Evidence (3062 claims)
Adoption
5227 claims
Productivity
4503 claims
Governance
4100 claims
Human-AI Collaboration
3062 claims
Labor Markets
2480 claims
Innovation
2320 claims
Org Design
2305 claims
Skills & Training
1920 claims
Inequality
1311 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 373 | 105 | 59 | 439 | 984 |
| Governance & Regulation | 366 | 172 | 115 | 55 | 718 |
| Research Productivity | 237 | 95 | 34 | 294 | 664 |
| Organizational Efficiency | 364 | 82 | 62 | 34 | 545 |
| Technology Adoption Rate | 293 | 118 | 66 | 30 | 511 |
| Firm Productivity | 274 | 33 | 68 | 10 | 390 |
| AI Safety & Ethics | 117 | 178 | 44 | 24 | 365 |
| Output Quality | 231 | 61 | 23 | 25 | 340 |
| Market Structure | 107 | 123 | 85 | 14 | 334 |
| Decision Quality | 158 | 68 | 33 | 17 | 279 |
| Fiscal & Macroeconomic | 75 | 52 | 32 | 21 | 187 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Skill Acquisition | 88 | 31 | 38 | 9 | 166 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 105 | 12 | 21 | 11 | 150 |
| Consumer Welfare | 68 | 29 | 35 | 7 | 139 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 68 | 31 | 4 | 127 |
| Task Allocation | 71 | 10 | 29 | 6 | 116 |
| Worker Satisfaction | 46 | 38 | 12 | 9 | 105 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 11 | 16 | 94 |
| Task Completion Time | 76 | 5 | 4 | 2 | 87 |
| Wages & Compensation | 46 | 13 | 19 | 5 | 83 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 16 | 9 | 5 | 48 |
| Job Displacement | 5 | 29 | 12 | — | 46 |
| Social Protection | 19 | 8 | 6 | 1 | 34 |
| Developer Productivity | 27 | 2 | 3 | 1 | 33 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 8 | 4 | 9 | — | 21 |
Human Ai Collab
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Generative AI raises measurable productivity (lower marginal cost per interaction) but introduces quality and trust externalities; optimal deployment balances these trade-offs.
Pilot cost analyses and operational reports showing lower marginal costs per interaction alongside documented quality/trust issues; primarily observational and model-based reasoning.
Full automation produces trade-offs unfavorable to complex service quality and trust; hybrid models with human-in-the-loop control are preferable.
Synthesis of case studies, pilot results, and conceptual reasoning comparing fully automated routing to hybrid/human-in-the-loop deployments; limited randomized comparisons.
Generative AI can materially improve customer service productivity through 24/7 automation, scalable personalization, and agent augmentation — but is not a substitute for humans.
Synthesis of deployments, pilot studies, vendor reports, and some experimental A/B tests described in the paper; no pooled sample size provided and much evidence is short-run or observational.
Data-driven HRM reinforces skill-biased technological change: routine HR tasks are being substituted by automation while demand rises for analytical and interpersonal skills.
Theoretical implication and synthesis across studies in the review noting automation of routine tasks and increased demand for analytic/interpersonal skills.
Adoption will be heterogeneous and distributional effects will follow: organizational readiness, regulatory environments, and industry structure will drive uneven adoption and competitive impacts.
Review finds varying adoption patterns in empirical and practitioner literature and synthesizes theoretical reasons for heterogeneity; empirical causal estimates are noted as scarce.
One-off AI features typically produce limited returns unless organizations build complementary human and process capabilities and adapt governance and incentives.
Interpretive synthesis of case studies and practitioner guidance showing short-lived or limited benefits from isolated feature deployments without complementary investments.
Blockchain and decentralized fintech tools could increase transparency and access to alternative assets for women, but practical adoption barriers remain.
Qualitative assessment of blockchain capabilities and uptake surveys / case studies cited in the article (product analyses and early adoption data; no large‑scale causal evidence).
AI-enabled macro and fiscal models can improve policy testing and contingency planning but require transparency, validation, and safeguards against overreliance.
Conceptual argument and illustrative examples; no empirical trials or model performance metrics reported.
AI shifts the locus of economic governance from static rules to living systems that anticipate shocks and adapt in real time.
Policy-analytic framing and scenario-based reasoning within the book; supported by illustrative examples rather than empirical measurement.
Expected differential wage pressure: wages are likely to fall for routine/low‑skill occupations and rise or remain stable for high‑skill workers who possess complementary AI skills.
Econometric studies summarized in the review (cross‑sectional and panel regressions) and theoretical consistency with SBTC; the review highlights heterogeneity in findings and limited long‑run causal certainty.
AI contributes to skills polarization: demand rises for advanced cognitive, digital, and socio‑emotional skills while routine cognitive and manual task demand declines.
Theoretical integration (SBTC), task decomposition studies showing shifts in task demand by skill content, and labour‑market analyses reporting changes in occupational skill mixes; evidence comes from cross‑sectional and panel studies summarized in the review.
AI/ML has a dual, sector- and skill-dependent effect on labor: widespread displacement of routine and lower-skilled tasks coexists with augmentation of professional and cognitive work and the creation of new labor forms (gig, platform-mediated, and human–AI hybrid roles).
Systematic synthesis of peer‑reviewed empirical studies, industry and policy reports, task‑based analyses, and firm/establishment case studies across cross‑country and sectoral analyses; empirical approaches include econometric (cross‑sectional and panel) studies linking automation/AI adoption to employment and wages, task decomposition analyses, and surveys of firm adoption and restructuring. The review notes heterogeneity across studies and limited long‑run causal evidence.
Routine automation of routine drafting tasks by GLAI may reduce demand for junior drafting labor while increasing demand for skilled reviewers, auditors, and legal technologists.
Labor-market reasoning based on task automation literature and illustrative vignettes; no labor-force survey or longitudinal employment data provided.
The dominant mechanism behind the performance drop is a collapse of Type2_Contextual issue detection at config_B, consistent with attention dilution in long contexts.
Analysis of issue-type specific detection rates shows Type2_Contextual detection collapses at config_B; interpretation ties this to attention dilution in longer contexts.
The economic inevitability of technological transformation (in agentic finance) and the critical urgency of proactive intervention.
Author claim synthesizing the paper's argument and modeling results (normative conclusion based on earlier analysis and assertions, not a validated empirical finding).
Surveillance intensity is associated with hyper-vigilance (reported effect = -4.213).
One of the six propositions from the paper's trilevel framework; the abstract reports an effect value of '-4.213' associated with surveillance intensity → hyper-vigilance.
Platform workers receive 36.3% more third-party ratings than traditional workers.
Quantitative synthesis/summary reported in the paper (no primary sample size in abstract); likely aggregated from included studies.
Platform workers experience 59.6% higher digital speed determination than traditional workers.
Quantitative synthesis/summary reported in the paper (no primary sample size given in the abstract); presumably aggregated from included studies comparing platform and traditional workers.
Our findings surface practical limits on the complexity people can manage in human-AI negotiation.
Synthesis claim based on the empirical study varying number of issues and observed decline in performance beyond three issues; presented as a conceptual/practical implication of the results.
Bias effects vary by vulnerability type, with injection flaws being more susceptible to framing bias than memory corruption bugs.
Subgroup analysis in Study 1 comparing framing sensitivity across vulnerability classes (injection vs memory corruption) within the experiment dataset.
Low internal conflict or unanimity can be diagnostic of variance depletion (i.e., exclusion) rather than healthy integration, so governance systems should treat low conflict as a potential red flag until heterogeneity integration is verified.
Interpretive policy implication derived from the model's demonstration that exclusionary processes can produce deceptively low observed disagreement while increasing fragility; this recommendation is based on theoretical reasoning without empirical validation in the paper.
Most existing candidate matching systems act as keyword filters, failing to handle skill synonyms and nonlinear careers, resulting in missed candidates and opaque match scores.
Paper's introductory assertion about limitations of most current systems. The excerpt does not cite empirical studies, statistics, or systematic reviews to substantiate this claim.
TDD (test-driven development) prompting alone increased regressions to 9.94%.
Empirical result reported in the paper comparing a TDD prompting intervention against other workflows on the benchmark (values given in the excerpt).
Current benchmarks focus almost exclusively on resolution rate, leaving regression behavior under-studied.
Paper's critique of existing benchmark literature and practices (asserted by authors in background; no specific benchmark survey details in the excerpt).
The paper identifies five structural challenges arising from the memory governance gap: memory silos across agent workflows; governance fragmentation across teams and tools; unstructured memories unusable by downstream systems; redundant context delivery in autonomous multi-step executions; and silent quality degradation without feedback loops.
Qualitative analysis and problem framing presented in the paper (authors' identification of five specific challenges).
AI raises managerial cognitive complexity and creates recurring tensions between algorithmic optimisation and systemic, ethical reasoning.
Theoretical synthesis highlighting emergent tensions from integrating computational optimisation with systems thinking and ethical considerations; conceptual, no empirical tests.
Underprovision of verification is likely if left to market forces because information quality has positive externalities and misinformation imposes negative externalities, justifying public funding, subsidies, or regulation.
Economic reasoning and policy implications drawn from the study's findings and the literature on public goods/externalities.
Censorship, restricted data flows, and government interference fragment markets, limit economies of scale, and favor well-resourced, internationally connected actors—widening capacity gaps.
Interpretive economic analysis grounded in observed access constraints and comparative case material across the three platforms.
Limited data access and censorship reduce the efficacy of AI tools by creating training and validation gaps; legal risks complicate use of proprietary platforms and cloud services.
Interviews describing constraints on data availability and legal/operational barriers to using some platforms and cloud services; interpretive analysis of implications for AI training/validation.
Generative AI increases the volume and sophistication of misinformation (deepfakes, fabricated documents), raises false-positive risks, and can be weaponized by state or nonstate actors.
Interview accounts and qualitative analysis noting observed or anticipated misuse of generative models and associated verification challenges.
Resource constraints—limited staff time, funding, and technical capacity—are recurring operational challenges for these platforms.
Staff and stakeholder interviews plus analysis of organizational reports indicating staffing, funding, and technical limitations.
Platforms experience difficulty building and retaining audience trust and engagement, especially in contexts of high public skepticism or polarization.
Interview data from platform staff describing audience engagement challenges, supported by analysis of audience-focused platform formats and community-reporting strategies.
Platforms face limited or asymmetric access to primary data sources such as platform APIs, state data, and archives.
Interview accounts and document analysis noting restricted API access and barriers to state-held data and archives across the three cases.
Censorship and legal risks constrain reporting and distribution for these fact-checking platforms.
Consistent reports from interview subjects and corroborating document analysis indicating legal/censorship-related limitations on publishing and distribution.
Political instability, legal pressure, and censorship strongly shape what platforms can investigate, publish, and access in the region.
Thematic findings from semi-structured interviews with platform staff and document analysis of public reports and policy statements across the three country cases.
AI can augment measurement (e.g., collaboration patterns, output tracking) but if poorly designed may reinforce visibility biases that disadvantage remote workers.
Theoretical reasoning and literature citations about algorithmic bias and monitoring; illustrated with secondary examples rather than primary empirical tests.
Hybrid arrangements can exacerbate inequities in access to informal networks and career advancement, often privileging co-located or better-networked employees.
Theoretical integration of sociological and management studies with comparative case illustrations; secondary data examples referenced but no new causal empirical tests reported.
Hybrid and remote work create risks of professional invisibility, fragmented social networks, and unequal access to workplace social capital.
Literature synthesis and illustrative case studies drawn from secondary sources; qualitative/comparative case evidence rather than primary quantitative data.
Traditional STP showed a 67% performance decline after six months in unstable market conditions.
Empirical observation reported in the study—likely derived from simulation scenarios and/or longitudinal analysis of behavioral data; precise data source (simulation vs. observed field data), statistical tests, and sample framing are not specified in the summary.
The persistence of interpretive, human-in-the-loop evaluation implies ongoing labor requirements (annotation, sense-making, governance roles), affecting forecasts of automation and labor substitution in sectors adopting LLMs.
Interview reports describing continued manual work for evaluation tasks across participants; authors draw implications for labor demand.
Automation and human–robot assemblages can reproduce subjugation and vulnerability affecting care workers and marginalized users, requiring attention to distributional justice and labor-market impacts.
Illustrative vignettes from healthcare robotics and literature synthesis on care ethics and labor impacts; no quantitative labor-market analysis presented.
Legal liability regimes and insurance products may systematically under- or mis-assign costs of harm in socio-technical assemblages when primordial ethical demands are considered.
Conceptual argument and suggested modeling directions; no empirical simulation or insurance-market data presented.
Treating responsibility as a Levinasian, asymmetrical moral obligation implies it operates as a non-contractible externality that markets and contracts may fail to internalize, creating persistent externalities in AI deployment that standard economic models may miss.
Theoretical implication derived from philosophical argument applied to economic concepts; suggested consequences but no formal models or empirical validation in the paper.
Simple pluralist or multi-principle balancing approaches risk reproducing structural subordination by failing to foreground the asymmetrical ethical demand toward vulnerable Others.
Normative critique supported by cross-disciplinary literature (care ethics, mediation, STS) and illustrative examples; no empirical test of pluralist approaches’ effects.
The Levinasian framework helps reveal how human–robot interactions can both expose and reproduce systemic vulnerabilities, subjugation, and unaddressed harms (termed 'Problem C' — attribution of responsibility and distributed agency).
Theoretical diagnosis supported by interdisciplinary literature synthesis and illustrative vignettes from healthcare robotics, autonomous vehicles, and algorithmic governance. No quantitative prevalence data.
Capabilities and data advantages for certain vendors could lead to market concentration and platform dominance in AI-driven educational feedback.
Expert concern synthesized from the workshop of 50 scholars about market dynamics; theoretical warning without empirical market-structure analysis in the report.
Differential access to high-quality AI feedback systems and bias in training data can exacerbate educational inequalities and harm marginalized groups.
Expert consensus and thematic analysis from the 50-scholar workshop, raising equity and bias risks; no empirical subgroup effectiveness estimates included.
Learners may over-rely on AI feedback or game systems to obtain desirable responses, reducing effortful learning.
Workshop participant concerns synthesized qualitatively; cited as risk and an open empirical question—no experimental data provided.
Reliance on single-agent outputs or non-diverse agent ensembles can understate substantive uncertainty and bias conclusions in automated policy evaluation or AI-assisted empirical research.
Observed substantial agent-to-agent variability (NSEs) in the experiment (150 agents) demonstrating that single-agent results do not capture between-agent methodological uncertainty; imbalance between model families further implies potential bias if only one family is used.
The post-exemplar convergence largely reflected imitation of exemplar choices rather than demonstrated understanding or principled correction by agents.
Qualitative and behavioral analysis of agents' post-exposure outputs showing direct adoption of exemplar measures/procedures and lack of substantive justification or mechanistic reasoning indicating comprehension; inference based on content of agent code and writeups after exposure.