Evidence (13827 claims)
Adoption
8454 claims
Productivity
7544 claims
Governance
6789 claims
Human-AI Collaboration
6327 claims
Org Design
4126 claims
Innovation
4058 claims
Labor Markets
3520 claims
Skills & Training
2924 claims
Inequality
2057 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 195 | 97 | 889 | 1979 |
| Governance & Regulation | 815 | 391 | 188 | 121 | 1539 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 624 | 233 | 123 | 96 | 1084 |
| Research Productivity | 410 | 121 | 56 | 331 | 929 |
| Output Quality | 466 | 177 | 59 | 47 | 749 |
| Decision Quality | 320 | 174 | 75 | 42 | 618 |
| Firm Productivity | 435 | 55 | 88 | 20 | 604 |
| AI Safety & Ethics | 214 | 276 | 65 | 33 | 593 |
| Market Structure | 178 | 166 | 122 | 24 | 495 |
| Task Allocation | 206 | 64 | 70 | 31 | 376 |
| Skill Acquisition | 165 | 57 | 60 | 17 | 299 |
| Innovation Output | 201 | 27 | 41 | 18 | 288 |
| Employment Level | 105 | 51 | 107 | 13 | 278 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 149 | 46 | 26 | 3 | 224 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 61 | 20 | 12 | 182 |
| Error Rate | 69 | 91 | 10 | 2 | 172 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 92 | 19 | 13 | 19 | 145 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Automation Exposure | 51 | 54 | 22 | 12 | 142 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Developer Productivity | 94 | 17 | 14 | 6 | 132 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Skill Obsolescence | 5 | 45 | 6 | 1 | 57 |
| Creative Output | 31 | 16 | 7 | 2 | 57 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
The audit surface follows the same one-versus-N pattern: DPM logs two LLM calls per decision while summarization logs 83-97 on LongHorizon-Bench.
Empirical measurement on LongHorizon-Bench reported in the paper: logged LLM calls per decision are 2 for DPM vs 83-97 for summarization.
DPM is additionally 7-15x faster at binding budgets, making one LLM call at decision time instead of N.
Empirical runtime/efficiency measurement reported in the paper (range 7-15x speedup) comparing number of LLM calls and latency under tight memory budgets.
At a 20x compression ratio, DPM improves reasoning coherence by +0.53 (Cohen's h=1.13, p=0.0034) compared to summarization-based memory (paired permutation, n=10).
Paired permutation test over 10 cases at a 20x compression ratio; reported effect +0.53 with Cohen's h=1.13 and p=0.0034.
At a 20x compression ratio, DPM improves factual precision by +0.52 (Cohen's h=1.17, p=0.0014) compared to summarization-based memory (paired permutation, n=10).
Paired permutation test over 10 cases at a 20x compression ratio; reported effect +0.52 with Cohen's h=1.17 and p=0.0014.
On ten regulated decisioning cases at three memory budgets, DPM matches summarization-based memory at generous budgets and substantially outperforms it when the budget binds.
Empirical evaluation on 10 decisioning cases across three memory budgets; comparison between DPM and summarization-based memory as reported in the paper (n=10).
We propose Deterministic Projection Memory (DPM): an append-only event log plus one task-conditioned projection at decision time.
Method/architectural proposal described in the paper.
Presumptuousness in legal AI is systematic but addressable, and addressing it is a necessary step towards systems that reliably support, rather than supplant, human judgment wherever decisions must await sufficient evidence.
Synthesis conclusion in paper based on the benchmark experiments, comparisons across prompting methods, and SPEC results.
SPEC achieves 89% overall accuracy, while appropriately deferring when evidence is insufficient.
Empirical evaluation of SPEC reported in paper: overall accuracy reported as 89% and behavior of proper deferral on insufficient-evidence cases.
We introduce SPEC (Structured Prompting for Evidence Checklists), a structured framework requiring explicit identification of missing information before any determination.
Methodological contribution described in paper: new prompting/framework (SPEC) that enforces explicit missing-information identification prior to decision.
Through a collaboration with the Colorado Department of Labor and Employment, we secured access to official training materials and guidance to design a novel benchmark that systematically varies information completeness.
Methodological description in paper: collaboration with state agency and dataset/benchmark construction using official training materials and guidance.
Long-term prospects of agentic AI include catalyzing accelerated innovation in physical design via autonomous algorithm discovery, continuous tool improvement, and closed-loop learning from large design corpora.
Forward-looking conclusion in the paper; framed as the authors' projection based on survey synthesis rather than as an empirically demonstrated outcome in the abstract.
Interfaces between agentic systems and traditional EDA frameworks are a key area of focus and enable tighter integration of agent capabilities into existing design workflows.
Survey highlights interfaces between agents and EDA frameworks as a focus area; claim is descriptive of research direction rather than reporting empirical outcomes.
Autonomous agents can explore heuristic spaces for placement, routing, and partitioning, enabling autonomous exploration of design heuristics.
Presented as an emphasized capability/area of research in the survey; the abstract asserts this possibility but does not report empirical benchmarks or sample sizes.
Tool-integrated agents can be used for algorithm evolution, debugging, and workflow automation in physical design R&D.
Paper emphasizes this as a primary area of application in the survey; rationale and examples are discussed but no quantitative trial sizes are given in the abstract.
Agentic AI systems can comprehend user specifications, modify code, run EDA tools, analyze results, perform multi-step reasoning, and iteratively refine design heuristics—unlike earlier ML uses that focused narrowly on prediction or optimization subroutines.
Descriptive claim in the paper contrasting agentic AI capabilities with earlier ML approaches; presented as an overview of functional capabilities rather than empirical measurement.
Recent advances in large language models (LLMs) and tool-using autonomous agents present new opportunities for accelerating research and development in physical design.
Stated as a central thesis in the paper's abstract/survey; based on the authors' synthesis of recent advances and emerging applications (no empirical sample or quantified evaluation reported in the abstract).
The framework is applied to Canada's 2025-2026 national AI Strategy consultation with n = 5,253 respondents across two independent policy topics.
Empirical application reported in the paper; dataset description gives sample size and two policy topics.
This paper introduces 'participatory provenance': a measurement framework grounded in optimal transport theory, causal inference and semantic analysis that tracks how individual public submissions are transformed, filtered or lost through AI-mediated summarization.
Methodological contribution described in the paper (framework design combining optimal transport, causal inference, semantic analysis).
Local governments should develop coordinated AI policy mixes, align differentiated policy pathways with regional conditions, and prioritize technology R&D support, talent cultivation and collaboration, and application demonstration and promotion to sustain long-term regional competitiveness.
Authors' policy recommendations derived from the fsQCA findings and interpretation of which conditions are recurrent/core across configurations.
Technology R&D support, talent cultivation and collaboration, and application demonstration and promotion are the most recurrent core policy conditions across the identified configurations.
Frequency/core-condition analysis within the fsQCA configurations reported by the authors showing these three policy instruments repeatedly appear as core conditions.
The study identifies three driving pathways to sustained competitiveness: (supply and demand)-environmental resonance; demand-driven (supply-environmental) assurance; and supply–demand complementarity, which together cover five specific configurations.
Reported fsQCA solution paths (three aggregated driving pathways and five specific configurations) derived from the analysis of provincial AI policy instruments.
Sustained competitiveness is achieved through multiple equivalent configurations of policy instruments (i.e., policy instrument combinations rather than single instruments).
fsQCA results reported in the paper showing multiple configurations (solution paths) that are associated with high regional competitiveness.
AI systems currently provide more consistent fraud warnings than lay humans in an identical advisory role.
Aggregate comparison from the preregistered experiment showing humans had nonzero endorsement and higher suppression rates while all tested LLMs showed 0% endorsements and lower suppression under pressure (human n=1,201; AI conversations n=3,360).
Human advisors endorsed fraudulent investments at baseline rates of 13-14%.
Human benchmark of 1,201 participants run in the preregistered experiment; reported baseline endorsement rates for fraudulent scenarios.
Motivated investor framing did not suppress AI fraud warnings; if anything, it marginally increased them.
Preregistered experiment across seven leading LLMs and twelve investment scenarios; 3,360 AI advisory conversations analyzed comparing motivated vs. baseline investor framings.
Future research should prioritize hybrid human-AI decision frameworks, robust evaluation in diverse emerging market contexts, and development of regulatory technology solutions that balance innovation with systemic stability.
Recommendations and Conclusion section derived from identified gaps and themes in the scoping review.
AI-driven approaches show substantial promise for enhancing financial risk management in emerging markets, particularly in credit scoring, fraud detection, and market forecasting.
Overall conclusion synthesizing reported improvements and application areas across the 64 studies; qualitative and quantitative findings summarized by authors.
Neural networks and ensemble methods demonstrate superior predictive accuracy compared to traditional methods.
Synthesis of comparative results across included studies indicating better predictive performance of neural networks and ensemble methods in market prediction, credit scoring, and related tasks.
Performance improvements (of AI methods) range from 15% to 35% over traditional methods.
Aggregate statement in Results summarizing reported performance improvements across reviewed studies (no single-trial RCT; based on comparative performance metrics reported by included studies).
This work provides a replicable methodology for auditing institutional ML systems and highlights the importance of evaluating construct validity alongside statistical fairness.
Paper presents the ASP-HEI Cycle-informed replica-based audit method and argues for assessing construct validity in addition to statistical fairness metrics.
We evaluate disparities by gender, age, and residency status across the full pipeline (training data, model predictions, and post-processing) using standard fairness metrics.
Paper reports conducting evaluation across the full ML pipeline using standard fairness metrics disaggregated by gender, age, and residency status.
We present a replica-based audit of a deployed Early Warning System (EWS), replicating its model using institutional training data and design specifications.
Statement in paper describing a replica-based audit using Centennial College's institutional training data and the system's design specifications; multi-year collaboration and prior ethnographic work informing approach.
Under these conditions (alignment of forces and AI-driven ideation cost reductions), PIM offers a framework for organising governed discovery in real time and provides the methodological foundation for later applied work.
The paper presents PIM as a proposed framework and positioning statement for future applied research and implementations (theoretical proposal; no applied trials reported).
Organised attacks on complex problems can generate an epistemic mode transition: a shift from predominantly Knightian uncertainty toward probabilistically characterisable innovation dynamics as relevant structures become more visible, decomposed, coordinated, and testable.
The paper states and formalises this methodological claim within PIM as a central proposition (theoretical argumentation; no empirical validation reported).
When problem-relevant causal, informational, and coordinative forces become sufficiently aligned, the epistemic character of search changes and open-ended uncertainty can be progressively transformed into structured probabilistic search.
The claim is presented as the central theoretical argument and formalised within the PIM conceptual framework (theoretical/model-based argumentation; no empirical sample).
The same user study (n=32) reports improvements in subjective measures including fluency and user preference for RAPIDDS over non-adaptive systems.
User study (n=32) reporting subjective questionnaire/ratings (fluency, preference) comparing RAPIDDS vs non-adaptive baselines.
A user study (n=32) shows significant plan improvement compared to non-adaptive systems across objective metrics such as efficiency and proximity.
User study reported in paper with sample size n=32 comparing RAPIDDS to non-adaptive systems on objective metrics (efficiency, proximity); significance claimed.
An ablation study in simulation and a physical robot scenario demonstrates the importance of dual (task + motion) adaptation.
Ablation experiments reported in paper (simulation and physical robot experiments comparing full RAPIDDS to ablated variants).
RAPIDDS jointly adapts task schedules and steers diffusion models of robot motions to maximize efficiency and minimize proximity accounting for individualized models.
Algorithmic method described in paper combining schedule optimization with motion steering (method section).
In the AI era, digital sovereignty is more plausibly pursued through institutionally governed interdependence than through technological autonomy.
Normative/conclusive argument presented by the paper (theoretical recommendation). This is an argumentative conclusion rather than an empirically demonstrated finding in the provided text.
The sovereign SLM+RAG configuration is discussed as one possible operational pathway through which the Governance Membrane architecture may be instantiated in contexts where embedded-mode governance is feasible.
Specific implementation pathway proposed/discussed by the authors (design suggestion). No empirical testing or sample information provided in the supplied text.
As a secondary, design-oriented contribution, the paper proposes the Governance Membrane as a reference architecture for operationalizing the Governed Interdependence paradigm, and introduces the Normative Compliance Model, the Infrastructure Status Index, and the Cognitive Dependence Index as complementary instruments for normative alignment and governance calibration.
Design-oriented conceptual proposal described in the paper (framework/instrument design). No empirical evaluation or sample details reported in the provided text.
The paper develops the Governed Interdependence paradigm, which reconceptualizes digital sovereignty as the institutional capacity to govern structured participation in globally distributed AI infrastructures rather than to achieve full technological autonomy.
Primary theoretical contribution described in the paper (conceptual/model development). This is a proposed framework introduced by the authors rather than an empirically validated result.
The paper provides firm-level empirical evidence from an underexplored emerging market context (Nigerian listed firms) on the relationship between AI adoption in financial reporting and audit quality.
Study sample and context are Nigerian listed firms; empirical analyses (content analysis, archival audit data, SEM) reported in the paper.
The study operationalizes AI adoption using a disclosure-based AI adoption index, representing a methodological advancement for measuring firm-level AI adoption in financial reporting.
Content analysis of corporate annual reports used to construct a disclosure-based AI adoption index; index applied in SEM analysis.
The positive relationship between AI adoption and audit quality is partially mediated by improvements in internal control quality.
SEM mediation analysis including internal control quality as a mediator; internal control quality measured through disclosure/content analysis and related archival indicators; audit quality captured via restatements and audit fees.
The positive relationship between AI adoption and audit quality is partially mediated by improvements in reporting transparency.
SEM mediation analysis including a reporting transparency measure derived from content analysis of annual reports; archival audit data used for audit quality indicators.
AI adoption is positively associated with audit quality in Nigerian listed firms.
Mixed-method quantitative design combining content analysis of corporate annual reports (to construct a disclosure-based AI adoption index) and archival audit data; Structural Equation Modeling (SEM) used to test the direct relationship. Audit quality modeled as a latent construct reflected by financial restatements and audit fees.
Sustainable development outcomes in MENA economies are driven not only by technology adoption but by the interaction between digital infrastructure, AI, and institutional readiness.
Regression models including interaction terms between digital transformation, AI measures, and indicators of institutional readiness within the System GMM analysis.
There is significant regional heterogeneity: Gulf Cooperation Council (GCC) countries exhibit stronger effects of digital transformation and AI on sustainable development than non-GCC MENA economies.
Subgroup/interaction analyses by region (GCC vs non-GCC) within the System GMM framework reported differential coefficients.