Evidence (7560 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).
Browse by theme
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 |
Human Ai Collab
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Test files lacking explicit assertions execute code without verifying behavior, so quality gates based on test-file presence overestimate verification strength.
Conceptual/analytic argument supported by the paper's framing and subsequent empirical analysis of oracle signals.
The paper highlights where assumptions, such as rationality and heterogeneity, may fail in agentic markets.
Analytical critique and theoretical examples/arguments in the paper pointing out potential breakdowns of classical assumptions when agents act on users' behalf; no empirical tests provided.
Disclosing AI involvement in visual content creation is associated with a weaker funding penalty than disclosing AI involvement in textual content creation.
Subgroup/moderation analysis within the same dataset (41,073 Kickstarter projects) comparing projects that disclosed AI-use in visual modalities versus textual modalities, using LLM-assisted classification to determine modality and entropy balancing for covariate adjustment.
AI-use disclosure is associated with a significant decline in funding performance for Kickstarter projects.
Observational analysis of 41,073 Kickstarter projects using LLM-assisted text classification to identify AI-use disclosure and entropy balancing to adjust for covariate differences; statistical tests reported as significant in the paper.
Developing a process twin is costly because it requires accurately modelling the entire production process (process steps, equipment and product-specific settings, process variations) and binding the model to live operational data.
Authoritative/technical description of development requirements and costs in paper (methodological claim), not quantified in abstract.
These findings highlight the risk of bias propagation in AI-assisted writing, calling for fairness-aware design in educational AI tools.
Authors' conclusion and recommendation based on the experimental results described above (N = 123 study showing bias transfer).
In a two-type heterogeneous-agent economy, high-cognitive-capital agents adopt AI more intensively and may eventually erode their unaided cognitive capital below that of initially lower-skilled agents.
Heterogeneous-agent extension of the analytical model; stated as a derived proposition. No empirical validation.
The decentralised equilibrium over-adopts substitutive AI relative to the social optimum because of systemic risk, cognitive public goods, and arms-race externalities.
Equilibrium analysis and welfare comparison in the formal model (decentralised vs socially optimal allocation). No empirical sample.
Expected crisis losses are convex in aggregate leverage.
Analytical result/proposition derived within the model showing convex relationship between expected losses and aggregate leverage. No empirical sample.
Temporary accommodation has become a major fiscal and administrative pressure for English local authorities, particularly in London, where demand and costs have risen sharply.
Statement in paper introduction/background; contextual claim based on administrative observations and cited motivation for building DOMUS (no specific sample size or numerical data reported in the provided text).
New accusations function as social gatekeeping of perceived authenticity without actually screening for AI.
Synthesis of findings: large-scale vocabulary trends, speech-act coding, and matched-control results indicate accusations are used for social signaling/gatekeeping rather than accurate AI detection.
Many existing approaches (interaction metrics, behavioral coding schemes, activity traces) often struggle to capture higher-order interaction dynamics, including how collaborative processes reorganize, stabilize, regulate, and evolve through time.
Theoretical critique and literature-level argument presented in the paper (conceptual analysis); no empirical sample or quantitative evaluation reported.
Directly evaluating agents on physical high-precision instruments is impractical due to high cost, safety risks, limited accessibility, and difficulty in ensuring reproducible evaluation.
Argument presented by authors as motivation for creating a simulated testbed; no empirical cost/safety/accessibility metrics provided in the excerpt.
Secure attachment further moderated the indirect effect of organizational AI adoption on employees' turnover intentions via identity threat (i.e., it attenuated the mediated effect).
Moderated mediation (conditional indirect effect) analysis reported on three-wave survey data of 312 employees; secure attachment reported to weaken the indirect AI adoption → identity threat → turnover intentions pathway.
Secure attachment negatively moderated the relationship between organizational AI adoption and identity threat (i.e., higher secure attachment reduced the AI adoption → identity threat effect).
Moderation analysis (interaction effect) reported in the three-wave survey data (N=312); secure attachment reported to negatively moderate the AI adoption to identity threat path.
Signal-planting and directed-versus-random experiments show that novelty alone is insufficient: random orthogonal jumps expand coverage but do not improve yield without predictive alignment.
Empirical claim supported by experimental comparisons (signal-planting experiments and directed vs. random proposal experiments); specific sample sizes and quantitative results not provided in the abstract.
Scientific discovery saturates when new hypotheses cease to provide independent information, even if the nominal hypothesis space remains large.
Conceptual/theoretical claim stated in the paper's framing; argued via the notion of informational independence of hypotheses (no specific sample size reported).
Aligning the dimensions with the regulatory frameworks above identifies overlapping gaps neither side currently closes.
Result claimed in abstract that mapping taxonomy dimensions to listed regulatory frameworks reveals overlapping unattended gaps; no detailed counts or specific gaps listed in the abstract.
Several open-source organisations have responded with contribution policies, but the result is fragmented, and its alignment with emerging AI governance frameworks (EU AI Act, NIST AI RMF with the UC Berkeley Agentic AI Profile, ISO/IEC 42001 and 23894) is unmapped at the contribution level.
Paper states that multiple organisations adopted policies and that alignment with listed regulatory frameworks is unmapped; the paper reports a comparative study across six organisations (names given).
Autonomous and semi-autonomous AI contributors strain those assumptions
Stated in abstract as an observed tension between agent capabilities and human-focused contribution norms; no quantified incidents or metrics provided in the abstract (but paper indicates mapping of documented agent incidents).
These mechanisms produce ethical harms such as accountability deficits, epistemic injustice, labour precarity, and constrained sovereignty.
Reported synthesis finding drawing on the reviewed literature (50 articles) linking the named mechanisms to listed ethical harms.
These dynamics operate through four mechanisms: epistemic templating, governance transfer, infrastructural lock-in, and labour opacity.
The paper reports these four mechanisms as the pathways identified via the critical synthesis of the 50 articles.
The analysis identifies four interrelated dynamics—algorithmic colonialism, data colonialism, platform imperialism, and platform sub-imperialism—through which dependency and domination are reproduced across global and intra-South contexts.
Synthesis of the 50 reviewed peer-reviewed articles; these dynamics are reported as the paper's analytical findings.
AI adoption may reproduce entrenched inequalities in postcolonial contexts.
Critical synthesis (literature review) of 50 peer-reviewed articles from 2019–2025 reported by the paper.
Those valuable signals are entangled with framework churn, naming drift, generated-source ambiguity, dependency rituals, CI dialects, weak proof routes, and human-oriented review customs.
Qualitative claim/analysis in the paper describing entanglement of signal and accidental complexity; no empirical quantification provided.
Frontier coding models may spend substantial capacity learning not only program behavior, but also accidental entropy in human repositories.
Conceptual/theoretical assertion presented in the paper (no empirical sample or experiment reported for this claim).
The review highlights critical challenges related to privacy, emotional surveillance, algorithmic bias, and employee trust associated with emotional AI in the workplace.
Aggregated observation from the systematic review; these concerns are reported as recurring themes across the surveyed literature (specific counts/examples not given in the abstract).
The study identifies specific retention issues including rigid work practices, a predominantly masculine culture, and occurrences of bullying and harassment.
Findings from thematic analysis of 23 interviews using NVivo 13; participants' accounts raised these specific themes as retention-related issues.
Women in UK construction continue to face major retention challenges driven by structural biases that lead to feelings of disrespect, insufficient support, and being undervalued.
Thematic analysis of 23 qualitative interviews with women involved in digitally enabled projects; participants reported experiences and perceptions related to retention and workplace culture.
Women make up less than 15% of the UK construction workforce.
Statement in the paper likely citing national labour/industry statistics or prior literature (not primary data from this study).
Interactive effects and dynamic vicious cycles exist among the three mechanisms: temporal loss of control amplifies the physiological effects of temporal predation, while temporal acceleration intensifies the psychological effects of temporal loss of control.
Theoretical interaction hypotheses articulated in the framework based on cross-model synthesis and literature discussion; no empirical interaction tests presented in the abstract.
Temporal loss of control is expected to contribute to depression and to heighten occupational injury risk, with learned helplessness and the depletion of cognitive resources as key mediating processes.
Theoretical claim derived from integrating Karasek’s demand-control model and job demands-resources literature; proposed mediators and outcomes come from conceptual argument and cited studies rather than new empirical tests.
Temporal acceleration and discipline are theorized to undermine mental health, giving rise to anxiety and burnout via time panic and emotional exhaustion.
Framework/theoretical argument grounded in integration of Rosa’s social acceleration and psychological job-stress models; claim supported by referenced literature but no new empirical data reported in the abstract.
Temporal predation primarily damages physiological health—manifesting as cardiovascular strain and musculoskeletal injuries—through the mediating pathway of chronic fatigue.
Theoretical proposition based on literature synthesis and mediation logic presented in the framework; no primary empirical data or sample size reported in the article text provided.
Algorithmic time politics damages occupational health through three interconnected mechanisms—temporal predation, temporal acceleration and discipline, and temporal loss of control—which form a progressive chain from 'the quantity of time' through 'the quality of time' to 'the sovereignty over time.'
Theoretical multilevel framework developed by the article combining disciplinary theory, social acceleration theory, job demand-control and job demands-resources models and literature review; no empirical testing reported.
In platform labor, algorithms reshape workers’ perception and control of time through mechanisms such as dynamic pricing, compulsory task assignment, time-limit compression, and real-time surveillance, giving rise to a novel power formation—“algorithmic time politics.”
Conceptual/theoretical claim constructed by the article via literature integration and argumentation (synthesis of Foucault, Rosa, Karasek, Bakker & Demerouti); no empirical sample or quantitative study reported.
API-based approaches struggle with heterogeneous protocols and inaccessible commercial interfaces.
Author assertion contrasting API-based approaches with GUI and COM approaches (conceptual/architectural argument rather than specific experiment).
GUI-based agents suffer from fragile visual grounding and long-horizon error accumulation.
Author assertion in paper introduction describing limitations of GUI-based agents (conceptual analysis / literature-grounding rather than new experimental data).
Research on embedded finance emphasizes modularity but offers limited insight into how systems evolve when AI-driven inference and platform environments jointly structure financial action.
Literature-based critique in the paper's theoretical review of embedded finance research; no empirical sampling reported.
An unconstrained multi-agent baseline produced critical failures in 72% of runs.
Reported experimental result from the 2x4 factorial experiment (failure rate for the unconstrained multi-agent baseline reported as 72%).
The bottleneck is often not model capability but missing project memory.
Assertion made in the abstract without accompanying quantitative evidence in the abstract.
Reconstructing this context can consume an estimated 5,000-20,000 tokens per session.
Statement in paper abstract presenting an estimate (no detailed method or sample described in the abstract).
With the instruction files, 26.35% of the projects decreased their merge rate.
Reported proportion of projects showing a decrease in merge rate after creating instruction files based on the pre/post comparison of projects in the dataset (148 projects, 15,549 PRs).
Xie et al. (2026) show experimentally that job candidates are less satisfied with firms using AI evaluators than with human experts due to perceived loss of control; the negative effect is stronger for individuals with an internal locus of control.
Experimental study on recruitment using control theory as described (sample size not provided).
In the healthcare sector, Chou et al. (2026a, 2026b) identify AI anxiety as a multifaceted hurdle to adoption; emotional affect and outcome expectations are essential influences on usage intentions (two-stage SEM-ANN approach).
Two-stage SEM–ANN modeling grounded in social cognitive theory as reported; empirical data specifics not provided in text.
Liu et al. (2026a, 2026b) find experimentally that the severity of AI service failure in hotel contactless services significantly decreases customers' forgiveness willingness, but high levels of brand attachment mitigate this negative effect.
Experimental studies in hotel contactless service contexts (details and sample sizes not provided in the text).
Allowing AI to take the lead in strategic decision-making without human wisdom may be inappropriate due to AI's inability to navigate tacit knowledge and ethical nuances in Chinese management wisdom.
Argumentative claim based on cited literature (e.g., De Cremer and Kasparov, 2021; Del Giudice et al., 2023) and authors' synthesis.
Developers reject fixes for (a) incorrect implementation (e.g., incomplete, wrong approach), (b) fixes that do not pass CI pipelines and fail tests, (c) fixes for which the agent is unable to perform the implementation (e.g., no code generated, sessions lost), and (d) fixes whose priority is low.
Observed categories from the qualitative analysis of the 306 non-merged PRs described in the study.
The qualitative findings identify 14 reasons divided into four high-level categories for rejecting AI-agent fixes.
Result of the paper's qualitative analysis on the representative sample (306 non-merged PRs).
From a first exploration of the AIDev dataset, 46.41% of the fixes proposed by the agents Copilot, Devin, Cursor, and Claude are rejected.
Empirical analysis of the AIDev dataset reported by the authors; agents named explicitly (Copilot, Devin, Cursor, Claude).