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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When a human colleague doubts a twin agent's output, they face three failure modes (a schema gap, an epistemic gap, and a model artifact) with no reliable attribution path between them.
Conceptual taxonomy derived from the authors' early design observations; presented as an identified set of failure modes in the paper (qualitative, no numeric sample reported in abstract).
Drawing on early design work in an ongoing project, we identify a trust calibration problem specific to this approach.
Based on the authors' early design work (qualitative/design research) described in the paper; no sample size or quantitative metrics reported in the abstract.
Major open challenges for responsible adoption include reliability, bias, privacy, automation bias, transparency, and evaluation.
Authors' identification of risks and open research challenges based on their review/analysis (conceptual synthesis).
Current AI support for code review remains fragmented, with tools focusing on isolated tasks such as reviewer recommendation, PR description generation, or comment suggestion rather than the end-to-end PR review workflow.
Authors' survey/overview of existing AI tooling for code review described in the paper (conceptual / review-based evidence). No quantitative counts provided in the abstract.
AI coding assistants expand the volume of code requiring review, turning code review into a growing bottleneck.
Authors' analytical claim linking increased code production from AI assistants to increased review workload; presented as an observed/trend claim in the paper rather than supported by a quantified study in the abstract.
Code review has evolved for decades, from informal peer checking to today's pull request (PR) workflows, yet it remains a largely manual, uneven, and cognitively demanding process.
Authors' literature review and historical synthesis of code review practices presented in the paper (conceptual / review-based evidence). No empirical sample or experiment reported in the abstract.
Challenges including algorithmic bias, data privacy concerns, high costs, and skill gaps persist across contexts.
Cross-study synthesis of barriers and challenges reported in the 21 included studies spanning multiple contexts.
SMEs face unique resource constraints yet lag in AI-HRM adoption.
Synthesis conclusion from the systematic review of 21 included studies (published 2019–2026) comparing adoption patterns and barriers for SMEs.
Real-world trajectory data can provide highly accurate insights but collecting it is costly and often infeasible for many retailers.
Author claim about practical constraints of data collection for retailers; argued contextually in the paper rather than presented as a quantified empirical finding in the excerpt.
Actual customer trajectories deviate by an average of 28% from shortest paths.
Empirical measurement reported in the paper comparing real-world trajectory data to shortest-path (TSP-like) routes; exact sample size not stated in the provided text.
Standard health system digital transformation policy, which typically addresses only the threshold failure through individual incentives, is predicted to systematically produce the partial adoption trap.
Model prediction contrasting full policy architecture vs. conventional policies that focus solely on individual incentives; analytical conclusion that such limited policies leave other failure modes unaddressed and therefore lead to stable partial adoption. Theoretical model; no empirical sample.
The barrier-lowering benefit of failed attempts is offset when trust erosion is rapid.
Model analysis combining cost-ratchet dynamics and trust erosion parameters; results showing interaction where fast trust erosion negates barrier reductions. Theoretical simulations/derivations; no empirical sample.
These failure modes are most severe precisely for the technologies with the greatest systemic value: the Value-Adoption Paradox.
Analytical result from the model showing failure-mode severity as a function of systemic value; theoretical identification of a paradox where higher systemic-value technologies face stronger coordination/trust/cultural barriers. Theoretical derivation; no empirical sample.
The basin of attraction of the partial adoption trap is enlarged by a cultural failure arising from negative coordination norms among doctors.
Model analysis including cultural coordination norms; theoretical demonstration that negative norms exacerbate partial adoption equilibria. Theoretical model; no empirical sample.
The basin of attraction of the partial adoption trap is enlarged by a trust failure arising from the organisation's inability to credibly commit to sharing productivity gains.
Model extension incorporating organisational commitment/transfer of gains; analytical results showing trust/commitment constraints increase stability of partial adoption. Theoretical model; no empirical sample.
The basin of attraction of the partial adoption trap is enlarged by a threshold coordination failure arising from the non-appropriable nature of systemic benefits.
Model analysis showing how non-appropriable systemic benefits (externalities) change payoff structure and enlarge the basin of attraction for partial adoption. Theoretical derivation; no empirical sample.
Distributing deliberation tools across a hierarchy degrades performance relative to hierarchy alone for all five model families, reaching up to 3.4× worse mean return while using 1.8–2.7× more tokens.
Empirical comparisons across the twelve configurations showing distributed deliberation vs. hierarchy-alone across five model families and six models; measured mean returns and token consumption over 3,475 episodes with token-level accounting.
Analysis indicates a significant negative relationship between perceived opportunities and challenges related to AI (i.e., higher perceived opportunities are associated with lower perceived challenges).
Correlation and regression analyses performed in SPSS on primary survey data showed a statistically significant negative association between measures of perceived opportunities and perceived challenges.
There exists employee resistance to change in response to AI adoption.
Survey-based measures of resistance included in the questionnaire and analyzed (descriptive/correlation/regression) using SPSS.
Employees identify ethical issues—particularly transparency and accountability of AI systems—as a notable challenge.
Survey items on ethical concerns analyzed with SPSS (descriptive and reliability analyses).
Employees have concerns regarding data privacy related to AI systems.
Primary survey data using a Likert-scale questionnaire; findings summarized with descriptive statistics and reliability analysis.
Employees report lack of AI-related skills (skill gaps) as a significant challenge to human–AI collaboration.
Survey responses from employees in AI-enabled organizations collected via a structured questionnaire and analyzed (descriptive/correlation).
Employees report fear of job displacement as a notable challenge associated with AI adoption.
Primary survey data (structured questionnaire) capturing perceived challenges; descriptive statistics reported.
Current alignment approaches are primarily reactive rather than proactive.
Author's critique/characterization of prevailing alignment practice (conceptual observation without quantitative support).
The prevailing paradigm of alignment parallels early psychology's focus on mental illness: necessary but incomplete.
Analogy/argument presented by the authors as a conceptual critique (no empirical test reported).
Existing alignment research is dominated by concerns about safety and preventing harm: safeguards, controllability, and compliance.
Author's literature-level observation / conceptual review in the paper (no systematic review or quantitative coding reported).
Step-wise verification (verifying each stage of the reasoning chain) increases computational overhead and infrastructure requirements when deployed at scale.
Paper's structural trade-off analysis and engineering argument; no measured compute-costs, benchmarks, or sample-size reporting included in the provided text.
Process-based supervision introduces challenges regarding the sustainability of human-in-the-loop feedback loops.
Socio-technical argumentation in the paper—concern raised about ongoing human verification burden; no longitudinal or empirical data on human labor sustainability provided.
Deploying PRMs at scale introduces unique challenges regarding system latency.
Engineering and infrastructure trade-off analysis described in the paper; no measured latency benchmarks or sample-size performance tests provided in the supplied text.
Traditional outcome-based reward models, which evaluate only the final correctness of a solution, often fail to identify logical fallacies or "hallucinations" occurring within intermediate steps.
Theoretical critique and conceptual argumentation presented in the paper; no empirical study or sample size reported.
Capital-intensive sectors face structural constraints on adaptability.
Observed sectoral differences in comparative analysis (e.g., inclusion of ExxonMobil among firms) indicating lower Flexibility Index scores or slower reallocation in capital-intensive firm(s).
Cross-sectoral empirical evidence linking budget flexibility, forecasting accuracy, and institutional oversight remains limited.
Statement of literature gap in paper motivating the study; no new quantitative estimate provided.
Traditional static budgeting models are increasingly inadequate in environments marked by volatility, technological disruption, and fiscal uncertainty.
Framing claim in paper introduction; no specific empirical estimate given. Based on comparative empirical design motivation.
The analysis also identifies risks linked to exclusion, symbolic compliance, and concentration of control over compliance processes.
Theoretical risk mapping produced by the integrative review and interpretive synthesis; no primary empirical evidence presented.
Uncertainty around compliance and excessive risk avoidance reduce the space for lawful business activity.
Interpretive synthesis of evidence and arguments across the reviewed literatures (sanctions compliance, institutional voids); no original empirical test.
Firms working under such conditions often experience limited access to finance and markets.
Claim derived from literature on firm constraints in weak institutional/sanctioned contexts as reviewed in the paper; no primary empirical data reported.
Post-conflict and sanctions-affected environments are strongly affected by sanctions pressure, weak rule enforcement, and high levels of corruption risk.
Synthesis of literature on sanctions, weak institutions, and corruption risk presented in the integrative review; no new empirical sample reported.
In algorithm-triggered emotional escalations, workers showed lower engagement: they sent fewer messages, contributed a smaller share of total chat rounds, and showed less proactivity in information seeking and solution provision.
Behavioral measures derived from chat logs in the randomized experiment comparing worker actions post-escalation across escalation types; reported differences in message counts, share of rounds, and proxies for proactivity.
Human intervention is less effective in algorithm-triggered emotional escalations (where customers express frustration or dissatisfaction).
Experimental subgroup analysis comparing intervention outcomes for algorithm-triggered emotional escalations versus technical escalations; emotional escalations showed worse post-intervention outcomes.
AI deployment substantially lowers ratings for AI-eligible chats.
Randomized field experiment measuring customer ratings for AI-eligible chats; treated condition (AI + human oversight) produced substantially lower ratings relative to control (humans only).
AI deployment reduces average chat duration.
Randomized field experiment on Alibaba's Taobao platform: workers in treatment supervised an agentic AI resolving AI-eligible chats while handling AI-ineligible chats; control workers resolved all chats without AI. Effect observed on average chat duration in experiment data.
Rather than restoring stability, this cycle intensifies anxiety, undermines mastery, and erodes professional confidence.
Theoretical claim about psychological outcomes from the conceptual reskilling loop; paper provides argumentation but no empirical measurements.
Based on Job Demands–Resources (JD-R) theory and Conservation of Resources (COR) theory, the paper conceptualizes an AI-induced reskilling loop in which ongoing technological change leads to skill erosion, continuous reskilling demands, cognitive and emotional depletion, and reinforced learning as a defensive response to perceived obsolescence.
Theoretical model/loop derived from applying JD-R and COR frameworks; no empirical test or sample reported in the paper.
The paper introduces the concept of 'reskilling fatigue' to explain the human consequences of persistent skill volatility among Established Knowledge Professionals (EKPs).
Conceptual/theoretical contribution presented by the authors; definition and argumentation rather than empirical validation.
Continuous reskilling is widely promoted as a solution to AI-driven disruption, but little attention has been paid to its cumulative psychological costs.
Argument from literature review/observation in the paper; no empirical measurement or sample reported in the paper.
These characteristics are properties of the tasks themselves rather than limitations of current AI models.
Conceptual argument in the paper asserting task-inherent properties drive resistance to automation; supported by theory and argumentation, not by empirical model-comparison experiments.
The resistance of Metis tasks to automation is not due to computational intractability but to institutional, social, and normative entanglements.
Theoretical argument differentiating computational from institutional/social/normative causes; supported by citations and cross-disciplinary theory rather than empirical causal identification.
There exists a class of entirely digital tasks, called 'Metis AI', that resist reliable AI automation.
Conceptual identification and definition introduced by the authors; supported by theoretical grounding in social sciences, philosophy, and humanitarian practice rather than empirical trials or quantified samples.
That digital-vs-physical framing misses the most consequential boundary: the one within digital tasks.
Normative/theoretical argument presented in the paper contrasting existing framing with a proposed alternative; grounded in cross-disciplinary literature rather than empirical measurement.
Employees experience technostress, anxiety and micro-political negotiation around AI tools in everyday work.
Reported experiences from semistructured interviews with 28 managers/professionals across 12 organizations; thematic analysis highlighting technostress and anxiety as themes.