Evidence (4049 claims)
Adoption
5126 claims
Productivity
4409 claims
Governance
4049 claims
Human-AI Collaboration
2954 claims
Labor Markets
2432 claims
Org Design
2273 claims
Innovation
2215 claims
Skills & Training
1902 claims
Inequality
1286 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 369 | 105 | 58 | 432 | 972 |
| Governance & Regulation | 365 | 171 | 113 | 54 | 713 |
| Research Productivity | 229 | 95 | 33 | 294 | 655 |
| Organizational Efficiency | 354 | 82 | 58 | 34 | 531 |
| Technology Adoption Rate | 277 | 115 | 63 | 27 | 486 |
| Firm Productivity | 273 | 33 | 68 | 10 | 389 |
| AI Safety & Ethics | 112 | 177 | 43 | 24 | 358 |
| Output Quality | 228 | 61 | 23 | 25 | 337 |
| Market Structure | 105 | 118 | 81 | 14 | 323 |
| Decision Quality | 154 | 68 | 33 | 17 | 275 |
| Employment Level | 68 | 32 | 74 | 8 | 184 |
| Fiscal & Macroeconomic | 74 | 52 | 32 | 21 | 183 |
| Skill Acquisition | 85 | 31 | 38 | 9 | 163 |
| Firm Revenue | 96 | 30 | 22 | — | 148 |
| Innovation Output | 100 | 11 | 20 | 11 | 143 |
| Consumer Welfare | 66 | 29 | 35 | 7 | 137 |
| Regulatory Compliance | 51 | 61 | 13 | 3 | 128 |
| Inequality Measures | 24 | 66 | 31 | 4 | 125 |
| Task Allocation | 64 | 6 | 28 | 6 | 104 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 10 | 16 | 93 |
| Worker Satisfaction | 42 | 32 | 11 | 6 | 91 |
| Task Completion Time | 71 | 5 | 3 | 1 | 80 |
| Wages & Compensation | 38 | 13 | 19 | 4 | 74 |
| Team Performance | 41 | 8 | 15 | 7 | 72 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 17 | 15 | 9 | 5 | 46 |
| Job Displacement | 5 | 28 | 12 | — | 45 |
| Social Protection | 18 | 8 | 6 | 1 | 33 |
| Developer Productivity | 25 | 1 | 2 | 1 | 29 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 7 | 4 | 9 | — | 20 |
Governance
Remove filter
AI accelerates value-chain maturation while creating distinct risks — including professional responsibility tensions and potential system-level externalities.
Conceptual argument and risk analysis in the Article (theoretical reasoning and synthesis of management/ethics literature). No empirical causal estimate reported in the excerpt.
The legal profession is at a crossroads, caught between intensifying fears of AI-driven displacement and a generational opportunity for transformation.
Author's synthesis and framing in the Article (conceptual assessment; literature/contextual synthesis). No empirical sample or experiment reported in the excerpt.
This advantage is contingent upon robust AI governance, ethical frameworks, and the transition from 'pilot-lite' projects to integrated, data-driven 'AI-first' business models.
Conditional claim in the paper linking success to governance, ethics, and organizational integration; appears to be normative/analytical rather than empirical in the abstract.
The paper reframes AI governance as a form of social policy shaped by political and economic institutions.
Conceptual/interpretive claim supported by the authors' comparative analysis and theoretical framing of AI governance alongside social policy dimensions.
Although many regions use similar ethical language, substantial differences persist in risk allocation, regulatory enforcement, welfare integration and social protection.
Content analysis of policy documents showing overlap in ethical rhetoric but divergence across coded institutional dimensions related to risk allocation, enforcement, welfare integration and social protection (n=24).
Five distinct governance models emerge: rights-based (EU), market-driven (US), state-centric (China), hybrid (Australia–Japan–Singapore) and developmental (India).
Typology derived from coding and index comparison of the 24 policy documents; authors classify regions/countries into five labeled governance models.
The findings show clear and systematic differences in how regions govern AI.
Comparative analysis of coded policy documents (n=24) producing indices that the authors interpret as showing systematic cross-regional differences in governance approaches.
The documents are systematically coded across four institutional dimensions and converted into simple indices to compare governance approaches across the regions.
Author-reported method: systematic coding of documents on four institutional dimensions and construction of indices for cross-regional comparison (based on the 24 documents).
This study uses a comparative qualitative policy analysis based on 24 key AI policy documents published between 2018 and 2025 across the European Union, United States, China, and Indo-Pacific economies.
Author-stated research design and sample: systematic review/comparative qualitative policy analysis of 24 AI policy documents spanning 2018–2025 covering EU, US, China and Indo-Pacific economies.
Energy policy uncertainty has a nonlinear effect on AI investment: moderate uncertainty fosters innovation, whereas high volatility hinders long-term investment.
Empirical analysis using nonlinear methods (WQR and WQC) on US quarterly data 2013Q1–2024Q4 (48 quarters), assessing distributional asymmetries across quantiles and time–frequency bands.
The growth effects of AI are conditional on institutional quality and organizational adaptability.
Theoretical/analytical claim in the paper's framework and supported by the stylized-facts analysis indicating heterogeneity in productivity and growth outcomes by institutional and digital capacity indicators.
AI agents implicate many areas of law, ranging from agency law and contracts to tort liability and labor law.
Legal/policy analysis in the paper enumerating legal domains implicated by AI agents (qualitative analysis; no sample size).
AI assistance in safety engineering is fundamentally a collaboration design problem rather than merely a software procurement decision: the same tool can either degrade or improve analysis quality depending entirely on how it is used.
Synthesis of the formal framework and analytic results in the paper (theoretical argument; no empirical sample reported).
The paper concludes by discussing open challenges in evaluating harmful manipulation by AI models.
Paper includes a discussion/conclusion section enumerating open challenges; stated in abstract.
We identify significant differences across our tested geographies, suggesting that AI manipulation results from one geographic region may not generalise to others.
Empirical comparison across three locales (US, UK, India) showing statistically significant differences in manipulation outcomes by geography.
Context matters: AI manipulation differs between domains, suggesting that it needs to be evaluated in the high-stakes context(s) in which an AI system is likely to be used.
Comparative analysis across three domains (public policy, finance, health) showing differences in manipulative behaviour and/or impact by domain in the empirical study.
The paper's findings deepen the understanding of algorithmic aversion in the context of generative AI and offer practical guidance for creators and platforms navigating transparency versus engagement trade-offs.
Authors' interpretation and conclusions summarized in the abstract, based on the two experiments (study 1: n = 325; study 2: n = 371).
The governance risk-mitigation effects of AI operate through increasing financial risk exposure.
Authors' mechanism tests indicate a relationship between AI adoption and changes in financial risk exposure measures, which they interpret as a channel affecting executive behavior.
The paper draws comparisons between inference tokens and established commodities such as electricity, carbon emission allowances, and bandwidth to motivate financialization.
Theoretical comparison and historical analysis (drawing on the historical experience of electricity futures markets and commodity financialization theory) as presented in the paper.
Initiatives such as Cassava AI's network of AI factories signal growing interest in adopting AI in Africa, but these projects remain very targeted and continental adoption still requires better coordination between African stakeholders.
Cited example (Cassava AI) in the paper to illustrate nascent initiatives; combined with the authors' qualitative assessment of scope and geographic targeting of such projects.
Automation holds significant potential for modernising tax administration, but its success depends on aligning technological innovation with inclusive policy design and institutional capacity.
Overall conclusion of the literature synthesis of 36 peer-reviewed articles; based on patterns of positive impacts conditional on contextual factors and governance highlighted across the studies.
Behavioural responses to automation vary across taxpayer segments: some users embrace automation as a facilitator of compliance while others resist due to perceived opacity and technological anxiety.
Synthesis of behavioural findings from the reviewed literature (36 studies) reporting heterogeneous responses by taxpayer segment, including qualitative reports of resistance and quantitative measures of uptake/adoption.
The effectiveness of automated tax systems is mediated by contingencies including digital literacy, institutional trust, and regulatory clarity.
The review identifies recurring contextual factors across the 36 articles that are reported to moderate or mediate the impact of automation on outcomes (qualitative and quantitative findings cited in the synthesis).
AI is not an inherent instrument of justice but a malleable socio-technical force whose equitable outcomes depend on policy design and institutional context.
Interpretation and synthesis of empirical results showing conditional and heterogeneous effects of AI; normative conclusion drawn by authors from observed heterogeneity and mediating channels.
Governmental structures, labor supply and demand, and incorporation of financial measures act as key intervening variables affecting achieved ROI from GenAI implementations.
Qualitative synthesis and theoretical analysis reported in the paper identifying contextual/intervening variables.
There is an evident tension between privacy and security in existing AI governance approaches.
Thematic synthesis and co-occurrence network from the reviewed studies identify trade-offs and tensions reported between privacy-preserving approaches and security requirements.
The fragility of 'Pax Silica' has implications for global capitalism, technological governance, and geopolitical stability.
Analytical inference and concluding assessment based on theoretical framework and comparative analysis; no empirical quantification provided in the abstract.
The paper proposes new mechanisms through which big data affects individual welfare (beyond simple productivity gains), linking privacy costs, multiplier effects, and R&D transformation patterns.
Theoretical/mechanism development: the paper articulates new channels in its macro theoretical framework describing how data sharing impacts welfare via multiple mechanisms (model construction and analytic discussion; no empirical/sample validation).
Consumption is affected by the multiplier effect and the transformation patterns of R&D.
Theoretical: model analysis links consumption dynamics to a multiplier effect and to how R&D transforms inputs/outputs (comparative statics/dynamics in the theoretical framework).
Individuals’ welfare is influenced by both the privacy cost of big data sharing and their consumption levels.
Theoretical: welfare in the model is specified as a function of consumption and a privacy cost term arising from big data sharing; result follows from analytic derivation within the model (no empirical/sample data).
Capability and trust formally diverge beyond a critical scale (Capability-Trust Divergence).
Claim of a formal proof in the paper (mathematical / theoretical demonstration). No empirical sample size reported in the excerpt.
The Institutional Scaling Law shows that institutional fitness -- jointly measuring capability, trust, affordability, and sovereignty -- is non-monotonic in model scale, with an environment-dependent optimum N*(ε).
Theoretical derivation / analytic model presented in the paper (formal derivation of an 'Institutional Scaling Law'). No empirical sample size reported in the excerpt.
Policy implication: smarter, better-coordinated green governance is needed to address the negative local impacts and the crowding-out interaction between AI and environmental regulation.
Policy recommendation drawn in the abstract based on the empirical spatial findings (negative local effects and negative interaction).
Substantial regional gaps persist: leading eastern provinces approach a UCEE value of 1.0 while some northeastern provinces remain below 0.1.
Regional UCEE index estimates from the Super-SBM model across the 30 provinces reported in the abstract.
The systemic implications of AI in finance depend less on model intelligence alone than on how agent architectures are distributed, coupled, and governed across institutions.
Central argumentative claim supported by the AFMM conceptual model and an illustrative empirical application described in the paper (modeling + event-study approach); no full-sample details provided in the excerpt.
The Agentic Financial Market Model (AFMM), a stylised agent-based representation, links agent design parameters (autonomy depth, heterogeneity, execution coupling, infrastructure concentration, supervisory observability) to market-level outcomes including efficiency, liquidity resilience, volatility, and systemic risk.
Presentation of a stylised agent-based model (AFMM) in the paper; conceptual modelling linking specified agent parameters to macro/market outcomes. No empirical sample size reported in the excerpt.
Financial AI agents can be described by a four-layer architecture covering data perception, reasoning engines, strategy generation, and execution with control.
Conceptual framework proposed by the authors (theoretical/architectural proposal); no empirical testing or sample size provided.
These productivity gains are most pronounced for lower-skilled workers, producing a pattern the authors call “skill compression.”
Cross-study pattern reported in the literature review: comparative evidence across worker-skill strata in multiple empirical papers showing larger relative gains for lower-skilled/junior workers; specific underlying studies and sample sizes are not enumerated in the brief.
Financial well-being is not an automatic byproduct of automated credit efficiency but an emergent outcome of architectural alignment among technology, borrower capability, and governance structures.
Theoretical conclusion drawn from empirical results showing mixed effects (positive on repayment and resilience, negative on stress) and significant moderation by human capability and institutional design.
Study 1 quantifies confirmation bias through controlled experiments on 250 CVE vulnerability/patch pairs evaluated across four state-of-the-art models under five framing conditions for the review prompt.
Controlled experiment described in the paper: 250 CVE vulnerability/patch pairs evaluated across four state-of-the-art LLMs under five prompt framing conditions.
Lightweight safeguards can reduce risk in some settings but do not consistently prevent severe failures.
Analysis of simulated interventions/safeguards within governance simulations showing reductions in certain risk metrics in some scenarios, but persistence of severe failures in others; assessment based on rubric-judged transcript segments.
There are large differences in corruption-related outcomes across governance regimes and specific model–governance pairings.
Observed heterogeneity in outcomes across different authority structures and model–governance pairings within the multi-agent simulations, evaluated via rubric-based scoring over 28,112 transcript segments.
These findings indicate a misalignment between the perceived benefit of AI writing and an implicit, consistent effect on the semantics of human writing, with potential implications for cultural and scientific institutions.
Synthesis and interpretation of the paper's empirical results (user study, essay revision experiments, and peer-review analysis); presented as the paper's broader conclusion.
Socioeconomic regression analysis confirms strong correlations between neighborhood racial composition and detection likelihood: Pearson r = 0.83 for percent White and r = -0.81 for percent Black.
Reported Pearson correlation coefficients from regression analysis between neighborhood racial composition variables and detection likelihood in the simulations.
A Conditional Tabular GAN (CTGAN) debiasing approach partially redistributes detection rates but cannot eliminate structural disparity without accompanying policy intervention.
Experimental comparison between baseline simulations and CTGAN-debiased synthetic data showing partial redistribution of detection rates; paper asserts remaining structural disparities.
The paper formalizes the distinction using a signal-aggregation model in which an organization maintains an anchor belief and achieves agreement through two exclusion channels: (1) report shrinkage toward the anchor and (2) a tolerance rule that discards reports deviating beyond a threshold.
Analytical formal model presented in the paper specifying an anchor belief and two exclusion mechanisms; model assumptions and mechanisms are explicit in the theoretical development. No empirical sample.
Organizational cohesion is observationally ambiguous: it can arise either from genuine information integration (debate and synthesis of heterogeneous inputs) or from exclusionary processes (conformity pressure, gatekeeping, intolerance of dissent).
Conceptual argument and formal definition in the paper framing; supported by the analytic distinction introduced in the paper between integration and exclusion as alternative generative mechanisms for observed agreement. No empirical sample—argument is theoretical and illustrated by model construction.
The authors identify ten evaluation practices that teams use, ranging from lightweight interpretive checks to formal organizational processes (examples: qualitative user reviews, red-team testing, A/B experiments, telemetry/log analysis, structured annotation, governance/meta-evaluation).
Thematic coding of 19 interview transcripts produced a taxonomy enumerating ten practices (paper reports the taxonomy as an outcome).
Quantum-driven growth depends critically on adoption rates, infrastructure readiness, complementary investments (digital infrastructure, human capital), and enabling policy/regulatory environments.
Scenario framework that varies (a) technical timelines, (b) sectoral adoption rates (diffusion models), (c) infrastructure readiness, and (d) policy environments; policy counterfactual modeling shows sensitivity of adoption and macro outcomes to these parameters.
The magnitude and timing of macroeconomic impact from quantum computing are highly uncertain.
Monte Carlo / scenario ensemble results showing wide (fat-tailed) outcome distributions driven by uncertainty in technical milestones, adoption rates, and complementarity strengths; use of expert elicitation to parameterize tail risks.