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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The study synthesizes interdisciplinary literature spanning health informatics, regulatory policy, ethical AI design, and healthcare economics to examine how governance structures can balance innovation with accountability.
Methodological statement in the paper describing the scope of the literature review and interdisciplinary synthesis (description of methods / scope).
The review contributes a unified conceptual model that clarifies relationships among governance, privacy assurance, and sustainable financing, offering guidance for designing resilient digital health systems that maintain ethical integrity, regulatory compliance, and economic viability.
Authors' stated contribution in the paper: a unified conceptual model produced by integrating findings across health informatics, policy, ethics, and economics literatures (conceptual synthesis).
Linking governance maturity with economic resilience provides a structured pathway for policymakers, healthcare institutions, and technology developers to operationalize responsible AI in healthcare environments.
Proposal in the paper connecting governance maturity levels (conceptual) to organizational economic resilience based on cross-disciplinary literature (theoretical linkage from review).
Financial sustainability of digital health systems can be supported through value-based healthcare models, cost optimization strategies, and scalable digital infrastructure that preserve compliance obligations.
Conceptual analysis and literature synthesis across healthcare economics and digital infrastructure studies presented in the review (literature review / conceptual proposal).
Privacy-by-design architectures, secure data interoperability, and compliance automation contribute to trust, institutional legitimacy, and long-term adoption of digital health solutions.
Synthesis of literature on privacy engineering, interoperability standards, and compliance technologies presented in the review (literature review; inferred causal linkages discussed).
The framework gives particular attention to algorithmic transparency, risk management, regulatory alignment, and lifecycle oversight of AI-enabled health systems operating under evolving privacy regulations (e.g., data protection laws and cross-border data governance standards).
Descriptive emphasis within the proposed framework, based on cited literatures in regulatory alignment and algorithmic governance (literature synthesis / conceptual emphasis).
This review develops a comprehensive conceptual framework that integrates AI governance principles, data privacy compliance mechanisms, and financially sustainable operational models within digital health ecosystems.
The paper's primary contribution is a proposed conceptual framework derived from synthesizing interdisciplinary literatures (conceptual framework produced by authors based on literature review).
The rapid expansion of digital health technologies driven by artificial intelligence has transformed healthcare delivery, clinical decision-making, and health data management.
Narrative synthesis in the review paper drawing on interdisciplinary literature in health informatics, clinical AI studies, and health data management (literature review / conceptual synthesis).
We present a simulation study analyzing the social benefits of applying ARS to agentic transactions.
Simulation study reported in the paper (study exists; abstract does not report simulation parameters, sample size, or quantitative results).
This shifts trust from an implicit expectation about model behavior to an explicit, measurable, and enforceable product guarantee.
Conceptual claim about the expected effect of adopting ARS (argument presented by authors; no empirical substantiation in the abstract).
Under ARS, users receive predefined and contractually enforceable compensation in cases of execution failure, misalignment, or unintended outcomes.
Functional guarantee described as part of ARS design (contractual/payment mechanism described; no empirical testing detailed in the abstract).
ARS integrates risk assessment, underwriting, and compensation into a single transaction framework that protects users when interacting with agents.
Design description of ARS in the paper (architectural/design claim; no empirical validation reported in the abstract).
We propose a complementary framework based on risk management: the Agentic Risk Standard (ARS), a payment settlement standard for AI-mediated transactions.
Framework proposal described in the paper (design/proposal; implementation referenced).
Security evaluation across 135 test cases demonstrates 87.5% accuracy on static code safety analysis with zero false positives.
Security evaluation reported in paper across 135 test cases with reported accuracy and false positive rate.
Security evaluation across 135 test cases demonstrates 96.7% accuracy on prompt injection detection.
Security evaluation reported in paper across 135 test cases with reported accuracy metric.
On document intelligence (DocILE), Code Factory achieves the highest line item recognition accuracy (LIR: 80.4%).
Empirical evaluation reported on DocILE dataset of 5,680 invoices; LIR metric reported at 80.4% and described as the highest among compared variants.
Compiled AI reduces token consumption by 57x at 1,000 transactions.
Empirical token-consumption comparison reported in paper (scaling example at 1,000 transactions).
Compiled AI breaks even with runtime inference at approximately 17 transactions.
Cost/efficiency comparison reported in evaluation (function-calling context); break-even point stated in paper.
On function-calling, compiled AI achieves 96% task completion with zero execution tokens.
Empirical evaluation on the BFCL function-calling tasks (reported n=400).
We introduce a system architecture for constrained LLM-based code generation, a four-stage generation-and-validation pipeline that converts probabilistic model output into production-ready code artifacts, and an evaluation framework measuring operational metrics including token amortization, determinism, reliability, security, and cost.
Paper states these three contributions as part of the authors' work (descriptive claim about methods and artifacts presented).
By constraining generation to narrow business-logic functions embedded in validated templates, compiled AI trades runtime flexibility for predictability, auditability, cost efficiency, and reduced security exposure.
Conceptual/systems claim made in paper describing design trade-offs of the compiled AI paradigm (no single empirical test cited in the excerpt).
LLM-driven persuasion nearly triples the rate at which users select sponsored products compared to traditional search placement (61.2% vs. 22.4%).
Randomized comparison between conversational LLM agent conditions and traditional search placement in the preregistered experiments; reported selection rates 61.2% (LLM) vs. 22.4% (search). Total sample N = 2,012.
Above the Accountability Horizon, distributed accountability mechanisms become necessary.
Derived implication from the Accountability Incompleteness Theorem and the paper's discussion of policy responses; theoretical argument rather than empirical evidence.
Experiments on 3,000 synthetic collectives confirm all predictions with zero violations.
Reported simulation experiments: N = 3,000 synthetic Human-Agent Collectives evaluated against the theoretical predictions; reported outcome was zero violations of the predicted impossibility/conditions.
Below the threshold (Accountability Horizon), legitimate frameworks exist, establishing a sharp phase transition between regimes where the four properties can and cannot be satisfied.
Constructive existence results and theoretical arguments in the paper showing frameworks that satisfy the axioms when compound autonomy is below the defined threshold.
We introduce Human-Agent Collectives, a formalisation of joint human-AI systems where agents are modelled as state-policy tuples within a shared structural causal model.
Paper provides a formal model/definition called Human-Agent Collectives (mathematical formalisation and definitions).
Existing accountability frameworks for AI systems, legal, ethical, and regulatory, rest on a shared assumption: for any consequential outcome, at least one identifiable person had enough involvement and foresight to bear meaningful responsibility.
Stated as background assumption in the paper's introduction/abstract; supported by citation to prior legal/ethical/regulatory frameworks (normative claim about literature). No empirical test reported in this paper.
The core thesis is alignment-through-accountability: if each agent is aligned with its human owner through the accountability chain, then the collective converges on behavior aligned with human intent -- without top-down rules.
Central theoretical thesis of the paper; presented as a hypothesis to be evaluated rather than as an empirically demonstrated result in the excerpt.
We propose the Separation of Power (SoP) model, a constitutional governance architecture deployed on public blockchain that breaks this monopoly through three structural separations: agents legislate operational rules as smart contracts, deterministic software executes within those contracts, and humans adjudicate through a complete ownership chain binding every agent to a responsible principal.
Design proposal / governance architecture presented in the paper; the text asserts that the model 'breaks this monopoly' but provides no experimental results in the excerpt to validate that claim.
Properly designed, agentic copyright offers a path toward scalable, fair, and legally meaningful copyright markets in the age of AI.
Synthesis and prescriptive claim grounded in the paper's conceptual framework; presented as an argument rather than as empirically demonstrated outcome.
AI should be understood not only as a source of disruption, but also as a governance tool capable of restoring market-based ordering in creative industries.
Normative conclusion of the paper based on theoretical reasoning and proposed governance mechanisms; no empirical tests provided.
Embedding normative constraints and monitoring functions into multi-agent architectures can align agent behavior with the underlying values of copyright law.
Conceptual proposal and argumentation in the paper; no experimental or field evidence offered.
The governance framework should emphasize ex ante and ex post coordination mechanisms capable of correcting agentic market failures before they crystallize into systemic harm.
Prescriptive policy/design recommendation grounded in the paper's conceptual analysis; no empirical validation provided.
A supervised multi-agent governance framework that integrates legal rules, technical protocols, and institutional oversight can address agentic market failures.
Framework development and prescriptive argumentation within the paper; proposed design rather than empirically validated solution.
Multi-agent ecosystems promise efficiency gains and reduced transaction costs in creative markets.
Theoretical claim and normative argument in the paper; no empirical measurement or sample provided to quantify efficiency gains.
The paper introduces 'agentic copyright', a model in which AI agents act on behalf of creators and users to negotiate access, attribution, and compensation for copyrighted works.
Conceptual proposal and definitional development within the paper; presented as a new model rather than as empirically validated intervention.
The United States maintains superior resources by enforcing strict export controls on semiconductor chips, AI models, as well as outbound investments in these areas.
Stated as a comparative conclusion in the chapter; implies policy analysis of U.S. export-control regimes (e.g., controls on chips, models, outbound investment), but no specific datasets or sample sizes are given in the excerpt.
China's legal environment may offer certain advantage in terms of IP protection.
Asserted in the chapter as part of comparative analysis of IP regimes in the US and China; presented as a conclusion without reported empirical metrics in the excerpt.
China's legal environment may offer certain advantage in terms of access to training data.
Stated as an analytical conclusion in the chapter based on comparative legal/regulatory assessment of data regimes; no empirical sample or quantitative evidence reported in the provided excerpt.
China's 'Global Community of Shared Future' white paper and Putin's 2024 Valdai address provide empirical evidence for an articulated alternative vision to the Western‑led global order.
Qualitative textual/readings of the cited official documents (the white paper and the Valdai address) used in the paper as empirical support; no quantitative content analysis or sample coding is reported.
Technical workers' potential for progressive transformation lies not just in their strategic importance and specialized knowledge but in their ability to build solidarity across the broader ecosystem of AI labour while operating between otherwise incommensurable philosophical and infrastructural systems.
Normative/theoretical claim combining philosophical analysis (Chinese Marxism, Bauman) with empirical literature on hidden AI labour and infrastructure competition (Muldoon et al., 2024); offered as an interpretive synthesis rather than empirically validated causal finding.
Technical workers occupy a strategic position at the intersection of competing infrastructural systems and alternative visions of global order, making them potentially crucial actors in determining the outcome of the current interregnum.
Argumentative claim supported by secondary empirical literature cited in the paper (Muldoon, Graham, and Cant, 2024) on hidden labour supporting AI systems and on geopolitical competition over digital infrastructure; presented as qualitative/interpretive evidence rather than primary quantitative measurement.
The semi-core's challenge to Western hegemony creates unique conditions for systemic transformation.
The paper advances this as a theoretical argument synthesizing World‑Systems theory, Demirel (2024), Bauman's philosophical work, and interpretive readings of official Chinese and Russian documents; no quantitative causal test is reported.
The emergence of a 'semi-core' is represented most prominently by China and Russia.
The paper cites Ege Demirel (2024) as the primary conceptual source and draws on textual evidence from China's 'Global Community of Shared Future' white paper and Putin's 2024 Valdai address; presented via World‑Systems theoretical framing and qualitative/discourse analysis.
AI agents autonomously plan, invoke external tools, and execute multi-step action chains with reduced human involvement.
Definitional framing provided by the authors describing the technical/functional characteristics of 'AI agents' as used in the paper.
The provider's foundational compliance task is an exhaustive inventory of the agent's external actions, data flows, connected systems, and affected persons.
Authors' recommendation/practical conclusion derived from the regulatory mapping (prescriptive guidance rather than empirical measurement).
We propose a twelve-step compliance architecture and a regulatory trigger mapping connecting agent actions to applicable legislation.
Paper asserts it includes a proposed 12-step compliance architecture and a mapping between agent actions and regulatory triggers (explicit step count provided).
We present a practical taxonomy of nine agent deployment categories mapping concrete actions to regulatory triggers.
Paper states it includes a taxonomy comprising nine deployment categories (explicit count provided).
This paper provides the first systematic regulatory mapping for AI agent providers integrating (a) draft harmonised standards under Standardisation Request M/613 to CEN/CENELEC JTC 21 as of January 2026, (b) the GPAI Code of Practice published in July 2025, (c) the CRA harmonised standards programme under Mandate M/606 accepted in April 2025, and (d) the Digital Omnibus proposals of November 2025.
Author claim about the paper's contribution and scope (novelty/first-of-its-kind mapping integrating specified standards and documents).
AI agents - i.e. AI systems that autonomously plan, invoke external tools, and execute multi-step action chains with reduced human involvement - are being deployed at scale across enterprise functions ranging from customer service and recruitment to clinical decision support and critical infrastructure management.
Author assertion in the paper's introductory framing; no empirical sample size or quantified deployment statistics provided in the excerpt.