Evidence (4892 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
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Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Org Design
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AI excels at hypothesis generation but cannot replace scientific reasoning and experimental validation; human expertise remains essential.
Argument and case examples in the paper showing AI-generated hypotheses requiring human-led experimental design, interpretation, and validation.
The research methodology combines systemic analysis, comparative assessment of international practices, and analytical generalization of organizational learning models, enabling capture of both structural trends and concrete institutional responses to technological changes.
Methodological statement from the paper describing its approach; this is a factual claim about methods used rather than an empirical finding.
The validity of human–AI decision-making studies hinges on participants' behaviours; effective incentives can potentially affect these behaviours.
Conclusion from the authors' thematic review and theoretical rationale linking incentive design to participant behaviour and study validity (no quantitative effect sizes provided in excerpt).
The study's counterfactual analytical model links HR indicators (training intensity, absenteeism, labor productivity, turnover rates, workforce allocation) to organizational performance outcomes using regression-based simulations and predictive estimation.
Methodological claim explicitly stated: model construction from an industrial firm dataset using regression-based simulations and predictive techniques. (Specific sample size, variable operationalizations, and time frame not reported in the description.)
A minimal linear specification (linearized model) demonstrates how coupling strength, persistence, and dissipation determine local stability and oscillatory regimes through spectral conditions on the Jacobian.
Analytic linear model and local stability analysis in the paper: computation of Jacobian, derivation of spectral conditions (eigenvalue locations) that separate stable/oscillatory regimes; illustrative examples within the paper (no empirical data).
We develop a theoretical framework - the productivity funnel - that traces how technological potential narrows through successive stages, from access and digital infrastructure, through organizational absorption and human capital adaptation, to ultimate value capture.
Conceptual/theoretical development presented in the paper; no empirical sample needed (framework-building).
Effects of curated Skills are highly heterogeneous across domains (e.g., +4.5 pp in Software Engineering vs. +51.9 pp in Healthcare).
Per-domain pass-rate deltas reported in the paper (SkillsBench per-domain analysis). The example domain deltas (+4.5 pp and +51.9 pp) are taken from the reported per-domain results.
Scholarly production, institutional incentives, funding, and the Cold War geopolitical context shaped which economic theories became prominent.
Historical institutional case study drawing on archives, correspondence, publication records, and contemporaneous debates to link institutional and funding environments to intellectual trajectories.
Whether AI increases or decreases overall inequality depends on AI’s technology structure (proprietary vs. commodity) and on labor-market institutions (rent‑sharing elasticity ξ and asset concentration).
Comparative statics and regime analysis within the calibrated model that varies the technological-form parameter (η1 vs. η0) and the rent‑sharing elasticity ξ, as well as measures of asset concentration.
AI can equalize individual task performance while increasing aggregate inequality because rents accrue to owners of complementary assets rather than to workers.
Analytical model and calibrated simulations demonstrating that within-task compression (reduced worker dispersion) can coexist with rising aggregate inequality (ΔGini) owing to rent concentration at the firm/asset-owner level.
The study's qualitative and exploratory design limits generalizability; the proposed framework requires quantitative testing and broader samples (practicing architects, firms, cross-cultural contexts).
Explicit limitations stated by authors; study is based on semi-structured interviews with architecture students (N unspecified) and inductive thematic analysis.
Important tradeoffs exist (privacy vs. utility; centralized vs. federated data architectures; automated moderation vs. freedom of expression; cost/complexity of secure hardware) that must be balanced in VR security design.
Comparative evaluation across the reviewed corpus (31 studies) identifying recurring ethical and technical tradeoffs; authors discuss these qualitatively.
Emotional redirection is common: 33% of fear-tagged posts receive joy-tagged responses.
Post–response emotion transition analysis using the emotion-labeled dataset; calculation of conditional probability that responses to fear-tagged posts are labeled joy (observed rate ≈33%) in Moltbook threads.
Self-reflective discussion was concentrated in Science & Technology and Arts & Entertainment topical categories, while Economy & Finance threads showed no self-referential content.
Topic modeling and manual/automatic tagging of self-referential themes across identified topical categories within the Moltbook dataset; category-level counts showing presence/absence of self-referential tags (dataset: 361,605 posts).
The topology of service-dependency graphs (modelled as DAGs of compute stages) is a first-order determinant of whether decentralised, price-based resource allocation will be stable and scalable.
Systematic ablation study using simulation: 1,620 runs total across six experiment types, sweeping graph topology (hierarchical vs cross-cutting), load, hybrid integrator presence, and governance constraints; metrics included price convergence/volatility and allocation throughput/quality. Effect sizes reported in the paper show topology had the largest impact on price stability and scalability.
Choice of scaffold materially affects outcomes: an open-source scaffold outperformed vendor-provided scaffolds by up to approximately 5 percentage points.
Comparative experiments across three scaffolding approaches (vendor scaffolds and at least one open-source scaffold) showing up to ~5 percentage point differences in measured outcomes.
Explanations change workflows, shift responsibilities between humans and machines, and can reshape power dynamics—creating both opportunities (better oversight) and risks (over-reliance, gaming).
Qualitative and conceptual studies synthesized in the review, including socio-technical analyses and case studies reporting observed or theorized workflow and responsibility shifts; no meta-analytic causal estimate.
Explanations increase user trust principally when they are understandable, actionable, and aligned with users’ domain knowledge; opaque or overly technical explanations can fail to build trust or even decrease it.
Thematic synthesis of empirical and conceptual studies in the reviewed literature reporting conditional effects of explanation form and comprehensibility on trust; review notes heterogeneity in study designs and contexts.
Explainability improves perceived legitimacy, user trust, and organizational accountability only when technical transparency is paired with human-centered explanation design and governance mechanisms.
Synthesis of studies from the reviewed literature showing conditional effects of algorithmic interpretability combined with explanation design and governance; derived via thematic coding across technical and social-science sources (no new primary experimental data reported).
Explainability is a necessary but not sufficient condition for trustworthy AI in high-stakes domains.
Systematic literature review (thematic coding and synthesis) of interdisciplinary scholarship (peer-reviewed research, technical reports, policy documents); the paper synthesizes conceptual and empirical studies rather than presenting new primary data. Emphasis on high-stakes domains (healthcare, finance, public sector).
Data‑driven policies can either amplify or mitigate inequalities depending on data representativeness, model design, and deployment governance.
Multiple empirical examples and theoretical analyses in the review highlighting cases of both harm (bias amplification) and mitigation, identified across the 103 items.
Citizen acceptance, transparency, and perceived fairness strongly shape adoption trajectories and the political feasibility of AI tools in government.
Repeated empirical findings in the reviewed literature linking public trust, transparency measures, and fairness perceptions to successful or failed deployments (drawn from multiple case studies in the 103 items).
Adoption of AI and data-driven governance is highly uneven across jurisdictions and sectors, driven by institutional capacity, governance frameworks, and public trust.
Cross‑regional and cross‑sector comparisons in the review corpus (103 items) showing varying maturity levels and repeated identification of institutional capacity, governance arrangements, and trust factors as determinants.
Productivity gains from generative AI depend on task mix, integration design, and the availability of complementary human skills.
Theoretical evaluation and synthesis of heterogeneous empirical findings; authors highlight variation across firms, sectors, and tasks.
Methodological caveats across the literature (heterogeneity of tasks/measures, publication bias, short-term studies) limit the generalizability of current findings.
Meta-level critique within the synthesis noting study heterogeneity, likely publication/short-term biases, and variable domain-specific performance dependent on user expertise and workflows.
Standard productivity metrics are likely to undercount the value generated by AI-augmented ideation; quality-adjusted measures of creative output are required.
Measurement critique based on the mismatch between existing productivity statistics and the kinds of upstream idea-generation gains observed in empirical studies; supported by the review's methodological discussion.
Realized value from AI methods (ML, predictive analytics, anomaly detection, XAI) is conditional: these technical methods deliver capabilities only when combined with strong data governance, standardized processes, and change management.
Thematic synthesis across the systematic review (2020–2025) showing repeated case-study and practitioner-report evidence that technical gains failed to scale without governance, process standardization, and organizational change efforts.
The hybrid estimator (GA+SQP) is computationally more intensive than single-stage MLE/local optimization, implying a trade-off between estimation reliability and runtime cost.
Reported runtime and computational cost comparisons in estimation experiments: the paper notes longer runtimes for GA+SQP versus standard optimizers while documenting improvements in objective values and convergence behavior.
Applying differential privacy to model updates provides a bounded formal guarantee on information leakage, but DP noise budgets and communication constraints create accuracy and latency trade-offs that must be managed.
Analytical treatment of DP's impact on learning (trade-off modeling) and qualitative simulation examples showing accuracy degradation under DP noise; no numeric privacy-utility curves from field deployments provided.
AI intensifies market concentration, reinforcing winner-takes-most dynamics through data-driven network effects.
Synthesis of market-structure and industrial-organization studies in the SLR reporting evidence of increased concentration and network/data advantages favoring incumbents.
AI displaces routine occupations.
Synthesis of empirical and modeling studies within the 78-study SLR reporting occupational/task-level substitution effects for routine activities.
Dimensional diagnosis identified that 69% of hallucination failures were prompt-induced interpretation errors—these were invisible in aggregate scoring.
Result from the paper's sales-intelligence case study reporting failure-mode breakdown (percentage reported: 69%).
Naively persisting entire conversation histories is token-inefficient and counterproductive because irrelevant context degrades generation quality.
Argumentation in the paper supported by empirical finding that full-history persistence reduced task completion; also conceptual token-efficiency rationale.
Naive full-history persistence actively degrades task completion (by biasing the agent with stale traces) compared to no memory and selective memory.
Empirical comparison reported in the paper showing full-history persistence produced 71% completion vs. 79% for no memory and 96% for selective memory; rationale given that stale reasoning traces bias agents.
These factors (surveillance anxiety, loss of autonomy, deskilling) negatively affect worker well-being and contribute to turnover.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). The paper synthesizes prior empirical and theoretical studies but does not report an original sample size.
Automation and algorithmic systems introduce risks of deskilling that affect workers' capabilities.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). No primary sample size stated.
Algorithmic management reduces worker autonomy (loss of autonomy) in warehouse settings.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). Sample sizes not reported in this paper.
Algorithmic management in automated logistics generates surveillance anxiety among workers.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). No sample size given.
AI use can reduce visibility of real skill differences among employees.
Reported findings from performance management and knowledge-work studies indicating that AI-mediated outputs can obscure underlying employee skill variation.
Use of AI can produce over-reliance on AI recommendations, reducing active human judgment and accountability.
Cited empirical observations and prior literature on automation bias and AI-supported decision processes in organizational settings.
AI systems miss contextual information that humans use to make better decisions.
Examples and studies cited from hiring, performance management, healthcare, and knowledge work demonstrating omissions of context by AI tools.
Empirical studies of AI use show recurring problems including mistakes in unusual cases.
Cited recent studies across domains (hiring, performance management, healthcare, knowledge work) reporting AI errors on atypical or edge-case instances.
Human judgment rooted in experience cannot be fully replaced by current AI systems.
Argument based on literature synthesis drawing on cognitive science, neuroscience, and organizational studies; supported by cited recent empirical studies of AI use in hiring, performance management, healthcare, and knowledge work (no single new experiment reported).
The study highlights the limited integration of GenAI in the choice phase of organizational decision-making.
Analysis of task-to-component mappings from the 68 reviewed studies showing relatively fewer GenAI applications mapped to the 'choice' component compared to other components.
Our findings reveal a fragmented application landscape for GenAI in organizational decision-making.
Synthesis of the 68 reviewed publications showing diverse, heterogeneous uses of GenAI across tasks and categories; authors describe the landscape as fragmented.
Existing studies are largely fragmented across industries, organizational contexts, and individual AI applications, with limited systematic evidence synthesizing how AI-aided SIS tools collectively influence organizational performance and sustainable competitive advantage.
Findings from the PRISMA-guided literature search and eligibility assessment that resulted in 22 included studies; thematic analysis highlighted heterogeneity and gaps in the literature.
Despite benefits, challenges persist including data privacy concerns, algorithmic bias, ethical risks, workforce skill gaps, organizational resistance, and high implementation costs.
Recurring themes identified across the 22 studies included in the PRISMA-guided systematic review (Scopus, ScienceDirect, Google Scholar searches, 2017–2026) and summarized via thematic analysis.
Repository-mining studies measure surface trends but seldom explain the mechanisms beneath them, and the trends themselves prove unstable.
Critical observation by the authors supported by their own GitHub observational analysis showing sensitivity of trends to analysis choices; presented as an interpretive claim in the paper.
Agent-authored pull requests are discussed less than human-authored ones.
Observational analysis of public GitHub activity reported in the paper (no sample size reported in abstract); comparison of discussion volume/length for agent- vs human-authored PRs.
Agent-authored pull requests are reviewed less often than human-authored ones.
Observational analysis of public GitHub activity reported in the paper (no sample size reported in abstract); comparison between agent-authored and human-authored pull requests.