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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Human-AI interaction factors influence people’s reliance on AI advice.
Synthesis claim from the analytical review indicating that interaction design and other human-AI interaction variables affect reliance (specific factors not enumerated in the abstract).
Although AI working autonomously achieved a 37% reproduction rate, it could be useful for automated screening when human review is cost-prohibitive.
Interpretation in paper: authors note 37% autonomous reproduction rate as potentially useful for large-scale screening where human review is infeasible; based on empirical results of the experiment.
The task-based adaptive collaboration model hypothesizes that trust, explainability, and task difficulty moderate the effect of human–AI collaboration on performance.
Statement of hypothesized relationships within the model developed in the paper (theoretical hypotheses rather than reported experimental estimates).
Essay quality changes little while students have AI access but improves in style and relevance one week later when students write unaided.
Open-ended essay assessments (higher-order skills) collected immediately (with AI access for treatment group) and one week later (unaided) in the randomized experiment; quality measured on dimensions including style and relevance.
Employment effects follow the same timing (i.e., emerge in 2021) but diverge by exposure type.
Paper reports employment effects with temporal alignment to output effects (emerging in 2021) and heterogeneity by type of AI exposure.
Approach motivation (BAS Drive) moderates whether interactive partnership benefits originality.
Moderation analysis reported from the pilot (N = 62) showing interaction between BAS Drive (a measured personality/motivation scale) and the effect of interactive partnership on originality.
Traditional jobs based on manual work are transforming into collaborative management and exception-handling roles that demand new cognitive and ethical skills from employees.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). No specific sample size reported.
AI performs best in routine, data-rich situations but falls short when decisions require lived experience and contextual understanding.
Synthesis of cross-domain empirical studies and theoretical arguments showing differential AI performance by task type (routine/data-rich vs. experience-dependent/contextual).
A distinctive feature of the taxonomy is a dedicated category for self-evaluation: every improvement loop is a claim that some signal can substitute for human judgment.
Authors' taxonomy and conceptual argument emphasizing self-evaluation as a separate category across surveyed works.
The taxonomy separates bounded self-refinement -- convergent, evaluable, and already industrial practice -- from open-ended recursive self-improvement (RSI).
Conceptual taxonomy constructed by the authors based on their survey of the literature; classification of surveyed works into categories.
The literature's vocabulary ("self-refine," "self-reward," "self-play," "self-evolve") conflates fundamentally different ambitions.
Qualitative analysis of terminology across the surveyed arXiv papers (2024-2026) reported in the paper's survey and taxonomy section.
The organizing claim of the theory is that review is the control point through which a coding agent's effect on software is decided, and that AI does not fix the sign of that effect: the team sets it, through the expertise its humans bring and how it structures the review process.
Synthesis of practitioner discourse coded into a causal model derived from the LLM-assisted analysis of 3,100 sampled documents; presented as the central theoretical claim.
Practitioners sharply disagree about how coding agents change code review: whether review becomes the bottleneck, whether human review remains necessary, and whether agents erode the understanding that review once built.
Synthesis of practitioner discourse at scale via collected grey-literature (engineering blogs and Reddit threads) and a coded sample; claim summarizes observed disagreement in practitioner sources.
The direction of these observed trends (review frequency, merge speed, discussion) flips under different but equally defensible analysis choices.
Authors' sensitivity/robustness checks on the observational GitHub analysis indicating that trend direction depends on analysis choices; reported in abstract without numeric detail.
We propose 'contextuality' — the degree to which an AI system autonomously accesses a user's accumulated knowledge capital — as a dimension of AI-mediated inequality that complements, but is not reducible to, the Sharp et al. framework.
Conceptual proposal and definitional contribution in the paper presenting contextuality as a new analytic dimension.
The paper analyzes the technical basis of the Context Access Divide in Model Context Protocol (MCP) and retrieval-augmented generation (RAG) architectures.
Technical analysis and architectural examination reported in the paper (discussion of MCP and RAG as implementation-relevant architectures).
The CAD is formalized with a probabilistic model grounded in the fan effect literature in cognitive psychology.
Paper reports a formal probabilistic model drawing on the fan effect literature; model described as the formalization of CAD.
Sharp et al. (2025) introduce "agentic inequality" as a framework for analyzing disparities in access to AI agents across three dimensions: availability, quality, and quantity.
Statement and citation in the paper (reference to Sharp et al. 2025); descriptive synthesis of prior work.
Coding agents are capable; human oversight is the bottleneck.
Authors' high-level claim/argument in the paper, supported conceptually and motivated by the reported experiment showing reviewer limits.
Macroeconomic evidence remains cautious because AI diffusion is still uneven across industries and many firms are in early adoption stages.
Paper's synthesis of macroeconomic and industry-level sources (OECD, IMF, BLS, McKinsey, etc.) reporting uneven diffusion and early-stage adoption.
The productivity effect of AI is not automatic; it depends on firm-level adoption, worker skills, complementary investment in software and data systems, managerial readiness, task suitability, and the ability of organisations to redesign workflows around AI.
Paper's conceptual argument and synthesis of secondary literature highlighting conditional factors for realizing productivity gains.
Agentic AI differs from human organisations because these patterns are not sustained by motivation, identity, trust, employment, socialisation, or moral accountability; they are sustained by context architecture: prompts, memory, traces, schemas, tools, validators, and permissions.
Theoretical argument in the paper contrasting sustaining mechanisms for organisational behaviour; based on conceptual analysis and description of system-level affordances (no sample size reported).
Across four high-stakes domains, assigning different personas is sufficient for AI agents to report divergent, often opposing, conclusions from the same data and question, with findings systematically aligned with those beliefs.
Experimental manipulation across four domains where AI agents were assigned different personas and produced analyses from the same data/question; comparison of resulting conclusions showing divergence and alignment with persona beliefs.
The net effect of AI on work is better described as displacement than wholesale elimination.
Author's conceptual argument and synthesis of literature/reports (qualitative argumentation in the paper).
Hybrid (human-AI) performance, analyzed at the individual forecaster level, is trimodal: most people either deferred to the model (matching it) or rubber-stamped a prior guess (performing worse than the model alone), while a minority engaged in genuine complementary reasoning and reached accuracy matching or even exceeding the market.
Pilot empirical analysis comparing individual forecasters' hybrid forecasts to both the model and the Polymarket benchmark; claims reported at individual level in the paper.
These findings do not necessarily generalize to more sophisticated schemes that simulate human conversation.
Cautionary/qualitative statement in the abstract noting limitation of the experimental manipulation (symbolic awards) and that more sophisticated conversational agents might have different effects; not an empirical finding from this study.
In established open-source projects, adopting an AI coding agent makes code modestly more complex but does not crowd out the human newcomers that a project depends on.
Synthesis of the paper's DiD results: no significant decline in newcomer inflow, unchanged onboarding/retention, correlational beginner-task measure unchanged, and measured modest increases in complexity metrics.
The cooperative effects of the prosocial AI interventions were short-lived, fading after the first few rounds.
Temporal analysis of contributions over rounds in the iterated game showing decay of the prosocial AI effect after the initial rounds (reported in the experiment with N = 1,283).
In coding tasks, low agreeableness leads to large communication shifts that have little effect on milestone completion.
Experimental manipulation of agreeableness in LLMs on structured coding tasks; observed large changes in communication but little change in milestone completion rates. No quantitative effect sizes or sample counts given in the abstract.
Personality effects depend critically on task structure.
Authors compared the impact of personality manipulation across three distinct task domains (structured coding, open-ended research collaboration, competitive bargaining) and report differing outcomes by domain. Abstract does not provide numeric sample sizes or statistical details.
Prior work shows that agents prompted with low agreeableness produce adversarial language, while those prompted with high agreeableness become cooperative.
Citation to prior literature (not specified in the abstract) reporting correlations/causal effects of agreeableness prompts on generated language (adversarial vs cooperative). No sample size or study details provided in the abstract.
Key human factors—trust calibration, output-quality sensemaking, expertise depth, feedback latency, cognitive load, and metacognitive skill development—serve as performance-shaping mechanisms within AI-enabled systems.
Presentation of a socio-technical evaluation model synthesizing prior research across several disciplines (conceptual synthesis; no empirical sample reported).
From a sociomaterial perspective, auditor reconfiguration depends both on the evolution of technological capabilities (material agency) and on professionals' engagement and adaptation (social agency).
Theoretical framing and interpretive synthesis in the SLR of 43 studies; application of sociomateriality theory to the empirical patterns identified in the literature.
The introduction of AI reconfigures the auditor’s role through an ongoing, dynamic process: as technology evolves, organizational practices and arrangements transform, rebalancing functions and responsibilities between auditors and tools.
Interpretive synthesis from the SLR of 43 studies using a sociomateriality theoretical lens; cross-study observations about changing tasks, responsibilities and human–machine interactions.
The paper develops a task-to-firm conversion framework explaining why task-level GenAI productivity gains do not automatically translate into firm-level improvements.
Theoretical and conceptual contribution presented in the review, integrating multiple literatures (GPT theory, digital economics, task experiments, China studies).
Despite task-level gains, GenAI produces uneven or limited firm-level productivity effects in many settings.
Review synthesizing discrepancies between task-level experiments and firm-level outcome studies, and discussion of conversion frictions in the paper.
Generative AI (GenAI) should not be treated as a standalone productivity shock; its economic value depends on the interaction between model capability, task fit, human-AI calibration, organizational complementary assets, and regional digital infrastructure.
Conceptual framework developed in this review synthesizing literature from AI research, task-level productivity experiments, general-purpose technology theory, digital economics, and China-focused digital transformation studies; no new firm-level empirical analysis in this paper.
Existing user-role frameworks (e.g., the BTP User Type Matrix) require adaptation because the workforce is undergoing significant role-specific changes.
Authors' analysis based on 20 expert interviews and a 24-person workshop that uncovered mismatches between current role taxonomies and emergent AI-influenced responsibilities.
There is a growing reliance on agentic AI systems within the platform context.
Qualitative evidence from the 20 interviews and the 24-participant workshop reporting increased dependence on AI agents for tasks and decision support.
There is increasing automation of operational tasks in the development domain.
Participant reports and workshop discussions from 20 interviews and a 24-person workshop indicating automation of operational activities; qualitative thematic evidence.
The results reveal substantial shifts in day-to-day tasks and roles in the development domain.
Reported findings from 20 expert interviews and a 24-participant participatory workshop; claim based on participants' reported changes to responsibilities and observed themes in the data.
AI is rapidly reshaping the nature of work in software development, transforming user roles, workflows, and collaboration patterns across enterprise platforms.
Qualitative study reported in the paper combining 20 expert interviews and a participatory workshop with 24 participants; findings derive from thematic analysis of participant accounts and workshop outputs.
The intended contribution is an Information Systems framework explaining when AI supports human augmentation and when it produces functional substitution.
Stated intended theoretical contribution in the abstract (proposed framework). This is an intended outcome rather than an empirically demonstrated result in the provided text.
The study investigates both perceived and enacted managerial agency.
Stated measurement targets in the abstract (descriptive of dependent variables). No measurement instruments or sample reported in the provided text.
The research uses a sequential multi-phase design combining experiments and qualitative fieldwork.
Stated methodology in the abstract (methodological claim about study design). No sample sizes or procedural details provided in the excerpt.
The study focuses on how technological design features, including transparency and override flexibility, interact with governance structures such as accountability and incentive systems.
Stated focus of the study in the abstract (descriptive of independent variables and governance moderators). No empirical details or sample reported in the provided text.
This doctoral research examines how AI-enabled decision systems affect human agency in data-driven organizations.
Stated research scope and aim in the paper (descriptive claim about the study's focus). No sample or results provided in the abstract.
Artificial intelligence is increasingly embedded in organizational decision-making, reshaping how managers exercise discretion and responsibility.
Stated as a background/motivation statement in the paper (literature-driven claim in the abstract). No empirical evidence or sample reported in the provided text.
The simulation offers a template of how firms ought to reorganize internal promotion ladders when junior positions are significantly automated.
Model-based policy/reorganization recommendation derived from the simulation results; presented as guidance for firm-level reorganization rather than an empirically tested organizational intervention in the abstract.
We simulate the elasticity of substitution between human intuition and the output of an algorithm.
Paper reports a simulation exercise modeling the elasticity of substitution between human inputs (intuition) and algorithmic outputs; no simulation parameters or sample size provided in the abstract.