rather than empirical finding.
This shift marks a transition from task-level AI for science to workflow-level research automation.
Conceptual argument backed by literature survey and examples of systems that coordinate multiple research tasks; no single quantitative study reported.
Scientific research is being reshaped by AI systems that move beyond isolated assistance toward longer-horizon workflows spanning literature grounding, hypothesis generation, experimentation, validation, reporting, and revision.
Survey / conceptual synthesis of recent AI research systems and literature; paper presents this as an observed trend rather than reporting original empirical measurements.
CHRONOS achieves a total privacy loss of epsilon = 4.25 at delta = 10^-6 under zCDP composition in the reported experiments.
Reported privacy accounting result in experimental section (zCDP composition).
Measured latency for CHRONOS is 161 ms.
Reported experimental latency metric in paper.
Across the benchmarks CHRONOS attains 2.74 queries per second throughput.
Reported experimental throughput metric in paper.
The paper reports empirical results across four benchmarks showing CHRONOS achieves 0.937 recall at ten (recall@10).
Experimental evaluation across four benchmarks reported in paper (four benchmarks stated).
The paper includes a scalability analysis for 500 sellers (multi-epoch settlement).
Scalability analysis reported in paper explicitly referencing 500 sellers.
CHRONOS releases a privatized affinity matrix per epoch using the Gaussian mechanism; all retrieval and ranking are post-processing and thus incur no extra privacy cost.
System design and privacy mechanism description in paper (Gaussian mechanism; post-processing argument).
Layer three uses EXP3-IX to achieve Big-O(sqrt(T log T)) regret while enforcing (epsilon, delta)-differential privacy via moments accounting.
Theoretical regret bound and privacy-preserving algorithmic design described in paper (EXP3-IX with moments accounting).
Layer two conditions Shapley valuation on detected changepoints and provides finite-sample error guarantees under noise.
Methodological description plus finite-sample theoretical guarantees under noise presented in paper.
The monotone-envelope guarantee in layer one reduces bound looseness to 1.8 to 3.2 times observed loss.
Empirical/theoretical comparison of bound looseness vs. observed loss reported in paper (range reported as 1.8–3.2×).
Layer one of CHRONOS applies neural-ODE temporal decay to shortcut edges and provides a per-query expected recall-loss bound of Big-O(Pq lambda delta t).
Theoretical bound and method description (neural-ODE temporal decay) presented in paper; no empirical sample size stated for the bound itself.
The study advances multilevel propositions and outlines a research agenda for examining legitimacy in hybrid human–AI decision systems.
Paper presents multilevel theoretical propositions and a suggested agenda for future empirical research (conceptual contribution; no empirical validation reported).
Human judgment remains essential for contextual interpretation and accountability in hybrid human–AI decision systems.
Conceptual claim advanced through theoretical argumentation and literature references in the paper (no empirical sample reported).
Legitimacy of AI-enabled decisions depends on transparency, explainability, and perceived fairness.
Conceptual argument and literature synthesis in the paper emphasizing transparency, explainability, and fairness as determinants (no empirical sample reported).
AI enhances efficiency and consistency in organizational decision-making.
Theoretical claim supported by referenced literature and conceptual argumentation within the paper (no empirical test or sample reported).
Procedural, distributive, and cognitive legitimacy are key dimensions of decision legitimacy in AI-enabled organizations.
Conceptual development in the paper drawing on institutional theory, socio-technical systems, and behavioral decision-making; literature synthesis and theoretical argumentation (no empirical sample reported).
Export controls often unintentionally boost China's self-reliance and R&D.
Argument in the paper that restrictions spur domestic substitution and investment in R&D in the targeted country (qualitative/historical reasoning; no quantified estimate provided).
Export controls are strategic tools in U.S.-China AI competition.
Analytical argument in the paper connecting export controls to broader strategic aims in great-power competition over AI; qualitative policy analysis rather than empirical measurement.
Since October 2022, the U.S. Bureau of Industry and Security (BIS) has progressively tightened restrictions on advanced computing components to China.
Factual timeline asserted in the paper referencing BIS policy actions beginning October 2022 (policy documents and announcements invoked).
Controls cover advanced chips, capital, personnel, and critical minerals for semiconductors.
Enumerative claim in the paper listing categories of items and flows targeted by export controls (policy documents and examples cited).
Export controls have become central to U.S.-China tech rivalry, especially in AI.
Policy analysis in the paper citing recent U.S. measures (e.g., BIS actions) and Chinese responses; contextual argumentation rather than a quantitative study.
Export control is a policy and legal tool to protect national interests by regulating exports of sensitive goods and technology to foreign nations.
Descriptive/legal characterization presented in the paper (normative definition and overview of export control regimes).
Accountability assets are complementary assets that make AI-supported outputs legitimate, auditable, reviewable, and assignable to a responsible party.
Conceptual definition and development in the paper; supported by illustrative domain examples but no empirical validation.
Agentic AI orchestrators reduce the interface and assembly costs of composing information systems capabilities across organizational boundaries, seemingly accelerating modularization and organizational disaggregation.
Conceptual/theoretical argument in the paper; theory development and illustrative examples across domains (document processing, legal services, audit, clinical decision support, procurement). No empirical sample or quantitative test reported.
The paper's contribution is to clarify the trade-offs that infrastructure decisions often obscure, distinguish deliberate triad governance from default allocation by market power or regulatory inertia, and propose a Deliberate Triad Choice Framework for policymakers considering AI infrastructure decisions of significant scale.
Stated contributions in the abstract: conceptual clarification, normative distinction between deliberate governance and default allocation, and proposal of a policy framework (Deliberate Triad Choice Framework).
This article develops the AI Infrastructure Triad as a conceptual framework for analyzing three competing priorities in regional AI infrastructure governance: Progress, Sustainability, and Equity.
Theoretical/conceptual development presented in the paper; synthesis of prior work on economic, physical, and moral limits of AI development.
The successful integration of AI-driven EPM systems relies on the synergy between AI technologies and human judgment, allowing healthcare organizations to cultivate a more dynamic, innovative and responsive workforce.
Normative/concluding statement in the scoping review based on synthesis of included studies (n=29).
AI-driven EPM systems mark a significant advance in accessing real-time performance data and provide considerable progression when utilized within appropriate guidelines.
Conclusion drawn in the paper from the scoping review of 29 empirical studies; phrased as an overall assessment.
Predictive analytics help manage high rates of burnout.
Reported in the scoping review as a benefit across included studies (n=29).
Predictive analytics optimize operations.
Stated as an operational benefit in the scoping review (29 studies).
Predictive analytics assist in assessing labor shortages.
Reported use-case in the scoping review synthesizing empirical studies (n=29).
Predictive analytics are vital in orchestrating healthcare organizations’ strategic and operational activities.
Claim derived from the scoping review's conclusions across included studies (n=29).
AI-powered EPM produces significant time savings for managers.
Reported as a benefit in the scoping review synthesis (29 studies); no numerical magnitude given in the excerpt.
AI-powered EPM helps identify potential leaders.
Summarized outcome across empirical studies in the scoping review (n=29).