Evidence (4560 claims)
Adoption
5267 claims
Productivity
4560 claims
Governance
4137 claims
Human-AI Collaboration
3103 claims
Labor Markets
2506 claims
Innovation
2354 claims
Org Design
2340 claims
Skills & Training
1945 claims
Inequality
1322 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 378 | 106 | 59 | 455 | 1007 |
| Governance & Regulation | 379 | 176 | 116 | 58 | 739 |
| Research Productivity | 240 | 96 | 34 | 294 | 668 |
| Organizational Efficiency | 370 | 82 | 63 | 35 | 553 |
| Technology Adoption Rate | 296 | 118 | 66 | 29 | 513 |
| Firm Productivity | 277 | 34 | 68 | 10 | 394 |
| AI Safety & Ethics | 117 | 177 | 44 | 24 | 364 |
| Output Quality | 244 | 61 | 23 | 26 | 354 |
| Market Structure | 107 | 123 | 85 | 14 | 334 |
| Decision Quality | 168 | 74 | 37 | 19 | 301 |
| Fiscal & Macroeconomic | 75 | 52 | 32 | 21 | 187 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Skill Acquisition | 89 | 32 | 39 | 9 | 169 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 106 | 12 | 21 | 11 | 151 |
| Consumer Welfare | 70 | 30 | 37 | 7 | 144 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 68 | 31 | 4 | 127 |
| Task Allocation | 75 | 11 | 29 | 6 | 121 |
| Training Effectiveness | 55 | 12 | 12 | 16 | 96 |
| Error Rate | 42 | 48 | 6 | — | 96 |
| Worker Satisfaction | 45 | 32 | 11 | 6 | 94 |
| Task Completion Time | 78 | 5 | 4 | 2 | 89 |
| Wages & Compensation | 46 | 13 | 19 | 5 | 83 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 17 | 9 | 5 | 50 |
| Job Displacement | 5 | 31 | 12 | — | 48 |
| Social Protection | 21 | 10 | 6 | 2 | 39 |
| Developer Productivity | 29 | 3 | 3 | 1 | 36 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Skill Obsolescence | 3 | 19 | 2 | — | 24 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Labor Share of Income | 10 | 4 | 9 | — | 23 |
Productivity
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Automated feedback may not capture pedagogical nuances expert teachers use (motivation, socio-emotional cues, complex reasoning), limiting pedagogical fit.
Expert syntheses from the workshop of 50 scholars highlighting limits of automation relative to expert teacher judgment; no empirical comparisons presented.
AI-generated feedback can be incorrect, misleading, or misaligned with learning objectives; assessing feedback quality is nontrivial.
Repeated concern raised across workshop participants (50 scholars) in qualitative synthesis; noted as a substantive risk and open challenge rather than empirically quantified here.
Using C.A.P. entails trade-offs: potential increases in latency and compute cost and a risk of over-correction (unnecessary clarification).
Paper explicitly notes these trade-offs as part of the design discussion and proposes measuring latency, compute cost, and unnecessary clarification rate in evaluations; this is an acknowledged design risk rather than an empirically quantified result.
Integration costs—domain modeling, human-in-the-loop protocols, and regulatory/liability frameworks—are significant barriers to deployment.
Conceptual assessment of operational and regulatory requirements; no quantified cost studies provided.
AFs and LLMs may be gamed or misled; adversaries may exploit systems leading to strategic argumentation or manipulation.
Conceptual security/adversarial concern based on known vulnerabilities in ML and strategic behavior; no adversarial tests reported.
Faithful extraction—aligning LLM-extracted arguments with formal AF primitives and ensuring fidelity to source evidence—is a key technical challenge.
Paper's explicit identification of failure modes and alignment issues; grounded in documented limitations of IE/LLMs (no empirical quantification here).
Computational argumentation approaches have required heavy feature engineering and domain-specific knowledge to be effective.
Conceptual claim grounded in prior work and practical experience reported in the literature; no quantitative cost estimates provided in the paper.
Automation bias (human tendency to defer to automated outputs) compounds the risk that GLAI errors become embedded in legal processes.
Behavioral literature review on automation bias and trust in AI systems; applied to legal-context vignettes. No primary empirical test within the paper.
A key architectural risk is interoperability failure and fragmentation across vendors and protocols in agent ecosystems.
Comparative analysis with IoT and other platform histories showing vendor/protocol fragmentation; argument is conceptual and illustrative rather than empirically measured for future agent ecosystems.
Domains such as disaster response, healthcare, industrial automation, and mobility will be affected and are safety‑critical, where failures have high social and economic cost.
Domain examples and policy reasoning; draws on general knowledge about those sectors and potential harms; no new empirical damage quantification provided in the paper.
IoT digitized perception at scale but exposed limitations such as fragmentation, weak security, limited autonomy, and poor sustainability.
Historical and comparative analysis of IoT deployments and literature cited illustratively in the paper; qualitative evidence from prior IoT incidents and ecosystem studies rather than new empirical data.
Adoption requires hardware (VR headsets, capable GPUs) and integration effort, implying upfront capital expenditure for labs/observatories.
Paper explicitly notes hardware requirements (VR headsets, capable GPUs) and integration effort as part of adoption considerations; common-sense assessment of required capital.
Current models heavily rely on large static datasets and batch training and exhibit poor lifelong/continual learning.
Synthesis of common practices in contemporary ML (supervised pretraining and offline training paradigms); no new experiments provided.
When identical replies are labeled as coming from AI rather than from a human, recipients report feeling less heard and less validated (an attribution effect).
Controlled attribution labeling experiment within the study: identical replies presented with different source labels (AI vs. human) and recipient-rated perceptions of being heard/validated measured.
HindSight scores are negatively correlated with LLM-judged novelty (Spearman ρ = −0.29, p < 0.01), indicating LLM judges tend to overvalue novel-sounding ideas that do not materialize in the literature.
Reported Spearman correlation between HindSight scores and LLM-judged novelty across the generated ideas; ρ = −0.29 with p < 0.01. Interpretation that LLMs overvalue novel-sounding ideas is drawn from the negative correlation.
Barriers to adoption include toolchain cost, trace data storage/transfer demands, IP-security concerns when sharing traces, and organizational inertia.
Listed as practical caveats and limitations in the summary; based on authors' experience and reasoning rather than quantified study.
Adoption requires up-front investment in tooling and infrastructure for deterministic capture/replay, plus management of large trace data and integration with existing validation/IP/security workflows.
Authors explicitly list these practical caveats in the summary: needs tooling/infrastructure, trace data management, and integration with validation flows and IP/security constraints. (Descriptive claim based on implementation experience; no cost figures provided.)
Static ACLs evaluate deterministic rules that ignore partial execution paths and therefore can only capture a subset of organizational constraints.
Formal argument and examples showing static ACLs map to Policy functions that do not depend on partial_path; illustrative limitations presented.
Runtime evaluation imposes additional compute, latency, logging, and engineering costs that increase the marginal cost of deploying agents.
Operational discussion in the paper outlining additional runtime compute and logging requirements; cost implications argued qualitatively; no empirical cost measurements provided.
Prompt-level instructions and static access control lists (ACLs) are limited special cases of a more general runtime policy-evaluation framework and cannot, in general, enforce path-dependent rules.
Formalization showing prompt/system messages and static ACLs map to restricted forms of the Policy(agent_id, partial_path, proposed_action, org_state) function; logical proof/argument in the paper and illustrative counterexamples.
LLM-based agent behavior is non-deterministic and path-dependent: an agent's safety/compliance risk depends on the entire execution path, not just the current prompt or single action.
Formal/abstract execution model defined in the paper (states, actions, execution paths) and conceptual arguments/illustrative examples showing how earlier states/actions affect later behavior; no large-scale empirical dataset reported.
Real-world deployment will require representative data coverage and online adaptation despite the method’s robustness mechanisms.
Authors' discussion/limitations section: theoretical requirements for persistently exciting/representative trajectories for DeePC and recommendation for online adaptation and continual data collection for deployment.
Agent performance degrades markedly as environment complexity, stochasticity, and non-stationarity increase, revealing core limitations of current LLM-based agents for long-horizon, multi-factor decision problems.
Experimental results across progressively harder RetailBench environments showing performance falloff for multiple LLMs under increased task complexity and non-stationarity.
Proactive AI at national scale amplifies concerns around transparency, accountability, privacy, and potential misuse, necessitating robust regulatory and ethical frameworks.
Normative and ethical analysis in the paper, supported by general literature on large-scale AI governance; no empirical assessment of regulatory effectiveness in Russia included.
Aggregating informal and recommendation data raises privacy and consent issues in low-regulation contexts, requiring governance safeguards.
Policy and ethical consideration based on the nature of the data used; no specific privacy-impact assessment reported in the summary.
NLP/ML systems can inherit biases from inputs (underrepresentation, noisy self-reports, biased recommendations) and may therefore disadvantage some youth unless transparency and fairness constraints are implemented.
Reasoned risk assessment grounded in known properties of ML/NLP; the pilot summary does not report an audit or measured bias outcomes.
There are limited randomized controlled trials or longitudinal evaluations; few studies measure patient-relevant outcomes or economic impacts.
Literature synthesis noting scarcity of RCTs and long-term observational studies, and absence of widespread patient-outcome and cost-effectiveness evaluations in existing publications.
Many published studies focus on standalone algorithm accuracy rather than clinician–AI joint performance in routine workflows.
Review of the literature categorizing study designs (preponderance of algorithm development/validation studies, fewer reader-in-the-loop, simulation, or deployment studies).
Regulators and payers remain central bottlenecks—AI can accelerate discovery but cannot bypass clinical evidence requirements.
Policy discussion and regulatory analysis in the paper noting that approvals require clinical evidence independent of discovery modality.
Downstream clinical development costs and translational failure rates remain the major drivers of total R&D expenditure; early-stage AI savings may not translate into proportionate increases in approved drugs.
Economic analysis and discussion in the paper referencing known cost distributions in drug development and historical attrition rates in clinical phases.
Inherent biological complexity and translational gaps between in silico predictions, preclinical models, and human biology constrain downstream success rates.
Review of translational failures and literature cited in the paper demonstrating mismatch between preclinical signals and clinical outcomes; conceptual analysis of biological complexity.
Gaps exist between computational designs and chemical/experimental feasibility (e.g., synthetic accessibility and assay readiness), limiting the usefulness of some generative outputs.
Case studies and critiques in the paper showing generated molecules that are synthetically infeasible or incompatible with experimental constraints; discussion of missing integration of practical constraints in many generative models.
Many models have limited interpretability and insufficient uncertainty quantification, hampering trust and decision-making.
Methodological analysis in the paper noting common deep-learning approaches lacking clear interpretability and uncertainty estimates; references to literature on model explainability and calibration gaps.
Poor data quality, fragmentation, and limited accessibility reduce model reliability and generalizability.
Survey of data characteristics and limitations presented in the paper; examples of biased or sparse datasets and the paper's discussion of impacts on model performance and transferability.
AI remains an augmenting technology rather than a standalone solution: no AI-only originated drug has yet achieved regulatory approval.
Review of drug-approval records and company disclosures summarized in the paper; explicit statement that to date no entirely AI-originated molecule has received full regulatory approval.
Ethical and legal issues—patient privacy, algorithmic bias, intellectual property, and equitable access—pose risks to AI deployment in drug development.
Ethics and legal analyses, policy reports, and documented case examples collated in the review that identify these recurring concerns.
Regulatory uncertainty about validation standards and liability for AI tools raises investment risk and may slow deployment.
Regulatory and policy reports included in the narrative review describing evolving standards and open questions about validation, explainability, and liability for ML-based tools.
Adoption of AI in drug R&D requires high upfront investment in data curation, compute infrastructure, and specialized talent.
Industry reports and economic analyses summarized in the review reporting capital and operational needs for building AI capabilities; qualitative synthesis rather than quantitative costing across firms.
Limited transparency and interpretability of many AI algorithms (black-box models) complicate clinical and regulatory trust and adoption.
Regulatory reports, methodological critiques, and case examples in the review highlighting interpretability concerns and their impact on clinical/regulatory acceptance.
Performance of AI models in drug R&D depends on large, high-quality, and representative biomedical datasets; dataset bias or gaps substantially undermine model performance and generalizability.
Methodological literature and case studies cited in the review documenting failures or limited generalization when training data are biased, sparse, or non-representative; thematic synthesis rather than pooled quantification.
Predictions from AI depend on data quality and coverage and still require experimental (wet-lab) validation.
Discussion of early failures and limits in case studies and expert observations within the narrative review; methodological argument about dependence of ML models on input data.
High-quality, standardized, interoperable data (clean, annotated, connected across modalities) is a critical limiting factor for translating AI capability into sustained impact.
Conceptual emphasis and domain knowledge argument in the editorial; no empirical measurement of data quality's causal effect included.
The paper's evidence base is limited by early-stage projects with limited longitudinal outcome data and dependence on publicly available project information which may be incomplete or biased.
Methods and limitations explicitly stated in the paper (qualitative review; reliance on secondary sources; two case studies; absence of large-scale quantitative evaluation).
Data protection and privacy (especially sensitive health data) complicate open-data DAO models.
Conceptual analysis referencing privacy/data-protection concerns for health data (e.g., GDPR-like regimes); no empirical evaluation of privacy breaches within DAOs provided.
Significant barriers remain for DAOs in pharma: regulatory uncertainty about tokenized securities, IP fractionalization, and clinical data sharing.
Legal/regulatory analysis and literature synthesis highlighting unclear classifications and open regulatory questions; no new regulatory rulings provided.
Pharmaceutical R&D faces rising costs, long approval timelines, supply-chain inefficiencies, and low patient involvement.
Literature review and synthesis of well-documented industry challenges cited in the paper (secondary sources); no new primary data presented in this study.
The black-box nature of many deep learning models undermines scientific interpretability and experimental trust, limiting adoption in materials research.
Cited concerns and methodological papers advocating interpretable architectures and post hoc explanation methods reviewed in the paper; synthesis of community critique.
Insufficient attention to model reliability—particularly uncertainty miscalibration—reduces real-world utility because experimentalists need reliable confidence estimates, not only point predictions.
Survey of literature on uncertainty estimation and calibration (Bayesian NNs, ensembles, temperature scaling, conformal prediction) and papers reporting calibration issues; recommendations drawn from these sources.
Progress of DL-driven materials discovery is limited by scarcity of high-quality, diverse labeled datasets; small, noisy, or biased datasets limit model generalization.
Review and synthesis of empirical studies and methodological papers documenting dataset size/quality issues and their impact on model performance; no new dataset analysis in this paper.
Advanced technologies' complexity and lack of explainability create risks for audit reliability and professional judgement.
Findings from literature synthesis and professional/regulatory perspectives included in the review; presented as an identified risk/challenge rather than quantified effect.