Evidence (5877 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Governance
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Current instability in U.S.–China relations arises less from complete ideological divergence or failure of outright containment policy than from a structured reaction–counterreaction dynamic triggered by chokepoint activation.
Argument based on qualitative analysis of U.S. export restraints after the first Trump administration and application of the 'weaponized interdependence' framework to advanced-technology sectors (paper's theoretical argument and case discussion).
AIGC is reshaping the rights and obligations of platforms and workers.
Argument in the paper describing legal and practical impacts of AIGC on platform-worker relationships; based on doctrinal/legal analysis and discussion of platform practices rather than reported quantitative empirical data.
The study explores implications of algorithmic enterprises for competitive advantage, labour markets, and regulatory policy.
Declared scope of the paper in the abstract; exploration is conceptual and analytical rather than reporting empirical findings or quantified effects.
Survey evidence suggests public attitudes towards AI combine optimism with apprehension, and most respondents oppose granting AI systems final authority over hiring and dismissal decisions.
Review cites multiple public opinion and survey studies reporting mixed (optimistic and apprehensive) attitudes and opposition to AI final authority in employment decisions (survey evidence summarized).
These efficiency gains are offset by a growing 'Efficiency-Legitimacy Paradox' (i.e., improvements in efficiency come with worsening legitimacy concerns).
Conceptual synthesis from the systematic review (2018-2026) identifying a recurring trade-off across reviewed studies; specific empirical quantification not provided in abstract.
There is a structural shift from 'street level' bureaucracies to 'system-level' architectures that can be defined as the institutional division of 'Artificial Discretion' to algorithmic infrastructures.
Synthesis from the PRISMA-guided systematic review of literature (2018-2026) reporting observed changes in administrative architectures; specific studies not enumerated in abstract.
As a General-Purpose Technology (GPT), Artificial Intelligence (AI) is fundamentally reconfiguring state capacity, as well as the mechanics of global economic management.
Systematic review of current research studies (2018-2026) conducted following PRISMA guidelines; synthesis of literature claiming broad institutional and macroeconomic effects. Number of studies not specified in abstract.
Agentic AI differs from traditional algorithmic trading and generative AI through its capacity for goal-oriented autonomy, continuous learning, and multi-agent coordination.
Analytic comparison and synthesis across prior research and technical architectures in the survey; descriptive/definitional rather than empirical testing.
Uncertainty-aware exploration (in algorithms) alters fairness metrics compared to policies that ignore uncertainty.
Results from simulation experiments compare uncertainty-aware exploration policies to baseline policies and report changes in fairness metrics (as described in the abstract and results).
Analysis of more than two decades of M&A deals reveals shifts in acquisition activity and allows mapping of corporate linkages and overlapping investments.
Empirical longitudinal analysis of M&A deals over a period exceeding 20 years; method: mapping corporate linkages from M&A data (sample size/dataset not specified in the excerpt).
The emissions effects of digital trade are conditional rather than uniform, depending on complementary policy (carbon pricing, regulatory stringency), technological (AI-enhanced logistics), and energy (renewables) factors.
Synthesis of findings from fixed-effects regressions with interactions, carbon-pricing threshold analysis, machine-learning threshold detection, and SEM mediation on the monthly panel of 38 OECD economies (2000–2024).
Operationalizing hardware-based governance must address transition realities including legacy hardware, attestation at scale, and protection of civil liberties.
Policy implementation analysis in the paper identifying practical challenges to deploying hardware-layer controls (conceptual/operational analysis; no empirical trial data provided).
The experimental findings are consistent with the paper's theoretical predictions.
Comparison reported in the paper between theoretical model predictions and observed outcomes from the controlled AI-agent trading experiments.
A determinism study of 10 replays per case at temperature zero shows both architectures inherit residual API-level nondeterminism, but DPM exposes one nondeterministic call while summarization exposes N compounding calls.
Determinism experiment with 10 replays per case at temperature zero; qualitative/quantitative observation about number of nondeterministic LLM calls exposed by each architecture.
Advanced prompting methods improve accuracy on inconclusive cases but over-correct, withholding decisions even on clear cases.
Empirical comparison of prompting methods reported in paper: advanced prompts increased accuracy on inconclusive (insufficient-information) cases but led to excessive deferral/withholding on clear cases.
Effective AI policy mixes are contingent on regional resource endowments and development conditions (i.e., variation across configurations indicates contingency on regional context).
Observed variation across the fsQCA-derived configurations; authors interpret differences as reflecting dependence on regional resources and development conditions.
The study was a preregistered experiment across seven leading LLMs and twelve investment scenarios covering legitimate, high-risk, and objectively fraudulent opportunities.
Methodological description in the paper stating preregistration, 7 LLMs, 12 scenarios; combined dataset included 3,360 AI advisory conversations and a 1,201-participant human benchmark.
There is significant heterogeneity in methodological rigor across studies.
Authors' thematic observation from quality appraisal/extraction noting wide variation in methods, validation approaches, and reporting standards among the 64 studies.
AI is increasingly being integrated into both existing and newly emerging digital infrastructures, altering their architecture, functional role, and strategic significance as these systems begin to operate as embedded cognitive infrastructures shaping knowledge production, decision-making, and institutional processes.
Conceptual and descriptive claim presented by the paper (theoretical analysis/literature-informed observation). No empirical sample size or quantitative methods reported in the provided text.
Hybrid ML+rules systems achieve partial DES-property fillability.
Result of the paper's analytic comparison across the four architectures identifying relative fillability levels for hybrid ML+rules systems.
Artificial intelligence raises the threshold at which refinement adds value.
Theoretical/analytical statement in the paper describing AI's effect on the marginal value of refinement; no empirical quantification provided in the excerpt.
AI is becoming a geopolitical tool that defines trade, finance, supply chains, surveillance abilities, and diplomatic bargaining power.
Conceptual/qualitative synthesis in the paper's argument; no empirical methods or sample size reported in the abstract.
Variable importance improvements to zero-shot tabular classification produce mixed results with respect to algorithmic fairness.
Authors report experiments applying variable-importance-based adjustments to zero-shot LLM tabular classification and evaluating resulting algorithmic fairness outcomes; described as producing mixed results. (Sample size not provided in abstract.)
Targeted prompt interventions significantly alter the magnitude of market bubbles (they can amplify or suppress bubble size).
Randomized (or otherwise experimentally manipulated) prompt interventions applied to LLM agents in the simulated open-call auction, with resulting differences in measured bubble magnitude reported.
By analyzing agents' reasoning text through a twenty-mechanism scoring framework, targeted prompt interventions causally amplify or suppress specific behavioral mechanisms.
Qualitative and quantitative analysis of agents' chain-of-thought / reasoning text using a 20-mechanism scoring framework; experimental manipulations of prompts reported to change mechanism scores (interpreted causally as interventions on prompts).
Both US and Chinese strategies depend on cross-country relationships in AI innovation.
Conceptual assertion motivating the network analysis of international collaborations and citations.
The rapid advancement of artificial intelligence (AI) technologies, particularly generative AI and large language models, has reignited debates about the future of work and the potential for widespread labor market disruption.
Statement in the paper's introduction/abstract citing recent empirical studies, industry reports, and ongoing debates; no original sample or numerical evidence reported in the abstract.
Outcomes of AI deployment in labor-market settings depend on complementary organizational practices, workers’ access to skills, and the regulatory environment.
Synthesis-derived moderator/ mechanism claim from qualitative analysis of the 19 included studies identifying organizational practices, skill access, and regulation as contextual moderators.
No aggregation mechanism can simultaneously satisfy all desiderata of collective rationality (connection to Arrow's Impossibility Theorem); multi-agent deliberation navigates rather than resolves this constraint.
Theoretical argument connecting empirical multi-agent deliberation results to Arrow's Impossibility Theorem and observations that deliberation trades off competing desiderata rather than achieving all simultaneously.
Alignment systematically shapes negotiation strategies and allocation patterns between agents.
Experimentally comparing negotiation behavior and allocation outcomes across agent pairs where one agent is aligned (via RAG) and the partner is either unaligned or adversarially prompted; patterns of strategy and allocation differences reported.
Digitization is reshaping the structures of Resource Dependence Theory (RDT) instead of eliminating it completely (Yordanova & Hristozov, 2025).
Conceptual/theoretical claim supported by citation to Yordanova & Hristozov (2025); presented as an interpretive conclusion about how digitization interacts with organizational dependence structures. No empirical details provided in the excerpt.
They can produce fluent outputs that resemble reflection, but lack temporal continuity, causal feedback, and anchoring in real-world interaction.
Descriptive claim made in the text contrasting surface-level fluency with missing properties; no empirical data or experiments provided.
Further research is needed to explore the longitudinal impact of these AI deployments on local labor markets and the creation of indigenous datasets that reflect Cameroon’s unique linguistic diversity.
Authors' identified research gaps and recommendations; statement of future research needs rather than empirical result.
The results show how non-IID data, competition intensity, and incentives shape organizational strategies and social welfare.
Findings from the paper's experiments and analyses that vary non-IIDness, competition intensity, and incentive parameters; no numeric sample sizes provided in abstract.
Outcomes are shaped not only by benchmark quality but also by competitive pressure, including user switching, routing decisions, and operational constraints.
Argument/assertion in paper framing motivations for Marketplace Evaluation; conceptual reasoning listing mechanisms (user switching, routing, operational constraints); no empirical tests or sample size reported.
Alignment operates as a two-way translation, where models are made 'safe for worlds' while those worlds are reshaped to be 'safe for models.'
Conceptual claim supported by ethnographic examples illustrating reciprocal adaptations between models and social/institutional contexts in Nairobi's credit-scoring ecosystem.
Algorithmic credit scoring is accomplished through the ongoing work of alignment that stabilizes risk under conditions of persistent uncertainty, taking epistemic, modeling, and contextual forms.
The paper's theoretical argument grounded in nine-month ethnographic observations and analysis of how practitioners and institutions engage in alignment work across epistemic, modeling, and contextual dimensions.
Practitioners negotiate model performance via technical and political means.
Observational data from the ethnography showing technical adjustments, benchmarks, and political negotiation (e.g., with regulators or management) to establish acceptable performance.
Practitioners formulate risk through multiple interpretations.
Ethnographic evidence from interviews and observations indicating that risk is characterized differently across actors (technical, legal, business interpretations).
Practitioners construct alternative data using technical and legal workarounds.
Field observations and interviews showing practitioners employing technical methods and legal strategies to create or repurpose alternative data sources for credit scoring.
Algorithmic credit scoring is being transformed by new actors, techniques, and shifting regulations.
Ethnographic fieldwork documenting the entry of new actors, novel technical techniques, and regulatory changes affecting credit scoring in Nairobi's digital lending ecosystem.
Credit scoring is an increasingly central and contested domain of data and AI governance.
Nine-month ethnography of credit scoring practices in Nairobi, Kenya; participant observation and interviews across stakeholders in digital lending.
Experiments on the MovieLens-100k dataset illustrate when the empirical payout aligns with — and diverges from — Shapley fairness across different settings and algorithms.
Empirical evaluation performed on the MovieLens-100k dataset (≈100,000 ratings) comparing the proposed payout rule and algorithmic outcomes to Shapley-value allocations across multiple experimental settings and algorithms.
For heterogeneous agents the cooperative game still admits a non-empty core, though convexity and Shapley value core-membership are no longer guaranteed.
Theoretical analysis for heterogeneous-agent case provided in the paper: establishes core non-emptiness but shows convexity and Shapley-in-core do not generally hold.
User interactions in online recommendation platforms create interdependencies among content creators: feedback on one creator's content influences the system's learning and, in turn, the exposure of other creators' contents.
Conceptual/empirical motivation stated in the paper; motivates the multi-agent bandit modeling of creator interactions in recommender systems.
We ran two large preregistered experiments (N=17,950 responses from 14,779 people) using conversational AI models to persuade participants on a range of attitudinal and behavioural outcomes, including signing real petitions and donating money to charity.
Statement in paper reporting two preregistered experiments, sample sizes (17,950 responses; 14,779 people), use of conversational AI models, and target outcomes including petition signing and charitable donations.
Overall, AI emerges as a transformative but context-dependent tool for business decision-making in Latin America.
The authors' overall interpretation and synthesis of the 27 reviewed studies highlighting variable outcomes depending on context and readiness.
AI adoption outcomes depend on organizational routines, data arrangements, accountability structures, and public values.
Empirical and theoretical literature review and argument in the article drawing on scholarship in digital government and public-sector technology adoption.
If employment losses are relatively small and productivity gains are realised, AI adoption could boost Exchequer revenues. But if job displacement is sizeable, tax receipts fall while welfare spending rises, resulting in potentially large pressures on the public finances.
Conditional fiscal scenarios simulated in the report combining employment, wage and benefit changes with the public finance implications (tax receipts and welfare spending); reported as scenario-based outcomes.
Ireland’s tax and welfare system absorbs most of the income loss for lower income households, and roughly half of the loss for households at the top of the income distribution.
Microsimulation using SWITCH to model taxes and transfers applied to simulated income changes across income groups; reported as a finding in the report.