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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Redeemable Self-Decaying/Devaluing Money (RSDM) is a tokenized commodity money whose essential innovation is to fill the hole in the storage fee of metal coins through the self-devaluing of metal weight recorded on the deposit certificate (warehouse receipt) of metal coins.
Design/specification proposed in the paper (conceptual mechanism); no empirical evaluation or sample size reported in the excerpt.
When AI acts as an agent for cross-border capital pool and cross cyclical asset allocation, it needs a sound money that can resist the depreciation of fiat currency and store long-term value.
Theoretical argument in the paper about functional requirements of AI agents managing cross-border capital; no empirical sample reported in the excerpt.
In the AI world, however, the medium of exchange tends to be a globally recognized currency.
Author's theoretical assertion / forward-looking claim in the paper; no empirical data or sample provided in the excerpt.
There is an urgency to implement measures to promote digital inclusion, equitable AI development, investment in education, and international cooperation to spread the benefits of AI more widely and equitably.
Normative/recommendation in the paper based on its analysis of global disparities and risks; no policy evaluation or impact estimates provided in the excerpt.
High-income regions are pioneers in the implementation of AI.
Assertion in the paper based on cross‑regional comparison of AI implementation (no specific metrics, methods, or sample size provided in the excerpt).
High-income regions (North America, Europe, parts of the Asia-Pacific region) have virtually complete access to the Internet.
Statement in the paper based on a global comparative analysis of internet access across regions; the excerpt does not report specific data sources, methods, or sample size.
The practical aim is to help strategic leaders and system designers recognize the configuration at work, notice when it shifts, and judge whether it fits the decision before them.
Stated aim/objective of the paper (normative guidance; conceptual).
The framework introduces 'co-adaptability'—the capacity of a configuration to improve as human and non-human participants adjust together—and situates it within 'heterogeneous teaming' where participants may vary by number, substrate, model architecture, capability, speed, memory, and form of participation.
Conceptual/theoretical introduction of new constructs (co-adaptability and heterogeneous teaming) in the paper; definitional rather than empirical.
The five positions serve as landmarks that help leaders recognize configurations as they layer, drift, or change in a single decision.
Normative/conceptual claim supported by the framework; no empirical validation or sample provided in the excerpt.
The spectrum focuses attention on where leadership work occurs: who frames the problem, who redirects the work, and who can answer for what follows.
Conceptual argument in the paper describing the axes/criteria of the spectrum (theoretical/thematic analysis; no empirical data reported).
This paper offers a leadership-facing spectrum to see human–AI decision relationships with five positions: Pure Human, Centaur (human-dominant, with AI in the loop), Co-equal, Minotaur (AI-dominant, with humans in the loop), and Pure AI.
Conceptual presentation in the paper: a theorized five-position spectrum (no empirical sample or experiment reported).
The paper formalizes these limitations, addresses four alternative views, and proposes a co-existence solution plus a call to action for system builders, benchmark designers, and the memory community.
Meta-claim about the paper's content: formalization, rebuttals, and recommendations stated in the abstract; no empirical sample reported in abstract.
Complementary Learning Systems (CLS) theory shows biological intelligence solved this problem by pairing fast hippocampal exemplar storage with slow neocortical weight consolidation.
Appeal to established neuroscience theory (CLS); the paper draws on CLS literature to justify the two-system solution in biology; no new empirical sample reported in abstract.
We evaluated fidelity, calibration, cost, and gaming vulnerability of the proposed attribution approach across more than 400 configurations.
Empirical experimental section of the paper reporting evaluation across >400 model/configuration runs (paper text: 'more than 400 configurations').
Gradient-based attribution on gridded GFS analysis inputs is a viable candidate value signal for individual sensor contributions.
Experiments reported in the paper applying gradient attribution to gridded GFS analysis inputs; methodological evaluation described.
Differentiable AI weather models can be utilised to fill the gap between data-quality methods and adjoint-based data valuation, providing a practical value signal.
Methodological proposal and motivation in the paper; supported by subsequent computational experiments using differentiable AI weather models.
Large-scale IoT weather sensing networks require incentive mechanisms to sustain participation.
Position/assertion in introduction and motivation section of the paper (conceptual argument; no empirical sample reported).
This work offers a principled foundation for autonomous AI agents that govern themselves the way humans do: not because rules are imposed upon them, but because deliberation is embedded in how they think.
Concluding claim summarizing the proposed framework's conceptual contribution (theoretical/architectural claim; not an empirical measurement).
Implemented on a production-grade retail supply chain workflow, the framework produces zero false escalations to human oversight.
Empirical implementation on a production-grade retail supply chain workflow reported in the paper (claim stated without sample size or measurement protocol in the abstract).
Implemented on a production-grade retail supply chain workflow, the framework achieves 95% compliance accuracy.
Empirical implementation on a production-grade retail supply chain workflow reported in the paper (no sample size or evaluation details provided in the abstract).
We formalize a Pre-Action Governance Reasoning Loop (PAGRL) in which agents consult a four-layer governance rule set: global, workflow-specific, agent-specific, and situational before every consequential action.
Methodological contribution described in the paper (formalization of a governance loop and four-layer rule hierarchy; no numerical sample given in the abstract).
We propose a neurocognitive governance framework that formally maps this human self-governance process to LLM-driven agent reasoning, establishing a structural parallel between the human brain and the large language model as the cognitive core of an agent.
Theoretical framework and formal mapping presented in the paper (design/proposal rather than empirical validation).
Before acting, humans engage deliberate cognitive processes grounded in executive function, inhibitory control, and internalized organizational rules to evaluate whether an intended action is permissible, requires modification, or demands escalation.
The paper's framing draws on cognitive/neurocognitive literature about human self-governance (presented as background/theoretical justification; no new empirical human-subject data reported in the abstract).
The review ends with policy recommendations to address barriers and to facilitate increased public–private partnership (PPP) aimed at increasing health access in India.
Statement in the paper summarizing its policy recommendations; based on authors' synthesis of reviewed literature and conclusions.
AI, Blockchain, and 5G have great potential for transforming healthcare in India.
Forward-looking claim in the review summarizing technological potential as reported in the literature; presented as potential rather than demonstrated effect (no empirical effect sizes given).
These technologies can optimize workforce output in constrained healthcare contexts.
Review assertion synthesizing qualitative and quantitative literature describing impacts on workforce productivity/output; no specific sample size reported in excerpt.
These technologies can increase clinical effectiveness.
Claimed potential in the review based on prior studies (synthesis of evidence; no single quantified trial/sample provided in excerpt).
These technologies can be used to enhance operational effectiveness in healthcare organisations operating under severe constraints.
Review claims and discussion of use-cases/ways technologies may improve operations; based on synthesis of qualitative and quantitative studies (no single trial/sample reported).
Healthcare technology is considered a key organizational-efficiency enhancer, particularly in traditional [healthcare] settings addressing escalating health needs.
Synthesis statement from the review summarizing prior papers that view technology as improving organizational efficiency in traditional settings; method = literature review (qualitative/quantitative studies synthesis).
India has a vast population, meaning a vast market for healthcare technology adoption.
Statement in paper's introduction/abstract asserting India's large population makes it a large market; based on literature review/contextual framing (no primary sample size reported).
The capacity to create, maintain, and control digital agents becomes a new axis of international inequality, potentially devaluing the demographic dividend of developing countries and revising the logic of comparative advantages.
Geoeconomic theoretical analysis in the paper; no cross-country empirical analysis demonstrating changed comparative advantages presented.
The institutional architecture of modern societies (pension systems, taxation models, etc.) is built on assumptions that are systematically undermined by the rise of an agentic economy, necessitating a revision of fiscal and social models, including discrete taxation of algorithmic employment.
Normative and theoretical analysis linking institutional assumptions to agentic economy dynamics; no empirical policy evaluation or fiscal simulation results reported.
The agent energy profile (AEP) is introduced as a measure of annual energy consumption per unit of cFTE, allowing energy-based comparisons between algorithmic and human cognitive labour.
Methodological/conceptual proposal in the paper; no empirical measurements or energy accounting dataset provided.
The paper proposes a quantitative identification of algorithmic agents via the category of cognitive full-time equivalent (cFTE), enabling comparison of algorithmic and human productivity within a unified analytical framework.
Methodological proposal (definition and proposed use of cFTE) presented in the paper; no empirical validation or implementation sample reported.
The ontological status of technology is transforming from a productivity-enhancing tool to an autonomous participant in economic processes, forming a hybrid factor of production that combines characteristics of both capital and labour.
Theoretical analysis and conceptual framing in the paper; no empirical factor decomposition or production-function estimation provided.
Institutionalising digital agent registration could transform 'shadow demographics' into formal 'algorithmic demographics'.
Policy/theoretical proposition in the paper (institutionalisation as a mechanism); no empirical pilot or legal implementation evidence reported.
The concept of 'shadow demographics' describes a growing algorithmic population that expands in parallel with the stagnation or decline of the human population.
Conceptual definition and theorised dynamics in the paper; no empirical counts or longitudinal measurements of algorithmic population provided.
The expanding role of digital agents in production and market processes creates the preconditions for a gradual decoupling of demographic dynamics from economic growth.
Argumentative/theoretical exposition in the paper; no empirical panel or cross-country time-series evidence reported in the text provided.
AI-based digital agents can be interpreted as functional equivalents of economic actors.
Theoretical and conceptual argument presented in the paper (conceptual interpretation; no empirical sample or quantitative validation reported).
These findings have important implications for website visibility, the effectiveness of generative engine optimization techniques, and the information users receive; we call for revenue frameworks to foster a sustainable and mutually beneficial ecosystem for publishers and generative search providers.
Synthesis and recommendations based on the empirical findings above (differences in retrieval, crawler-blocking effects, AIO prevalence and stability) leading to policy/revenue recommendation.
Generative search engines are significantly more likely to retrieve Google-owned content.
Domain/source analysis across the benchmark showing generative outputs (AIO/Gemini) include a higher share of Google-owned domains/content than traditional search results.
Traditional Google search is significantly more likely to retrieve information from popular or institutional websites in government or education.
Domain classification of results returned by traditional Google search across the benchmark (11,500 queries) showing higher proportions of gov/edu/institutional domains compared to generative outputs.
For 51.5% of representative, real-user queries, AI Overviews (AIOs) are generated and are displayed above the organic search results.
Empirical crawl/measurement across the 11,500-query benchmark comparing Google's search results and AIO outputs; counted fraction of queries with an AIO shown above organic results.
New tendencies in managerial AI research and practice include explainable AI, human–AI collaboration, knowledge management, enterprise analytics, and algorithmic management.
Descriptive finding from the paper's literature synthesis (topics emphasized in the review); no quantitative prevalence or counts provided in the abstract.
Machine Learning and Deep Learning enhance employee productivity, business intelligence, process mining, and data-driven decision-making by enabling prediction, perception, and adaptive learning solutions.
Claim synthesized in the review from multiple studies identified via PRISMA screening; abstract does not list the number or identity of underlying empirical studies.
AI-based technologies can greatly enhance managerial efficiency by automating repetitive activities, improving resource allocation, enabling intelligent scheduling, and supporting predictive modelling and strategic planning.
Summary conclusion from the paper's literature review (PRISMA methodology referenced); no quantitative meta-analytic effect sizes provided in abstract.
Machine Learning, Artificial Intelligence, and Deep Learning are tools that can optimize managerial decisions, enable intelligent automation, streamline workflows, and improve organizational performance.
Synthesis claim from the paper's PRISMA-based literature review (no numeric sample size reported in the abstract).
The paper ends with strategic suggestions to foster inclusive growth and orchestrate disruption, contributing evidence-based insights to the future of work in Africa.
Description of the paper's conclusions/recommendations drawn from its systematic review; represents the paper's stated contribution rather than an empirical claim about external data.
The technologies are capable of raising productivity.
Synthesis from the paper's systematic review indicating productivity gains associated with AI/automation in the literature; no quantified meta‑analytic estimate provided in the summary.
Findings underscore the importance of robust evaluation frameworks for deploying VLMs in visually rich and safety-critical environments.
Synthesis/recommendation based on experimental results showing that visual inputs (images and colors) can influence VLM decisions and that mitigation effectiveness varies by model.