Evidence (3308 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
Filter claims →
Productivity
8807 claims
Filter claims →
Governance
7870 claims
Filter claims →
Human-AI Collaboration
7560 claims
Filter claims →
Org Design
4892 claims
Filter claims →
Innovation
4781 claims
Filter claims →
Labor Markets
4004 claims
Filter claims →
Skills & Training
3308 claims
Filtered →
Inequality
2332 claims
Filter claims →
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 |
Skills Training
Remove filter
Automation has intensified income inequality between high-skilled and low-skilled workers.
Synthesis of empirical literature linking automation adoption to widening wage and income gaps across skill groups (literature review).
Displacement effects have extended from manufacturing into cognitive roles such as clerical work and customer service.
Review of empirical studies documenting automation/substitution effects in cognitive, clerical, and customer-service roles (literature synthesis).
Automation has put downward pressure on wages.
Cited empirical studies and wage analyses in the reviewed literature indicating wage suppression associated with automation adoption (literature review).
AI and robotics have led to contractions in low-skilled occupations.
Synthesis of empirical literature reporting occupational contractions in low-skilled jobs following automation adoption (literature review).
Extensive empirical evidence shows that AI and robotics can substitute for rule-based, codifiable routine tasks.
Review cites extensive empirical studies demonstrating substitution of rule-based, codifiable routine tasks by AI/robotics (literature synthesis).
Artificial intelligence and robotic technologies are fundamentally reshaping labour markets and pose multifaceted challenges to workers engaged in routine and low-skilled tasks.
Narrative review of domestic and international scholarly literature over the past decade (literature review / synthesis).
Structural barriers, workforce biases, and digital skill gaps affect women’s participation in AI-enabled sectors.
Claim derived from the paper's synthesis of literature (peer-reviewed studies, policy analyses, preprints) identifying common barriers; the abstract does not report quantitative meta-analysis or specific sample sizes.
Routine-intensive sectors exhibit higher susceptibility to automation.
Synthesis result reported in the paper based on the systematic review of sector-specific literature (no numeric aggregation or sample size provided in the abstract).
Scalable AI tutoring for procedural skill learning requires structured knowledge representations, yet constructing these representations remains a labor-intensive bottleneck.
Background/claim made in the paper's introduction framing the problem; no specific quantitative evidence reported in the abstract.
The study is framed based on Job Demands-Resources (JD-R) theory, positing that HAI-C task complexity is a job demand and AI self-efficacy/humble leadership act as resources that can mitigate negative effects on engagement.
Introduction states JD-R theory as the theoretical basis and describes job demands (HAI-C task complexity) and job/personal resources (humble leadership, AI self-efficacy) in the hypothesized model.
HAI-C tech-learning anxiety reduces employees' work engagement (serves as the mediator between HAI-C task complexity and work engagement).
Mediation analysis via hierarchical regression and bootstrapping on the three-wave survey sample of 497 employees; reported in Results as the mediating mechanism.
Human-AI collaboration task complexity (HAI-C task complexity) negatively affects employees' work engagement by amplifying their HAI-C tech-learning anxiety.
Three-wave longitudinal survey of matched data from 497 employees; mediation analysis using hierarchical regression and bootstrapping reported in the Results section.
Users push back against agent outputs -- through corrections, failure reports, and interruptions -- in 44% of all turns.
Turn-level coding of user behavior in the SWE-chat dataset: proportion of conversational turns containing correction/complaint/interrupt signals, computed across >63,000 user prompts and sessions.
Agent-written code introduces more security vulnerabilities than code authored by humans.
Comparative analysis of security vulnerabilities attributed to agent-authored code versus human-authored code within the SWE-chat dataset (method details not specified in excerpt).
Just 44% of all agent-produced code survives into user commits.
Empirical measurement of code provenance and survival within the SWE-chat dataset: proportion of agent-produced code that becomes part of subsequent user commits across sessions.
Despite rapidly improving capabilities, coding agents remain inefficient in natural settings.
Authors' summary claim supported by dataset-derived metrics such as agent code survival rate (44%) and user pushback (44% of turns); observational analysis of SWE-chat.
The policy and research challenge posed by platform-mediated automation is not merely job quantity (technological unemployment) but institutional continuity — how societies reproduce practical competence when platforms optimize for efficiency rather than formation.
Normative and conceptual claim developed through literature synthesis (institutional economics, platform governance, workforce development); presented as an analytical reframing rather than an empirically tested hypothesis.
Entry-level roles have historically functioned as apprenticeships in which workers acquire tacit knowledge and critical judgment; if platforms curtail these formative occupational layers, organizations may lack future workers capable of exercising contextual reasoning required to manage complex systems.
Institutional economics and workforce development literature cited in the paper; conceptual synthesis without original empirical measurement reported.
Platform-mediated automation risks hollowing out labor structures from both directions: eroding repetitive, junior roles from below and automating supervisory coordination functions from above.
Theoretical argument synthesizing institutional economics and platform literature; articulated as a conceptual risk rather than demonstrated with original empirical data.
Algorithmic systems are displacing routine tasks across both low-wage entry-level work and middle-management functions.
Stated in paper's argumentation; supported by a literature-based review drawing on platform governance literature and recent research on AI-enhanced automation (no original empirical sample or quantitative study reported).
As multimodal AI achieves human-parity understanding of speech and gesture, [the keyboard's] necessity dissolves.
Theoretical claim supported by multidisciplinary review (history, neuroscience, technology, organizational studies); no quantified empirical test reported.
There was a nonsignificant absolute retest performance reduction in the AI condition and a larger retest performance decrement in the AI condition (i.e., retention decreased more after using Copilot).
Comparison of retest (one-week) performance across conditions reported in results; authors report a nonsignificant reduction and larger decrement for the AI/Copilot condition (n=22).
Thin training coverage fosters anxiety about substitution and slows diffusion of AI tools.
Reported associations from surveys of mid-level managers and technical staff, interviews, and document analysis across cases; thematic coding identified links between limited training, worker anxiety, and slower diffusion. (Sample size not reported.)
Agency in software engineering is primarily constrained by organizational policies rather than individual preferences.
Authors' synthesis of qualitative results across the ACTA/Delphi and task/review phases indicating organizational policy factors were cited as primary constraints.
Underreliance on AI might deprive software developers of potential gains in productivity and quality.
Stated in the paper and motivated by themes from twenty-two developer interviews indicating missed benefits when developers underuse LLM tools.
Overreliance on AI may lead to long-term negative consequences (e.g., atrophy of critical thinking skills).
Paper explicitly states this risk and grounds the discussion in findings from twenty-two developer interviews (qualitative evidence and participant-reported concerns).
AI can exacerbate occupational polarization, digital exclusion, and discriminatory outcomes when models are trained on biased data or deployed without transparency and accountability.
Thematic synthesis across included studies identifying mechanisms (biased training data, lack of transparency/accountability) linked to negative distributional outcomes (occupational polarization, digital exclusion, discrimination).
Small and medium-sized practices face challenges of skill gaps and resource constraints that hinder adoption of technology and data analytics.
Consistent findings across included studies highlighting barriers in small and medium-sized practices (SMPs).
Large language models remain confined to linguistic simulation rather than grounded understanding.
Conceptual assertion in the paper arguing limits of current models; no empirical tests or measurements reported.
Human decision makers may fail to execute optimal follow-up actions, potentially reducing overall performance.
Motivating argument in the paper (conceptual observation about human suboptimal policies in sequential decision-making).
The opacity, fluency, and low-friction interaction patterns of LLMs obscure the boundary between human and machine contribution, leading users to infer competence from outputs rather than from the processes that generate them.
Theoretical argument grounded in prior literature on automation bias and cognitive offloading; presented as explanatory mechanism in the paper rather than an empirically tested causal estimate.
The paper introduces the 'LLM fallacy,' a cognitive attribution error in which individuals misinterpret LLM-assisted outputs as evidence of their own independent competence, producing a systematic divergence between perceived and actual capability.
Conceptual/theoretical claim and formal definition offered in the paper; no empirical validation reported in the abstract.
Infrastructure constraints, particularly in developing countries, limit AI adoption in auditing.
Thematic analysis of reviewed articles noting infrastructure limitations (e.g., ICT infrastructure) in developing-country contexts.
Limitations in auditor competencies (skills and training) hinder effective AI adoption in auditing.
Thematic findings across the sample of articles report auditor competency gaps as a challenge to AI implementation.
Ethical and data privacy concerns are persistent challenges to AI implementation in auditing.
Recurring theme in the reviewed literature identified via thematic analysis; papers cite ethics and privacy as obstacles.
Several challenges persist for AI adoption in auditing, including high technology investment costs.
Thematic analysis of barriers reported across the 15 articles highlighting cost as a recurrent challenge.
Asymptomatic effects of AI use evolved into chronic harms such as skill atrophy and identity commoditization among workers.
Reported longitudinal findings from the study indicating progression from asymptomatic (subtle) effects to chronic harms; abstract lists harms but provides no quantification or sample details.
Initial operational gains from AI use masked a phenomenon called 'intuition rust' — a gradual dulling of expert judgment.
Empirical observation reported from the year-long longitudinal study of cancer specialists (phenomenon named and described; abstract provides no quantitative measures or sample size).
Low-skill roles in packaging, sorting, and basic assembly face a high risk of automation.
Paper's findings/prediction derived from task-level classification (routine/repetitive tasks) applied to jobs in Nagpur's medium enterprises; no reported sample size or quantified risk metrics in the excerpt.
The study's findings are subject to design limitations including an AM/PM session confound, differential attrition, and LLM grading sensitivity to document length.
Authors' reported limitations section citing specific threats to internal validity and measurement (session timing confound, differential attrition across conditions, and grading biases of the LLM used to evaluate documents).
The behavioral scaffolding intervention was associated with substantially lower document production.
Same field experiment (N=388); the behavioral scaffolding required joint AI use within pairs and was compared to unstructured use, with reported reductions in document production in the behavioral condition.
A behavioral scaffolding intervention (a structured protocol requiring joint AI use within pairs) was associated with lower document quality relative to unstructured use.
Field experiment with 388 employees at a Fortune 500 retailer; random/experimental assignment to scaffolding conditions while all participants had access to the same AI tool; comparison reported between behavioral scaffolding condition and unstructured use.
Latent-outcome estimation faces a within-study noncomparability challenge: different indicators within a study may have different and possibly nonlinear relationships with the same latent outcome, making them not directly comparable.
Theoretical exposition in the paper describing heterogenous indicator-to-latent mappings and potential nonlinearity; illustrated with examples (no empirical sample size).
Latent-outcome estimation faces a cross-study noncomparability challenge: different measurement systems across studies may cause estimators to target different empirical quantities even when the underlying latent treatment effect is the same.
Conceptual and theoretical argumentation in the paper describing identification issues across studies due to differing measurement systems; supported by examples and discussion (no empirical sample size).
Rote learning will become obsolete in favor of contextual application.
Paper's forward-looking prediction based on synthesis of adult learning theory and workforce development literature; no empirical sample size or quantified trend data provided.
These advancements have raised concerns regarding workforce redundancy, particularly for routine and low-skilled jobs.
Synthesis of concerns documented in the reviewed literature and observed sectoral trends (literature review; qualitative synthesis).
Foundation-model usage can increase compute-related emissions.
Conceptual/environmental concern highlighted in the paper about the carbon footprint of heavy model use and persistent storage; no quantified emissions analysis or lifecycle assessment presented.
These systems can cause skill atrophy.
Theoretical risk articulated in the paper that reliance on AI assistance may degrade human skills over time; no longitudinal skill-measurement or experimental evidence provided.
The same foundation-model systems can also intensify surveillance.
Cautionary claim in the paper noting the surveillance risk of durable, queryable traces and integrated tooling; presented as a conceptual risk rather than empirically measured increase in surveillance.
Job insecurity emerges as a critical mediating factor influencing employee attitudes and behavioural responses to generative AI, including upskilling intentions and resistance to technological change.
Review-level synthesis identifying job insecurity reported in included studies as mediating relationships between AI adoption and employee attitudes/behaviours (e.g., upskilling, resistance).