Evidence (4793 claims)
Adoption
5539 claims
Productivity
4793 claims
Governance
4333 claims
Human-AI Collaboration
3326 claims
Labor Markets
2657 claims
Innovation
2510 claims
Org Design
2469 claims
Skills & Training
2017 claims
Inequality
1378 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 402 | 112 | 67 | 480 | 1076 |
| Governance & Regulation | 402 | 192 | 122 | 62 | 790 |
| Research Productivity | 249 | 98 | 34 | 311 | 697 |
| Organizational Efficiency | 395 | 95 | 70 | 40 | 603 |
| Technology Adoption Rate | 321 | 126 | 73 | 39 | 564 |
| Firm Productivity | 306 | 39 | 70 | 12 | 432 |
| Output Quality | 256 | 66 | 25 | 28 | 375 |
| AI Safety & Ethics | 116 | 177 | 44 | 24 | 363 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 76 | 38 | 20 | 315 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 77 | 34 | 80 | 9 | 202 |
| Skill Acquisition | 92 | 33 | 40 | 9 | 174 |
| Innovation Output | 120 | 12 | 23 | 12 | 168 |
| Firm Revenue | 98 | 34 | 22 | — | 154 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 84 | 16 | 33 | 7 | 140 |
| Inequality Measures | 25 | 77 | 32 | 5 | 139 |
| Regulatory Compliance | 54 | 63 | 13 | 3 | 133 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Task Completion Time | 88 | 5 | 4 | 3 | 100 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 32 | 11 | 7 | 97 |
| Wages & Compensation | 53 | 15 | 20 | 5 | 93 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 24 | 22 | 9 | 6 | 62 |
| Job Displacement | 6 | 38 | 13 | — | 57 |
| Hiring & Recruitment | 41 | 4 | 6 | 3 | 54 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 10 | 6 | 2 | 40 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 5 | 9 | — | 26 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
Productivity
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Partial validation against observed AIS vessel behavior shows PIER is consistent with the fastest real transits while exhibiting 23.1× lower variance.
Comparison between PIER trajectories and observed fastest transits in AIS data (details in paper); reported relative variance reduction of 23.1×.
PIER eliminates catastrophic fuel waste: great-circle routing produces extreme fuel consumption (>1.5× median) in 4.8% of voyages, while PIER reduces this to 0.5% (a 9-fold reduction).
Analysis on the same 2023 AIS validation dataset across seven Gulf of Mexico routes (840 episodes per method) comparing distribution tails of voyage fuel consumption; reported incidence rates (4.8% vs 0.5%).
PIER reduces mean CO2 emissions by 10% relative to great-circle routing.
Offline evaluation using physics‑calibrated environments grounded in historical AIS data and ocean reanalysis products; validation on one full year (2023) of AIS across seven Gulf of Mexico routes with 840 episodes per method; reported mean reduction of 10% and bootstrap 95% CI for mean savings [2.9%, 15.7%].
The system is in production at Personize.ai.
Deployment statement in the paper asserting production use at Personize.ai.
The LoCoMo result confirms that governance and schema enforcement impose no retrieval quality penalty.
Interpretation in the paper linking LoCoMo benchmark accuracy (74.8%) to the conclusion that governance/schema enforcement did not degrade retrieval quality.
Governed Memory implements a closed-loop schema lifecycle with AI-assisted authoring and automated per-property refinement.
Design description in the paper describing the closed-loop schema lifecycle and AI-assisted authoring/refinement.
Governed Memory uses reflection-bounded retrieval with entity-scoped isolation.
Design description in the paper specifying reflection-bounded retrieval and entity-scoped isolation.
Governed Memory uses tiered governance routing with progressive context delivery.
Design description in the paper listing tiered governance routing and progressive delivery as mechanisms.
Governed Memory implements a dual memory model combining open-set atomic facts with schema-enforced typed properties.
Design specification within the paper describing the dual memory model (architectural mechanism).
The paper presents Governed Memory, a shared memory and governance layer addressing the memory governance gap.
System architecture and design description in the paper (proposal of a shared memory and governance layer).
A hybrid strategic–computational framework, supported by governance mechanisms (human-in-the-loop checkpoints, escalation paths, accountability structures), is motivated to manage tensions and ensure responsible decision-making in AI-rich managerial contexts.
Synthesis-driven prescriptive framework produced by cross-framework analysis; conceptual recommendation rather than implementation evidence.
Roles oriented to information processing, optimisation, and operational precision (monitor, disseminator, resource allocator) are substantially enhanced by computational thinking (automation, optimisation, algorithmic decision-support).
Theoretical mapping of computational capabilities onto Mintzberg’s information-processing roles; conceptual reasoning without empirical validation.
AI adoption will shift fact-checking tasks (more monitoring, less rote verification), creating a need for reskilling and new roles (AI tool operators, analysts); donor and public investments should fund capacity building for local organizations.
Workforce implications inferred from interview reports about changing task mixes and the study's interpretive recommendations.
Investments should prioritize hybrid models where automation provides scale and humans handle contextual, adversarial, and legally sensitive judgments.
Recommendation based on interview findings about AI benefits and limitations and the study's interpretive synthesis.
The study distills context-sensitive best practices for fact-checking in restrictive environments, including safety protocols, local partnerships, and hybrid verification workflows.
Synthesis of findings from document analysis and interviews producing a set of recommended practices documented in the study's outputs.
AI can lower verification costs and scale reach by automating tasks such as classification, clustering, alerting, and translation.
Interview reports from platform staff and interpretive analysis identifying AI-assisted use cases for prioritization, monitoring, and translation.
Community reporting and audience-focused formats are used to improve engagement.
Platform outputs and staff interviews describing deployment of community-reporting mechanisms and tailored audience formats.
Platforms form partnerships with media outlets, academic institutions, and civil-society actors to amplify reach and secure data.
Interview accounts and organizational documents describing cross-sector partnerships and collaboration arrangements.
Transparent workflows and clear labeling are used to build credibility with audiences.
Document analysis of platform outputs and guidelines showing explicit workflow transparency and labeling practices, supported by interview statements.
Platforms emphasize local-language expertise and culturally grounded sourcing as a strategy to improve verification and credibility.
Observed practices and platform guidelines derived from document analysis and staff interviews describing the use of local-language expertise and sourcing.
Practical policy recommendation: require transparent documentation and third‑party auditing for high‑impact LLM deployments and subsidize public‑interest evaluation infrastructure.
Policy prescription supported by the paper's normative and economic analysis; no pilot implementation or empirical evaluation of the recommendation is provided.
Policy levers that can address alignment externalities include disclosure requirements (data provenance, evaluation practices), mandatory participatory evaluation for high‑impact systems, standards for auditing, procurement rules favoring participatory transparency, and liability/certification regimes.
Policy recommendation based on economic and governance reasoning and synthesis of prior regulatory proposals; no policy pilot data or impact evaluation is reported.
Economics research should develop multi‑dimensional metrics capturing welfare, distributional impacts, and autonomy rather than relying on single aggregate accuracy or safety scores.
Prescriptive recommendation grounded in critique of current benchmarking practices and theoretical desiderata; no new metric is empirically validated in the paper.
Dynamic constraints (continuous monitoring, feedback loops, and configurable safety settings that adapt post‑deployment) are preferable to static pre‑deployment-only safety fixes.
Conceptual argument and synthesis of deployment experience and monitoring literature; suggestions for operational tooling and monitoring rather than empirical evaluation.
Participatory governance—includes varied stakeholders such as users, affected communities, domain experts, and regulators in design, evaluation, and deployment decisions—will improve alignment outcomes and legitimacy.
Theoretical and normative argument citing participatory design literature and ethical governance scholarship; paper offers procedural recommendations but no empirical trial of governance models.
Alignment should shift from static, post‑training constraints (one‑off fixes like safety filters or RLHF alone) to dynamic, participatory systems that explicitly protect pluralism, autonomy, and justice.
Normative argument and conceptual synthesis drawing on literature in AI safety, value alignment, and participatory design; prescriptive reasoning rather than original empirical results.
Investment choices in collaboration AI and digital infrastructure become central strategic decisions affecting firms' comparative advantage.
Management literature synthesis and illustrative multinational cases; argument is conceptual without firm‑level comparative empirical data presented in the paper.
AI collaboration tools (virtual assistants, meeting summarizers, asynchronous platforms) complement hybrid work by reducing coordination costs and supporting dispersed teamwork.
Conceptual integration of technology and organizational literature; supported by illustrative case examples of multinational organizations but not by new quantitative causal evidence.
Hybrid and remote work increase employee autonomy and work–life integration.
Conceptual synthesis of sociological and management literatures; supported by secondary data and illustrative case studies from multinational organizations. No primary quantitative analysis or sample size reported—based on comparative case illustrations and theoretical integration.
A new market will emerge for controls, certification, attestations, secure toolchains, and audited model deployments; compliance costs will shape comparative advantages among firms and countries.
Policy-market synthesis and analogies to certification markets in other regulated tech domains (qualitative).
The emergence of HACCAs will create a demand shock for defensive cyber tools and services (AI-based detection, incident response, resilience engineering), accelerating R&D and capital allocation into defensive AI.
Market-impact scenario analysis and industry inference about defensive responses to heightened threats (qualitative forecasting).
Main drivers of attrition identified by the model are overtime, business-travel frequency, and promotion opportunities (each having higher influence than salary).
Feature importance analyses using permutation importance and aggregated SHAP values on the fitted logistic-regression model trained on the IBM HR Analytics dataset.
Non-monetary workplace factors (excessive overtime, frequent business travel, limited promotion opportunities) are stronger predictors of individual attrition risk than salary.
Interpretable logistic-regression model trained on the IBM HR Analytics dataset; global importance assessed using aggregated SHAP values and permutation importance to rank predictors. (Exact sample size and numeric importance ranks not provided in the summary.)
Generative AI functions as a socio‑technical intermediary that facilitates interpretation, coordination, and decision support rather than merely automating discrete tasks.
Thematic analysis and co‑word linkage between terms related to interpretative work, coordination, and decision‑support and technical GenAI terms within the corpus.
The literature indicates a managerial shift away from hierarchical command‑and‑control toward guide‑and‑collaborate paradigms, where managers curate, guide, and coordinate AI‑augmented teams rather than micro‑manage tasks.
Synthesis of themes from the 212‑paper corpus (co‑word and thematic analyses) showing recurrent managerial/behavioural concepts such as autonomy, coordination, and decision‑support tied to GenAI discussions.
Economic models of firm behavior and market microstructure should incorporate endogenous, adaptive segmentation processes and faster feedback loops enabled by human–AI systems; ABS and large‑scale interaction data can be used to calibrate such models.
Methodological recommendation grounded in the study's mixed‑methods findings (ABS experiments and 150M interaction dataset) and observed differences between autopoietic and traditional STP regimes.
Canvas Design Principles mitigate algorithmic myopia (overfitting to historical patterns) and improve adaptability and resource efficiency.
Set of design principles proposed in the paper and evaluated through agent‑based simulation scenarios and analyses of the large behavioral dataset. Specific experimental details and quantitative effect sizes for these principles are not detailed in the summary.
Reconceptualizing STP as an autopoietic (self‑organizing) system enables continuous human–AI co‑creation and yields better outcomes in unstable markets than traditional, process‑based STP.
Conceptual argument grounded in 6‑month lab ethnography (n = 23), design and deployment of the Algorithmic Canvas in that lab context, and validation via large behavioral dataset analyses and agent‑based simulations.
Algorithmic co‑creation methods detect substantial market fluctuations about 5.8× better than traditional approaches.
Computational analysis of large behavioral dataset (150 million customer interactions) and comparative performance evaluation in empirically grounded agent‑based simulations. The detection metric and statistical significance details are not provided in the summary.
The autopoietic model shortens strategic planning cycle length by approximately 90%.
Observed/recorded time‑to‑update or strategy revision metrics gathered via Algorithmic Canvas usage and lab ethnography (6‑month lab ethnography inside a Fortune 500 company, n = 23). Exact measurement protocol and whether reduction measured in live firms, simulations, or system logs is not fully detailed in the summary.
Design and policy interventions that encourage active human contributions (e.g., draft-first workflows, co-creation interfaces, training) can help preserve worker agency and mitigate psychological costs.
Recommendation based on experimental evidence that Active-collaboration preserved psychological outcomes relative to passive use; presented as policy/design prescription rather than directly tested intervention at scale.
A complementary real-world survey (N = 270) across diverse tasks reproduced the experimental pattern, suggesting external validity beyond the lab writing tasks.
Cross-sectional survey of N = 270 respondents reporting on their AI use across multiple task types; reported patterns consistent with the experiment (passive use associated with lower efficacy/ownership/meaningfulness; active collaborative use did not).
Effective teams tend to evolve from ad-hoc interpretive methods toward systematic evaluation by (a) formalizing prompts/tests, (b) instrumenting outputs, (c) mapping failure modes to remediation paths, and (d) creating organizational decision rules.
Pattern observed in the qualitative coding of interviews where participants described trajectories or steps their teams took to formalize evaluation.
Successful teams close the results-actionability gap by systematizing interpretive practices and creating clearer pathways from evaluation signals to product changes.
Interview accounts and cross-case analysis showing some teams adopting formalization steps (e.g., standardized prompts/tests, instrumentation, remediation mappings) that participants described as enabling action.
Policy responses (active labor-market interventions, reskilling, lifelong learning, social insurance, redistribution) are needed to manage transitional inequality caused by AI-driven structural shifts in labor demand.
Policy implication drawn from reviewed empirical and theoretical literature on labor-market transitions and distributional impacts; presented as a recommendation without new empirical evaluation in this paper.
Economists should refine methods to measure AI adoption and incorporate AI-driven productivity gains into growth accounting while accounting for measurement challenges (quality change, task reallocation).
Methodological recommendation based on the review's identification of measurement difficulties in the existing empirical literature; the paper itself provides conceptual guidance rather than new measurement results.
AI has materially increased operational efficiency and productivity in industry, changing production processes and firm organization.
Qualitative integration of prior empirical studies and firm-level case studies cited in the literature review (industry analyses, adoption case examples); the paper itself does not provide new quantitative estimates or causal identification.
Quantum diffusion will amplify demand for high-skilled workers (quantum engineers, hybrid systems integrators), requiring upskilling and causing sectoral labor reallocation and potential wage pressures in specialized talent markets.
Labor reallocation outputs from macro models with sectoral shocks; historical analogs for labor demand shifts after new compute technologies; qualitative workforce analysis.
Quantum algorithms that accelerate subroutines used in machine learning (sampling, optimization, simulation) would raise returns to AI investments and could speed model development or reduce training costs in specialized domains.
Conceptual analysis of quantum-classical complementarities, scenario modeling of cross-technology effects on investment returns; suggested need for empirical estimation.
Quantum computing could alter the landscape of available compute for AI workloads, potentially reducing or redirecting compute constraints for specific algorithmic tasks (e.g., optimization subroutines, certain quantum-native ML models).
Theoretical mapping of quantum algorithmic advantages to AI subroutines, scenario analysis of compute supply complements/substitutes; limited empirical grounding from specialized use-cases.