Evidence (2966 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Skills Training
Remove filter
Training humans to develop teamwork competencies, independent from task training, can enhance collaboration and performance in human-agent teams (HATs).
Overall experimental findings in KeyWe: task-independent teamwork training (<30 min) was associated with higher delegation, more strategy-based assignment, and better performance under difficulty for trained teams compared to controls.
Trained teams demonstrated resilience by achieving higher task performance when the game difficulty increased.
Performance comparison under increased difficulty in the KeyWe game between teams with trained humans and teams without training; task performance measured (score or completion metric) showed trained teams performed better under harder conditions.
The clearest added value of AI over structured self-reflection lies in increasing felt accountability.
Based on RCT comparisons showing no significant AI advantage over the written-reflection questionnaire on overall goal progress, but showing higher perceived social accountability in the AI condition and a significant mediation of the AI effect on progress via perceived accountability (indirect effect = 0.15, 95% CI [0.04, 0.31]).
AI-assisted goal setting can improve short-term (two-week) goal progress.
Aggregate interpretation based on the RCT finding that the AI condition outperformed the no-support control on two-week goal progress (d = 0.33, p = .016); two-week follow-up window specified in study.
The AI increased perceived social accountability relative to the written-reflection questionnaire.
Reported comparison from the RCT showing higher perceived social accountability in the AI condition versus the written-reflection condition; measured via self-report scales at follow-up (exact scale and statistics reported in paper).
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.
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.
Higher educational attainment is positively associated with greater willingness to keep working before retirement.
Multivariate regression analysis of the cross-sectional survey (n=889) using education level as a key explanatory variable.
Male gender is positively associated with higher willingness to remain employed before retirement.
Multivariate regression on the survey sample (n=889) including gender as an explanatory variable, controlling for demographic and socioeconomic covariates.
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).
Adoption of AI feedback could lower marginal costs of delivering high-quality feedback and change fixed vs. variable cost structures for instruction delivery.
Economic implication discussed by workshop participants (50 scholars) as a theoretical possibility; no quantitative cost estimates in the report.
Generative AI can enable new feedback modalities (text, hints, worked examples, formative prompts) adaptable to content and learner needs.
Thematic conclusions from the interdisciplinary meeting of 50 scholars, describing possible modality generation capabilities of current generative models; no empirical modality-comparison data provided.
Immediate AI-generated feedback may sustain learner momentum and improve formative assessment cycles (timeliness & engagement).
Expert-opinion synthesis from structured workshop (50 scholars) identifying timely feedback as a potential pedagogical benefit; no empirical trials reported.
Large language and generative models can tailor explanations, scaffolding, and practice to learners' current states and preferences (personalization).
Workshop expert consensus and thematic synthesis from 50 interdisciplinary scholars; illustrative examples discussed rather than empirical evaluation.
Generative AI can produce real-time, individualized feedback at scale, potentially reducing per-student feedback costs and increasing feedback frequency.
Synthesis of expert perspectives from an interdisciplinary workshop of 50 scholars (educational psychology, computer science, learning sciences); qualitative small-group activities and thematic extraction. No primary experimental or quantitative cost data presented.
Agents learn from one another without curricula (agent-to-agent learning occurs organically in the ecosystem).
Naturalistic daily observations across platforms noting peer-to-peer agent interactions and apparent transfer of behaviors/knowledge; no controlled tests of learning or counterfactuals.
Agents form idea cascades and quality hierarchies without any centrally designed curriculum or intervention (emergent peer learning and spontaneous knowledge diffusion).
Observed interaction patterns across platforms showing cascades, hierarchies, and diffusion among agents in the qualitative dataset; documentation is comparative and observational rather than experimental.
A rapidly growing ecosystem of autonomous AI agents is producing organic, multi-agent learning dynamics that go beyond dyadic human–AI interactions.
Naturalistic, qualitative daily observations over one month across multiple agent platforms (reported platforms: Moltbook, The Colony, 4claw); coverage reported of >167,000 agents interacting as peers; comparative observational documentation rather than controlled experimentation.
Open-source orchestration and evaluation harnesses plus a self-contained evaluation pipeline improve reproducibility for the Speedrunning Track.
Paper claims and documents the release of orchestration and evaluation code and describes the self-contained pipeline designed for deterministic reproducible evaluation.
Version 1.0 marks integration into operational workflows and establishes a base for future capabilities.
Authors report that v1.0 has been used in verification and mask-refinement loops for real datasets (MeerKAT, ASKAP, APERTIF); no detailed deployment metrics provided.
Immersive inspection tools like iDaVIE are complements to automated ML pipelines by helping generate higher-quality labels and curated training examples.
Paper argues conceptual complementarity and cites iDaVIE's use for mask refinement and curated subcube export; no experimental comparison of label quality or downstream ML performance provided.
iDaVIE accelerates inspection-driven parts of astronomy workflows (e.g., mask refinement, verification).
Reported use cases where iDaVIE was used to refine masks and verify sources in real datasets; no measured time-per-task or throughput statistics provided.
iDaVIE has already been integrated into real pipelines (MeerKAT, ASKAP, APERTIF) and used to improve quality control, refine detection masks, and identify new sources.
Author statement of integration and use cases citing verification of HI data cubes from MeerKAT, ASKAP and APERTIF; no quantitative deployment counts or independent validation provided in the text.
The taxonomy and measurement approach provide operational metrics to quantify empathic communication for economic analyses (productivity, customer satisfaction, retention).
Authors propose that their data-driven taxonomy and automated/coding measures can be used as metrics; the paper demonstrates derivation and use in trial outcomes but does not present direct economic outcome measurements.
LLM-generated responses frequently score as more empathic than human-written responses in blinded evaluations.
Blinded evaluations comparing LLM-generated replies to human-written replies using recipient/judge ratings of perceived empathy (reported in blinded tests described in paper). Exact blinded-test sample sizes not specified in the summary but derived from the study's evaluation procedures.
Employers are increasingly demanding digital literacy, basic data competencies, and stronger communication and interpersonal skills.
Employer survey analysis tracking changes in required skills; descriptive summary of survey frequencies and employer-reported skill priorities. Survey sample size and representativeness not specified in summary.
Some occupations experience efficiency and productivity gains where AI complements tasks, implying complementarity effects for those jobs.
Qualitative case studies of firms and employer survey reports documenting productivity/efficiency improvements in certain roles following AI adoption; descriptive analysis of sectoral/occupational outcomes. Quantitative magnitude not specified.
Policy implication: prioritize large-scale, targeted reskilling and lifelong learning programs to enable workforce adaptability and capture AI complementarity gains.
Policy recommendations derived from the paper's findings (association between AI adoption and skill shifts, heterogeneous sectoral impacts) and the literature synthesis that links reskilling interventions to better labor outcomes; recommendation is prescriptive rather than empirically tested within the study.
The paper provides empirical support for the complementarity hypothesis: AI tends to reconfigure jobs and create hybrid roles rather than eliminate employment wholesale.
Convergence of simulated sectoral employment patterns (some sectors showing net gains and hybrid-role growth), the strong correlation between AI adoption and skill shifts (r = 0.71), and corroborating studies from the literature synthesis emphasizing augmentation and hybridization mechanisms.
Institutional reskilling programs and governance frameworks markedly moderate labor-market outcomes: better frameworks correlate with more complementarities and lower net job loss.
Integration of literature-derived mechanisms with simulated empirical patterns; paper reports correlations/moderation-style comparisons across simulated sector-year cases incorporating policy/institutional variables (described in methods), supported by studies in the systematic review linking policy interventions to labor outcomes.
Healthcare and IT Services experienced net employment gains consistent with AI complementarity (augmented tasks and creation of new hybrid roles).
Simulated sectoral employment trends and net-change metrics for Healthcare and IT Services (2020–2024) presented in the paper, supported by literature synthesis examples showing human–AI complementarities in these sectors.
The largest rises in hybrid jobs occurred in IT Services and Healthcare.
Sectoral decomposition of hybrid job share trends in the simulated dataset across the seven industries (2020–2024) and supporting qualitative/quantitative findings from the literature synthesis focused on IT Services and Healthcare.
Hybrid human–AI jobs increased substantially across all seven analyzed sectors between 2020 and 2024.
Descriptive trend analysis of the simulated dataset's hybrid job share metric (fraction of roles reclassified as human–AI hybrid) for the seven industries over 2020–2024, combined with corroborating examples from the literature synthesis (selected ACM/IEEE/Springer studies 2020–2024).
Responsible implementation requires legal/liability clarity, continuous monitoring for performance drift and distributional shifts, usable explanations, baseline AI literacy for clinicians, and co-design with frontline radiology teams.
Synthesis of governance literature, implementation best-practice reports, and recommendations from usability and deployment studies.
Triage and automation can shorten time-to-diagnosis, increase throughput, and reduce time spent on repetitive tasks.
Observational deployment reports and simulation studies that measured time-to-report or throughput improvements in pilot settings (evidence heterogeneous and context-dependent).
Integration points for AI across the imaging pathway include acquisition (image quality/protocol selection), triage (prioritization), interpretation/reporting (detection, quantification, report pre-population), and post-interpretation (teaching, QA, model improvement loops).
Descriptive synthesis of reported implementations and proposed use cases in the literature and deployment reports across multiple institutions.
Human-AI collaboration can produce synergistic gains (diagnostic complementarity) when errors are uncorrelated and tasks are allocated to leverage comparative strengths.
Theoretical/analytical models of error complementarity and empirical reader studies showing instances where combined readings outperform either agent alone (evidence drawn from multiple small-to-moderate reader studies and simulations).
AI in radiology has clear potential to improve diagnostic performance and workflow efficiency.
Narrative synthesis of laboratory evaluation studies, reader/comparison studies, and a limited number of observational deployment reports showing improved algorithm accuracy and some improvements in measured throughput or time-to-review in pilots (study sizes and settings heterogeneous; few large-scale RCTs).
Cognitive Shadow supports real-time model updates based on immediate user feedback, enabling iterative improvement and continuous alignment with human decision patterns.
Described human-in-the-loop interaction loop where CS captures human decisions, provides recommendations, receives immediate feedback, and updates models dynamically in the simulation environment (implementation detail).