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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We illustrate this transition through examples in consumer markets, education, news, and coding.
Authors state they use sectoral examples to illustrate the framework; this is a claim about the paper's contents rather than an empirical finding.
We offer a three-stage lens: Augmentation, Automation, and Reconstruction.
Conceptual framework proposed by the authors; presented as a taxonomy in the paper (no empirical validation reported in the excerpt).
There is a suggestive non-linear relationship between embodiment and team performance.
Analysis reported in the paper indicating a non-linear (not strictly monotonic) association between degree of agent embodiment (Box, Avatar, humanoid) and measured team performance; described as 'suggestive' in the abstract, without quantified functional form or statistics included there.
Artificial agents have an uneven impact on team outcomes, with some mixed human–AI teams performing exceptionally well and others markedly worse.
Observed performance outcomes across mixed human–AI teams in the escape room experiment, showing high between-team variability; exact sample size and statistical details not provided in the abstract.
Acquiescent silence (resignation-based) is motivationally distinct from defensive (fear-driven) silence.
Theoretical distinction advanced using organisational silence literature (conceptual claim referencing existing theory).
These findings demonstrate the feasibility and current limits of automated expertise mapping.
Synthesis/conclusion based on model performance (e.g., MAE results) and observed limitations reported across evaluations.
Reward-level intervention (via equity-aware LLM refinement) significantly improves equity, but demographic disparities in AI-driven controllers persist.
Overall conclusion drawn from reported experimental results (improvements in group satisfaction metrics but acknowledgment that disparities remain).
The UPCT framework offers a unified explanation for varied phenomena: pandemic resilience patterns, divergent digital transformation outcomes, and emerging risks of AI-driven organizational rigidity.
Synthesis claim by the author asserting explanatory scope of the theoretical framework; no empirical cross-case synthesis or formal validation included.
The paper's Universal Phase Crystallization Theory (UPCT) reconceptualizes organizations as recursive generative cycles (Φ→R→S→Φ′) and asserts organizational existence is better described as E = ΦR rather than E = S.
Theoretical/model claim introduced and developed in the paper; purely conceptual without empirical testing.
Resilience should be redefined not as reserve magnitude (accumulated buffers) but as recoverability of generative relational capacity.
Normative/theoretical redefinition proposed by the paper; no empirical validation provided.
AI is changing skill requirements—some skills become obsolete and new skills are required.
Paper identifies changing skill requirements as a key area of examination (abstract). This is stated as an asserted trend based on the paper's review rather than a quantified empirical finding in the provided text.
AI has changed how work is executed (work processes and execution).
Explicit statement in the paper's abstract; presented as a qualitative/general finding from the paper's evaluation and literature synthesis (no numerical sample provided).
AI has changed who works in jobs (i.e., workforce composition).
Stated in the paper's abstract as an asserted effect of AI on employment composition; presented as part of the paper's review rather than a specific empirical estimate.
The penetrating utilization of AI-based methods to perform tasks has drastically changed how jobs are performed.
Claim asserted in the paper (abstract) as a descriptive conclusion from the paper's review/analysis; no empirical sample or quantified effect reported in the provided text.
AI is altering nearly every aspect of human interaction—such as work and society.
Statement in the paper's abstract/intro; presented as a general observation in the paper (literature review/qualitative synthesis implied). No primary sample size or empirical estimate reported in the provided text.
Comparative analysis of Japanese, European, and United States legal frameworks shows differing treatments of translation data and points toward the need for redistributive design to remedy unequal attribution and capture.
Comparative legal analysis across jurisdictions (Japan, EU, US) and normative argument proposing redistributive design directions; no experimental or quantitative evaluation provided.
Emotion is a strategic action channel rather than a surface style.
Interpretation based on experimental results (GoEmotions prompting and subsequent analyses) demonstrating that adding emotional framing changes negotiation outcomes in systematic ways.
cBCI synergy is heavily contingent on the temporal dynamics of trust, providing a critical framework for designing dynamically gated Human-AI systems.
Interpretive/concluding claim based on experimental results (timing-dependent failure modes, Oracle gating, Hybrid Fusion effects) reported in the study.
AI timing dictates the mechanism of team failure: high-speed AI interventions risk inducing reflexive blind compliance while delayed interventions can induce ambiguous cognitive conflict.
Synthesis claim derived from experimental contrasts between Fast/Less-Accurate and Slow/Accurate AI conditions and observed human/team behaviors (blind compliance vs. delayed conflict).
AI's future impact on employment will depend not only on automation capabilities but also on how responsibly enterprises manage workforce transitions.
Paper's concluding claim synthesizing arguments and proposed governance approach (normative conclusion rather than an empirically tested causal estimate in the excerpt).
AI-induced workforce disruption is not only a labor market issue but also an enterprise governance challenge.
Argument/position advanced in the paper highlighting governance responsibilities for firms implementing AI.
Artificial intelligence, especially generative AI, is transforming enterprise operations by automating tasks, enhancing decision-making, and redefining job roles.
Conceptual statement in the paper describing observed/expected effects of generative AI on enterprise operations (no specific empirical sample or experiment reported in the excerpt).
Depending on operational parameters, the most time-efficient way to complete a workflow may undergo a transition between two task-processing regimes: a fully AI-assisted regime and a fully manual regime.
Analytical results derived from the paper's formal queueing model (theoretical/model-based derivation; no empirical sample reported).
AI assistance can generate a deceptive productivity signature: average completion times fall because AI tools typically supply a fast first draft, yet workflow-level performance can deteriorate when a subset of AI errors escapes review and returns as costly downstream rework.
Analytical derivation and discussion based on the paper's queueing model (theoretical/model-based evidence; no empirical sample provided).
Drawing on the partial equilibrium model of Gries and Naudé (2022), existing economic frameworks may inadvertently overlook these factors.
The paper's theoretical critique referencing Gries & Naudé (2022); argument is based on model comparison and conceptual analysis rather than new empirical tests.
We identify five key moderating factors: human resource composition, baseline capability of individuals, learning curve of practitioners, incentives for fair use, and flexibility of objectives.
Explicit enumeration of proposed moderating factors in the paper (conceptual identification rather than empirical measurement).
Following the advent of high-performance generative models, AI use has been rapidly encouraged in some sectors while being restricted in others.
Descriptive claim in the paper's introduction/abstract; based on observation and literature context rather than new empirical data.
These findings have broader implications for productivity, equity, and capacity across the global research system.
Discussion/interpretation in paper based on causal results from randomized experiment; inference from observed behavioral changes and heterogeneous effects.
AI redefines job roles.
Authors' thematic analysis of secondary sources and peer-reviewed literature (qualitative synthesis). No sample size reported.
Artificial Intelligence (AI) has changed how people work across various fields and businesses, especially in the Indian Information Technology (IT) industry.
Authors' qualitative synthesis of peer-reviewed literature and thematic evaluation of secondary data (literature review). No sample size reported.
Completion time itself is not sufficient to characterize efficiency gains.
Authors' inferential conclusion in the abstract based on observed dissociation between completion time (no difference) and subjective effort (lower with AI) in their preregistered study (N = 1237).
Including narrative explanations with AI predictions may involve tradeoffs for decision-making performance.
Synthesis and conclusion based on the experiment's findings (null effect on accuracy, increased reliance, and exploratory detrimental effects on response time and discrimination).
Recent Chinese regulatory initiatives addressing anthropomorphic and emotionally interactive AI services illustrate emerging governmental responses to the social and psychological risks associated with relational AI.
Cited as an illustrative example in the recommendations; the text references Chinese initiatives but does not provide specific citations, legal texts, or empirical evaluation within the document.
Regulatory approaches to advanced AI systems are evolving differently across major jurisdictions.
General observation in the recommendations; no cross-jurisdictional comparative analysis or dataset provided in the text.
Widely used conversational systems increasingly function as interfaces through which users access information, digital services, and online markets.
Descriptive claim presented in the recommendations; no quantitative metrics (e.g., usage statistics, market share) or empirical study cited in the text.
Conversational AI evolves into systems capable of shaping users’ emotions, behaviour, and social engagement.
Stated as a descriptive premise in the policy recommendations; no empirical study, sample size, or quantitative data provided in the text.
AutoResearch autonomy is domain-conditioned: more credible in structured, executable, and rapidly verifiable settings but limited in embodied, delayed, heterogeneous, ethical, or institutionally accountable contexts.
Authors' synthesis of system capabilities and application domains from the surveyed literature; qualitative assessment of where autonomy is plausible vs limited.
Emerging AI-led systems coordinate larger portions of the discovery loop without achieving robust autonomy.
Survey of recently proposed AI scientist and AI-led systems showing increased coordination across workflow steps but lacking evidence of fully autonomous, robust operation; qualitative synthesis.
Algorithmic authority may both strengthen and undermine legitimacy of decisions in AI-enabled organizations.
Theoretical analysis in the paper presenting dual possibilities for algorithmic authority's impact on legitimacy, supported by conceptual reasoning and literature (no empirical test reported).
Models with near-identical overall strength show qualitatively different capability profiles.
Observed differences in capability-profile axes for models with similar aggregate scores in the tournament.
AI is changing informal cultural practices like professional mentoring that are key to helping professionals settle in their positions, stay engaged with their work, and grow their careers.
Participant reports from the 24 interviews indicating changes to informal practices such as mentoring, onboarding, and informal feedback.
AI is changing formal role responsibilities and collaborations between those roles.
Qualitative interview data from 24 product-focused employees describing shifts in formal responsibilities and inter-role collaboration.
AI adoption is allowing professionals to blur and extend the boundaries of their corporate roles.
Reported by interview participants (qualitative evidence) from the 24 interviews at one large technology firm.
AI opacity, automation intensity, anthropomorphic and affective design features, and the degree of human-centered system design are determinant factors shaping users' psychological responses to human–AI collaboration.
Authors' synthesis from reviewed empirical and theoretical studies highlighting design and system characteristics associated with psychological outcomes.
The interdisciplinary literature identifies technostress, automation fatigue, cognitive overload, algorithmic anxiety, overtrust, and responsibility ambiguity as key phenomena arising from integration of AI systems and AI-enabled robots into collaborative human work environments.
Synthesis of interdisciplinary peer-reviewed studies (systematic review); topics extracted from reviewed papers as reported by the authors.
Artificial Intelligence (AI) has caused massive changes in nature of workplaces in healthcare sector.
Asserted in paper's introduction and supported by a scoping review (PRISMA-ScR) of 29 peer-reviewed empirical studies published 2020–2025.
Scaling helps but does not solve the accumulated-message effect (Anthropic models: Haiku -0.22 to Opus -0.17; OpenAI models: Nano -0.34 to GPT-5.2 -0.17).
Comparison of effect magnitudes (Cohen's d values) across model families and sizes reported in the experiments.
The accumulated-message effect concentrates on items where the model is genuinely uncertain at baseline (d = -0.34 for high-entropy items, vs d = -0.15 when the baseline is deterministic).
Subset analysis partitioning items by baseline model entropy/uncertainty; reported Cohen's d for high-entropy vs deterministic-baseline items (no separate sample counts reported in the abstract).
Models shift toward the conversation's prevailing polarity (accumulated message effect on LLM judgments, AMEL).
Experimental comparison where identical test items were presented either in isolation or following histories saturated with predominantly positive or negative evaluations, across the full dataset (75,898 API calls to 11 models). Reported effect: d = -0.17, p < 10^-46.
Much of the earlier provider spread came from end-to-end system behavior rather than planning alone.
Inference from the contrast between the cross-provider championship (end-to-end) where provider differences were observed and the planner bakeoff (standardized execution) where planners were near-equal.