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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This lack of focus creates uncertainty about whether regulatory technology helps legitimate economic recovery or instead strengthens exclusion and informality.
Interpretive observation from gaps identified in the reviewed literature; no empirical resolution provided.
There is a governance–task decoupling: under structural stress, text-only governance degrades on both governance and task dimensions simultaneously, whereas mechanical enforcement preserves governance quality even as task performance drops.
Experimental stress tests or structural-stress scenarios applied to both governance architectures in the paper's synthetic experiments; observed differential behavior across governance and task metrics. Abstract does not provide numeric details.
The improvement from mechanical enforcement is driven by architectural separation: LLM-generated rationales under mechanical enforcement show comparable CDL to text-only governance — the gain comes from removing clear-cut decisions from the model's control.
Analysis comparing LLM-generated rationales and a metric called CDL across governance architectures in the synthetic banking experiments; authors attribute improvement to removing certain decisions from the model's control. Specific statistics and CDL definition not provided in abstract.
Digital transformation reconfigures development patterns across regions and countries, altering established trajectories of regional development.
Theoretical integration of a technology–labor–space framework together with comparative regional field evidence illustrating changing development patterns (no quantified effect sizes or sample sizes reported).
Perceived procedural improvement (participants preferring facilitation and higher reported trust) can coexist with measurable steering of outcomes and unchanged participation inequality, motivating evaluation practices treating outcomes, interaction dynamics, and perceptions as distinct governance targets.
Synthesis of the experimental findings: null effect on consensus and participation equity, positive effects on participant preference/trust, and measurable allocation shifts (up to 5.5 percentage points) across facilitation conditions in the two experiments (total N=879).
Facilitators shifted select charity-level allocations by up to 5.5 percentage points, directly affecting the final charitable payout.
Analysis of final group allocation outcomes across experimental conditions showing shifts in allocation to specific charities; reported maximum observed shift of 5.5 percentage points attributable to facilitator condition(s). (Study-level sample covering the two experiments; participants organized in groups of three.)
Augmented work agency is shaped by whether applications are generative or non-generative, by employees' experiences of anxiety and technostress, and by micro-politics through which teams negotiate AI use and AI ethics.
Thematic findings from semistructured interviews (28 participants) and document review identifying these factors as shaping agency in practice.
The analysis uncovers three central tensions shaping AI-mediated work: autonomy versus orchestration; capability versus dependency; and experimentation versus ethics.
Recurring themes identified through qualitative interviews (28 participants) and document review; interpretive synthesis presented in findings.
AI integration transforms managerial practices, workforce identities and organizational coordination.
Thematic and interpretive analysis of semistructured interviews with 28 managers/professionals across 12 organizations and review of organizational documents.
Specialized detectors generally perform better but remain inconsistent across generators and can produce false positives on real-damaged samples.
Experimental comparison showing specialized AI-generated image detectors outperform MLLMs on some generator subsets, yet show variability across generators and some false positives on genuine damaged images.
The intervention serves as a middle ground in the trade-off between higher costs (from more granular demographic targeting) and skew (from ignoring demographics entirely).
Authors' comparative claim about cost–skew trade-offs observed in their intervention versus alternatives; no quantitative cost or skew figures provided in the excerpt.
The research challenges for this vision stem from a broader flexibility–robustness tension that requires moving beyond the on-the-fly paradigm to navigate effectively.
Analytical claim in paper identifying a design trade-off (flexibility vs. robustness) as the core challenge motivating the proposed shift; no empirical demonstration provided.
Under open-ended prompts, trust drops to 3-55%, confirming prompt framing as a confound; we report both conditions.
Experimental comparison reported by authors between directed queries and open-ended prompts; observed trust rates under open-ended prompts ranged from 3% to 55% (no explicit per-model sample sizes reported in the summary).
Depending on the used fairness metric, the Pareto frontier may include upper-bound threshold rules, thus preferring individuals with lower success probabilities.
Analytical derivations showing that for certain fairness metrics the set of Pareto-optimal rules includes rules that impose upper-bound thresholds; theoretical examples and arguments in the paper.
The study reframes VTech adoption as legitimacy-seeking rather than efficiency-driven.
Thematic analysis using Rogers' diffusion of innovations and institutional theory, resulting in the institutionally mediated diffusion of innovations (IDOI) framework which emphasizes legitimacy concerns.
Practitioners stress that human judgement remains indispensable, positioning technology as an aid rather than a replacement.
Interview responses from valuers and firm leaders emphasizing the continued role of human judgement; thematic analysis framed by the IDOI model.
Responses [about AI's effects] vary by cohort and depending on survey framing.
Paper asserts heterogeneity in survey responses across demographic cohorts and due to framing effects (no subgroup sample sizes or framing experiment details in excerpt).
This [model divergence] may explain why public opinion is not settled about the effects of AI.
Paper's interpretive claim linking model divergence to unsettled public opinion (presented as a plausible explanation; no causal test or survey linkage provided in excerpt).
Current models about the vulnerability level of occupations and economic sectors differ widely in their forecasts.
Paper's comparative statement about existing models and their forecasts (no specific models, quantitative comparisons, or sample sizes provided in the excerpt).
Message for AI alignment: smooth scoring-based oversight cannot elicit truthful reports from a strategic agent; sharp thresholds (step functions) are the calibration-preserving design.
Synthesis of the paper's theoretical impossibility and constructive results applied to AI oversight setting (argument plus the step-function constructive escape).
Governance machinery from energy systems and critical infrastructure offers a partial template for governing automated web actors, but only some dimensions transfer.
Comparative governance argument drawing on adjacent-sector governance literature; conceptual mapping rather than empirical governance trial reported.
Public discussion of generative AI in accounting swings between the allure of full automation and job-displacement anxiety, yet the most immediate reality in organizations is human + AI work.
Paper's background/intro synthesizing recent research and practitioner commentary (2023–2025); conceptual observation rather than empirical test.
The novel governance problem is not that AI creates new failure modes, but that AI changes their incidence, observability, and persuasive force enough to require different governance responses.
Normative/analytic claim in the paper; argumentation rather than empirical evidence.
The study provides new empirical evidence that technological innovation (specifically generative AI) reshapes financial spillover networks and highlights the importance of considering both the level and structure of connectedness in assessing systemic risk.
Overall empirical results from the TVP-VAR analysis of connectedness across AI equities, cryptocurrencies, and traditional assets, and discussion of implications for systemic risk assessment.
The impact of AI on financial markets is better understood as a structural transformation of interconnectedness rather than a simple intensification of linkages.
Synthesis of empirical findings from the TVP-VAR showing changes in network structure and heterogeneous directional roles across asset groups, rather than a monotonic increase in aggregate connectedness.
The structure of spillovers undergoes significant changes over the sample period.
TVP-VAR estimated time-varying spillover/connectedness network showing changes in directional spillovers and network topology (paper states 'significant changes').
Introducing taxes on AI returns (τ_ai) and financial gains (τ_f) yields three distinct long-run regimes: low-tax (extreme inequality), moderate-tax (stable mixed economy), and high-tax (post-scarcity with universal basic income).
Model extension with tax parameters τ_ai and τ_f and analysis of steady states/long-run regimes; bifurcation analysis identifying regime types associated with ranges of (τ_ai, τ_f).
The rapid emergence of agentic AI tools raises new questions that the political science discipline must address.
Epilogue of the report raises agentic AI tools as a rapidly emerging phenomenon and lists questions for the discipline; based on expert judgment and forward-looking analysis rather than empirical measurement in the introduction/epilogue.
AI will affect political science research and teaching.
Report introduction explicitly notes the report investigates implications for political science research and teaching; based on the task force's review and analysis rather than a quantitative study.
AI will affect public opinion and the information ecosystem.
Introductory chapter enumerates public opinion and the information ecosystem as report topics; based on conceptual synthesis and literature review.
AI will affect the labor market.
Report introduction identifies the labor market as an area the task force examines; presented as a conceptual claim without primary-sample estimates in the introduction.
AI will affect international relations.
Introductory chapter lists international relations as a topic the report investigates; claim arises from conceptual analysis and synthesis by task force authors.
AI will affect national security.
Report introduction stating a section addressing national security implications; based on expert assessment and literature review rather than a specific empirical sample.
AI will affect public administration.
Report introduction describing a section focused on how AI will affect public administration; based on expert synthesis rather than reported empirical study.
AI will affect democracy (i.e., democratic processes and institutions).
Report introduction listing a section of the report devoted to democracy and AI; conceptual argumentation rather than reported empirical tests.
AI has the potential to reshape politics and political science, similar to how it is transforming other social phenomena and academic fields.
Introductory chapter of the APSA Presidential Task Force report; conceptual framing and literature synthesis by the task force authors (no primary empirical sample reported).
We empirically validate these theoretical observations using both synthetic and real datasets.
Experimental evaluation reported in the paper applying proposed policies and measures to synthetic data and at least one real dataset (details not given in abstract).
Two minimal extension policies, each derived from the observation, close the regime along orthogonal axes: a sample-size-aware static rule (Periodic-with-floor) closes the granularity-failure case, while a history-conditioned suspicion-escalation policy closes the coverage-failure case for the naive Drift strategy — and neither closes both, exactly as the observation predicts.
Design and analysis of two auditor policies in the paper; theoretical argument from Observation 1 and supporting simulation results illustrating which failure modes each policy addresses.
A standard learning agent can obtain near-reference revenue per available room (RevPAR) while failing to learn market-like yield management: it sells too aggressively, undercuts, or collapses to modal price buckets.
Experiments in a two-hotel revenue-management simulator where Hotel A is trained against a fixed rule-based competitor (Hotel B); comparison of learned agent behavior to market-like yield management patterns observed in traces.
Modeling fiscal policy as a government problem (instead of an abstract planner) implies a tax changes the firm's automation first-order condition, raises revenue only on the remaining automation base, and requires specifying rebates and administrative losses.
Explicit governmental optimization and budget-accounting setup in the model: taxes enter firms' automation first-order conditions; revenue is computed on post-tax automation activity and rebates/administration are modeled.
The central analytic object is the derivative of household consumption demand and the collective wage bill with respect to automation.
Paper's stated modeling focus: comparative-static derivatives linking automation to household consumption demand and aggregate wages; used to characterize incidence and welfare effects.
Automation reallocates income and ownership claims.
Theoretical model with heterogeneous households who hold capital/equity claims; equilibrium determines wages and returns and shows changes in income and ownership shares when automation increases.
The strategic interplay between antitrust regulation and vertical integration materially influences the evolutionary transitions of the computing power ecosystem.
Core focus of the paper's tripartite evolutionary game model which explicitly models government regulators, incumbents, and downstream innovators and analyzes resulting equilibria and transitions (method: theoretical evolutionary game + analytical derivation).
The evolution of the AI computing power innovation ecosystem manifests distinct stage-based progressions and threshold-driven bifurcation characteristics, potentially transitioning from an initial 'natural monopoly and passive dependence' state through intermediary states (e.g., 'comfort zone trap' or 'regulatory stalemate') toward a mature configuration of 'co-opetition and endogenous growth.'
Derived from the paper's tripartite evolutionary game model and analytical derivation of evolutionarily stable strategies, with supporting numerical simulations exploring parametric sensitivities (method: theoretical evolutionary game + numerical simulation).
The computing power industry is undergoing a paradigm shift from traditional linear supply chains toward complex, interdependent innovation ecosystems driven by the rapid proliferation of generative artificial intelligence.
Conceptual claim presented in the paper's introduction/motivation; supported by the paper's theoretical framing and literature-based motivation rather than empirical data (method: narrative/theoretical framing).
Overall conclusion: AI offers substantial benefits to financial institutions, but ethical considerations and strategic workforce planning are essential for sustainable integration.
Synthesis/interpretation by the authors drawing on their empirical results (positive effects on ROA, efficiency, risk-adjusted returns, customer satisfaction, reduced compliance costs/breaches) and identified challenges (algorithmic bias, workforce displacement).
Empirical analysis of cases demonstrates that diverse, and often non-ethics-related, levers motivate organizations to abandon AI development.
Analysis of cases drawn from the AI incident database and practitioner survey contrasted with the taxonomy from the scoping review; specific counts/effect measures not provided in the summary.
Three sovereignty boundaries determine whether AI remains an amplifier within a human-governed system or becomes a de facto control center: irreversible decision authority, physical resource mobilization authority, and self-expansion authority.
Conceptual model element in the paper; identification and definition of three 'sovereignty boundaries' used to analyze governance risks.
The paper formalizes this claim through decision-energy density: the rate-weighted capacity of a node to generate, evaluate, select, and execute consequential decisions.
Formal/modeling claim — the paper defines and uses a formal metric called 'decision-energy density' within its theoretical framework.
AI capabilities can be copied, invoked, embedded in workflows, and scaled across institutions at low marginal cost.
Descriptive claim about AI technology characteristics made in the paper; supported by conceptual argument and examples rather than quantified empirical data.