Evidence (7198 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
8921 claims
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Productivity
8002 claims
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Governance
7198 claims
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Human-AI Collaboration
6864 claims
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Org Design
4398 claims
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Innovation
4286 claims
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Labor Markets
3629 claims
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Skills & Training
3001 claims
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Inequality
2141 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 | 790 | 208 | 103 | 950 | 2117 |
| Governance & Regulation | 869 | 411 | 195 | 126 | 1630 |
| Organizational Efficiency | 817 | 202 | 126 | 87 | 1243 |
| Technology Adoption Rate | 675 | 258 | 128 | 106 | 1178 |
| Research Productivity | 462 | 138 | 64 | 347 | 1023 |
| Output Quality | 501 | 193 | 61 | 52 | 807 |
| Decision Quality | 346 | 180 | 84 | 51 | 668 |
| AI Safety & Ethics | 235 | 285 | 70 | 34 | 630 |
| Firm Productivity | 452 | 58 | 91 | 20 | 627 |
| Market Structure | 184 | 171 | 123 | 24 | 507 |
| Task Allocation | 221 | 65 | 76 | 34 | 401 |
| Skill Acquisition | 176 | 62 | 62 | 17 | 317 |
| Innovation Output | 207 | 28 | 48 | 18 | 303 |
| Fiscal & Macroeconomic | 135 | 72 | 44 | 26 | 284 |
| Employment Level | 105 | 56 | 108 | 13 | 284 |
| Consumer Welfare | 121 | 67 | 45 | 11 | 244 |
| Firm Revenue | 160 | 50 | 28 | 4 | 242 |
| Task Completion Time | 182 | 33 | 10 | 13 | 239 |
| Inequality Measures | 45 | 126 | 50 | 6 | 227 |
| Worker Satisfaction | 94 | 73 | 23 | 12 | 202 |
| Error Rate | 76 | 98 | 11 | 4 | 189 |
| Regulatory Compliance | 81 | 73 | 17 | 7 | 178 |
| Automation Exposure | 61 | 59 | 26 | 14 | 163 |
| Training Effectiveness | 97 | 21 | 14 | 19 | 153 |
| 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 | 21 | 1 | 117 |
| Hiring & Recruitment | 52 | 8 | 8 | 3 | 71 |
| Social Protection | 39 | 17 | 8 | 2 | 66 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 49 | 6 | 1 | 61 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 15 | 14 | — | 3 | 32 |
| Industry | — | — | — | 1 | 1 |
Governance
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We connect DAO design with digital-democracy research on deliberation and voting, showing how each can advance the other.
Conceptual linkage and theoretical argumentation drawing on literature from DAO design and digital-democracy research; no empirical test or sample described.
We synthesize foundations in blockchains, decentralized autonomous organizations (DAOs), and cryptoeconomics.
Literature synthesis and conceptual review within the paper; no empirical sample or experimental method reported.
We propose DAO-enabled decentralized physical AI (DePAI), a democratic architecture for coordinating humans and autonomous machines in the operation and governance of physical-digital systems.
Conceptual proposal and architectural synthesis presented in the paper (theory/design contribution). No empirical evaluation or sample reported.
Human labor retains premium value when human judgment, attention, accountability, authorship, or relational participation is not incidental to the output but constitutive of what is being purchased (the paper proposes 'constitutive human presence' as the relevant standard for evaluating hybrid human-AI work).
Conceptual definition and prescriptive standard introduced in the paper; no empirical validation or measurement reported in the excerpt.
Because these premiums depend on credible verification, AI governance should treat human-provenance verification systems as labor infrastructure rather than as luxury authenticity labels.
Normative/policy recommendation based on the paper's conceptual analysis; the excerpt contains argumentation but no empirical evaluation of governance interventions.
AI-saturated markets are likely to create Veblen-good premiums, termed human-provenance premiums, for verified human presence (i.e., consumers will pay price premiums for verified human-produced outputs).
Theoretical claim drawing on economic reasoning about Veblen goods and market preferences; paper presents argumentation rather than reported empirical estimation in the excerpt.
This compression reallocates demand for human labor toward work valued for its visible human character (performative humanity), including relational presence, aesthetic provenance, and accountability.
Theoretical/conceptual reasoning and typology proposed in the paper (no empirical sample or measurement reported in the excerpt).
Key research directions—joint capacity planning, multi-timescale control, a compute–power protocol stack, and market innovation—must be pursued to power the future of AI sustainably and reliably.
Prescriptive recommendations in the paper listing research and policy priorities; presented as required directions rather than empirically validated interventions in the excerpt.
The resulting entanglement of compute and power infrastructure requires a shift from implicit coexistence to explicit co-development between the historically decoupled data center and electric power industries.
Normative recommendation and synthesis in the paper arguing that current practices are insufficient and that new collaborative development is needed; based on conceptual reasoning rather than empirical evaluation in the excerpt.
For government policy, it is necessary to establish precise dynamic intervention and orderly exit mechanisms to effectively govern the computing power innovation ecosystem.
Policy implication drawn from the model's analysis of equilibria and regime transitions, and numerical experiments indicating path-dependent/regime-dependent outcomes under different regulatory strategies (method: theoretical model implications + simulation).
A leading computing power incumbent could strengthen its ecological niche and maintain its role as an industry cornerstone by opening its underlying interfaces and software stacks while remaining integrated.
Implication derived from the model's strategic equilibrium analysis and simulations regarding incumbents' strategies for preserving niche/market position (method: evolutionary game analysis + simulations).
Downstream AI firms may benefit from advancing vertical integration, achieving hardware–software co-optimization through self-developed domain-specific architectures.
Result of the theoretical model (tripartite evolutionary game) and numerical simulation experiments showing advantages to downstream innovators when pursuing vertical integration and co-optimization (method: theoretical model + simulation).
AI integration reduces regulatory breaches.
Reported empirical association showing fewer regulatory breaches following AI integration, estimated via System GMM with FE and RE robustness checks (no count data or effect sizes provided in the supplied summary).
AI integration reduces compliance costs.
Empirical results reported in the paper indicate a reduction in compliance costs associated with AI integration, based on System GMM estimation and FE/RE robustness checks (no numeric cost savings provided in the supplied text).
AI integration significantly enhances customer satisfaction.
Paper reports statistically significant positive association between AI integration and customer satisfaction using System GMM and robustness checks (no details on customer satisfaction measurement or sample size in the supplied text).
AI integration significantly enhances risk-adjusted returns.
Reported empirical results using System GMM with FE and RE robustness checks; the paper states statistical significance but does not provide effect magnitudes in the supplied summary.
AI integration significantly enhances operational efficiency.
Same empirical analysis using System GMM, with FE and RE models for robustness (no sample size or numeric estimates provided in the supplied text).
AI integration significantly enhances return on assets (ROA).
Empirical analysis reported in the paper using System Generalized Method of Moments (System GMM) estimator, with Fixed Effects (FE) and Random Effects (RE) models used as robustness checks. (No sample size or test statistics provided in the text supplied.)
Synthesizing evidence, the paper identifies gaps and opportunities in current responsible AI research: (1) to engage with the diverse range of levers that influence organizations to abandon AI development, and (2) to better support appropriate engagement or disengagement with AI system development.
Synthesis and discussion section combining the taxonomy and empirical case analysis to produce research agenda and recommendations.
Decisions taken in earlier stages of development shape which systems are ultimately released, representing potential points for intervention to influence AI deployment outcomes.
Conceptual argument supported by the paper's taxonomy and case analyses showing pre-deployment factors that lead to abandonment.
Academic responsible AI communities often emphasize ethical risks as reasons to not develop AI.
Observation from the scoping review and literature synthesis comparing academic emphases with other sources.
The authors collected data on real-world cases of AI system abandonment via an AI incident database and a practitioner survey to evidence and compare factors that drive abandonment both prior to and following system deployment.
Empirical data collection described in the paper: use of an AI incident database and a practitioner survey; summary does not report sample sizes or survey response counts.
Through thematic analysis of reviewed sources, the paper develops a taxonomy of six categories of factors contributing to AI abandonment: ethical concerns, stakeholder feedback, development lifecycle challenges, organizational dynamics, resource constraints, and legal/regulatory concerns.
Qualitative thematic analysis of the scoping review materials, resulting taxonomy enumerated in the paper; number of documents/sources not stated in the summary provided.
The authors performed a scoping review of academic literature, civil society resources, and grey literature (including journalism and industry reports) to identify factors influencing AI abandonment.
Methods statement in the paper describing a systematic scoping review of multiple source types; no numeric sample size reported in the summary.
The paper reframes AI safety as layered control, authorization, and externally reviewable limits, linking alignment, security engineering, organizational economics, and institutional design.
Synthesis and prescriptive claim based on the paper's theoretical analysis and proposed framework; supported by conceptual integration rather than empirical testing.
The main result is a boundary stabilization theorem showing that safety need not require proving that advanced systems are always correct; instead it requires institutional and technical designs that prevent irreversible power from being released by a single high-efficiency node.
Formal/theoretical claim presented as the paper's primary theorem (a 'boundary stabilization theorem') demonstrated within the paper's formal model.
The frontier for AI-augmented science is not acceleration; it is the redesign of the certifying infrastructure around these new scarcities.
Prescriptive conclusion in the paper arguing priority of institutional redesign over mere speed gains; presented without empirical testing in the excerpt.
Competent-looking judgment, including selecting, ranking, attributing, and certifying, is now produced at scale at marginal cost approaching zero, inverting the dominant economics-of-AI reading that treats judgment as the scarce complement to cheap prediction.
Argumentative/theoretical claim in the paper; no empirical sample, experiment, or quantitative data reported in the excerpt (implicit basis: observation of scalable AI outputs).
HAAS can serve as a pre-deployment workbench for comparing and inspecting human–AI allocation policies before organisational commitment.
Claim about intended use and demonstration of HAAS as an implemented tool; based on the framework implementation and benchmark experiments reported. No deployment-scale evaluation or sample sizes provided in the excerpt.
In manufacturing, stronger governance can improve operational performance and reduce fatigue simultaneously — a workload-buffering effect.
Domain-specific empirical result reported for the manufacturing benchmark in the paper, comparing operational performance and fatigue under different governance strengths. No numeric sample size or effect sizes provided in the excerpt.
Task–agent fit is represented through five auditable cognitive dimensions and a five-mode autonomy spectrum (from human-only to fully autonomous) embedded in a reproducible benchmark spanning software engineering and manufacturing.
Design and benchmark description within the paper; specification of five cognitive dimensions and a five-mode autonomy spectrum and a reproducible benchmark across two domains. No numeric sample size provided.
HAAS combines a rule-based expert system that enforces governance constraints before any learning occurs, and a contextual-bandit learner that selects among feasible collaboration modes from outcome feedback.
Descriptive claim about the implemented HAAS framework as presented in the paper; method description of system architecture (rule-based expert system + contextual-bandit learner). No sample size reported.
KOs transform verification economics: what was previously too costly to verify becomes feasible, enabling accumulated human validation to improve reliability over time.
Theoretical claim about economic and cumulative effects of adopting KOs; no cost-benefit analysis, pilot results, or quantitative evidence reported in the paper.
We propose Knowledge Objects (KOs) — structured artifacts that externalize implicit knowledge into forms humans can inspect, verify, and endorse.
Proposed solution described in the paper; conceptual design and intended properties presented, without reported deployments, trials, or empirical evaluation.
The framework addresses AI-specific challenges including model versioning, human-AI interaction dynamics, contamination and spillover effects, and equitable impact assessment.
Paper lists and provides guidance on AI-specific methodological issues (model versioning, interaction dynamics, contamination/spillover, equity). This is a descriptive claim about topics the framework covers, not an empirical evaluation of solutions.
The framework implements a graded transparency and repeatability framework.
Paper extends TOP-guideline-derived transparency principle into a graded scheme for transparency and repeatability; described as an operational feature of the proposed framework.
The framework integrates heterogeneity analysis and practical significance assessment.
Paper reports inclusion of guidance on analyzing heterogenous treatment effects and assessing practical significance; presented as part of guidelines rather than tested across datasets.
The framework formalizes causal inference through RCT methodology for AI contexts.
Paper states adoption of randomized controlled trial methods and causal inference framing for AI impact evaluation; described as methodological proposition rather than validated application.
Our framework extends prior work by centering evaluation on human performance rather than model output alone.
Paper claims a conceptual shift: focus on human performance metrics; supported by argumentative rationale and literature references rather than empirical demonstration.
The principles and guidelines serve three key roles for AI evaluation RCTs: a design tool for planning studies, an evaluation rubric for assessing existing work, and a blueprint for standard setting as the field converges on norms.
Paper's stated intended uses/positioning of the framework; presented as roles in the discussion/positioning section rather than empirically validated roles.
We operationalize all five principles into 33 guidelines adapted for AI evaluation RCT contexts, expressed as requirements with rationales, implementation instructions, and evidence bases.
Paper reports a concrete output: 33 guidelines derived from the five principles, with each guideline presented as requirement + rationale + implementation instructions + evidence base (documented in paper content).
The paper adopts the (Shadish et al., 2002) four-validity framework and extends it with a fifth principle on transparency, repeatability, and verification adapted from the Transparency and Openness Promotion (TOP) Guidelines (Center for Open Science, 2025).
Explicit methodological choice described in the paper: adoption of Shadish et al. four-validity framework and addition of a transparency/repeatability principle based on TOP Guidelines; documented in the text as design decision.
The framework draws on established experimental practices from disciplines with established RCT traditions, including software engineering, economics, clinical and health sciences, and psychology.
Paper reports literature review and cross-disciplinary synthesis as the methodological foundation for the framework (references to those disciplines). No empirical cross-disciplinary experiment reported.
This work establishes a foundational framework for standardizing AI evaluation RCTs (sometimes called human uplift studies).
Paper's stated contribution: development of a conceptual framework integrating RCT design principles for AI evaluation. Based on literature synthesis and methodological argumentation rather than empirical testing.
The paper introduces a Specification Governance Model (SGM), grounded in Transaction Cost Economics, and provides a practical governance decision guide.
Conceptual/modeling contribution described in the paper: SGM grounded in TCE with an applied decision guide (theoretical plus prescriptive).
The paper proposes the AI-Augmented Methodology Taxonomy (AAMT), classifying six methodologies under three AI integration tiers.
Conceptual contribution: taxonomy introduced and described in the paper (six methodologies, three tiers).
Telemetry across 10,000+ developers shows a 98% increase in pull requests.
Observational telemetry data aggregated across >10,000 developers reported in the paper; metric reported is percent increase in pull request count.
Controlled studies report 20-56% productivity gains on well-scoped tasks.
Aggregate of multiple controlled experimental studies cited in the paper (2022–2026); reported as observed productivity improvements on well-scoped tasks in those studies. Specific study-level sample sizes not reported in the claim text.
The paper proposes five forms of online and offline issuance of RSDM, providing a prototype for creating a globally recognized modern honest money.
Authors' stated contribution in the paper (enumeration of five issuance forms and provision of a prototype); the excerpt explicitly refers to 'five forms'.
RSDM is an innovative version of Jiaozi (a deposit receipt for base metal coin that emerged in Sichuan, China, about a thousand years ago).
Comparative/analogical claim by the authors linking the proposed design to a historical instrument; no empirical analysis provided in the excerpt.