Evidence (220 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 |
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.
These patterns suggest a commoditization effect of AI on labor, with implications for online labor market design, workers' incentives to invest in human capital, and labor welfare.
Interpretation synthesized from the three empirical findings above (decline in human-capital importance, rise in price importance, decline in demand premium for high-human-capital workers, and reallocation toward lower-priced workers). This is presented as the paper's conceptual/mechanistic conclusion and policy implication rather than a separately tested causal estimate. (Empirical basis: Upwork analysis and difference-in-differences; sample size not reported in abstract.)
Dominant comments shifted in tone from mockery toward gatekeeping and structural protest.
Speech-act coding of 300 confirmed accusations and sentiment/trajectory analysis showing relative decline in mockery-coded acts and increase in gatekeeping/structural-protest acts over time.
There is a significant U-shaped relationship between AI application and employees' job insecurity: moderate AI application reduces insecurity, whereas excessive application heightens it.
Empirical analysis of cross-sectional self-reported questionnaire data collected from employees (411 valid responses) using regression-type analyses reported as showing a significant U-shaped relationship between AI application intensity and job insecurity.
This research employed a vignette experiment to investigate how the embeddedness of GenAI and organizational authorization impact employees' negative emotion (specifically guilt) and risk perception.
Stated method in paper: a vignette experiment was used to test effects on guilt and risk perception. (No sample size reported in the provided text.)
Algorithmic systems for productivity and performance monitoring generate efficiencies but also create new pressures in technology-mediated work environments, including the tracking of employees’ emotional and physiological responses at work and during non-work time.
Literature synthesis and citations (e.g. Giermindl et al., 2022; McCartney and Fu, 2022; Norlander et al., 2021; Downie et al., 2025).
Acquiescent silence (resignation-based) is motivationally distinct from defensive (fear-driven) silence.
Theoretical distinction advanced using organisational silence literature (conceptual claim referencing existing theory).
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.
The reviewer-effort collapse creates a welfare misalignment: authors benefit from a weakened 'rat race' while editors suffer from degraded signal informativeness.
Comparative statics and welfare analysis in the theoretical model showing authors' equilibrium payoffs rise as competition/polishing dissipates, while editor's signal informativeness declines due to lower reviewer effort.
Social, cultural, and ethical considerations influence women’s engagement in AI-centric workplaces.
Claim made in the review, based on interdisciplinary literature that includes sociocultural analyses and ethical discussions; the abstract does not provide empirical effect estimates or sample sizes.
Sensitivity analyses indicate the observed positive belief changes likely reflect recovery from carry-over effects rather than genuine training-induced shifts.
Authors' sensitivity analyses discussed in the paper that examined alternative explanations (e.g., carry-over effects) and concluded the belief-change result is likely due to recovery from such effects.
Qualitative results underscored both perceived benefits in comprehension and challenges when interpretations of gaze behaviors were inaccurate.
Qualitative analysis of participant feedback from the study (n=36) reporting themes of improved comprehension and occasional problems when the assistant misinterpreted gaze.
AI adoption has an inverted U-shaped effect on employee-related corporate social responsibility (ECSR).
Panel regression with quadratic specification (AI and AI^2) showing statistically significant positive coefficient on AI and statistically significant negative coefficient on AI^2; sample of 2,575 Chinese listed firms observed 2013–2023; controls, firm and/or year fixed effects and robustness checks reported.
Algorithmic management reduces worker autonomy (loss of autonomy) in warehouse settings.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). Sample sizes not reported in this paper.
Algorithmic management in automated logistics generates surveillance anxiety among workers.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). No sample size given.
The paper identifies an emergent phenomenon called 'Precariousness 2.0' — a state of manufactured uncertainty characterized by loss of professional autonomy and chronic anxiety among workers.
Conceptual/qualitative construct developed in the paper from synthesis of secondary reports and national observations; no primary survey data cited supporting prevalence or magnitude.
Longer system responses and more information-providing turns negatively affect user satisfaction.
Statistical modeling of user satisfaction using features of multi-turn interactions (response length, number of information-providing turns) derived from the 49 participant sessions; models show negative associations reported in the paper.
Data workers in Kenya report direct employment by big tech corporations and exposure to graphic content.
Qualitative interviews / responses from data workers in Kenya collected and reported in the paper.
Hyper-datafication systematically redistributes labour risks and representational harms toward the Global South.
Qualitative responses from data workers in Kenya describing labour conditions and exposure; analysis of language data representation; external data on global data centre infrastructure and geography.
Algorithmic management introduces significant challenges related to fairness, transparency, and worker dignity.
Synthesis of qualitative interview findings (16 gig workers and 21 stakeholders) interpreted through a social justice framework.
In the production stage, workers lose decision-making power.
Theoretical analysis of production relations using Marxist reproduction framework; qualitative claim without reported empirical data.
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.
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.
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.
Transformational leadership negatively moderates the relationship between AI application and employees' job insecurity, buffering employees' insecurity responses across varying levels of AI application.
Moderation analysis reported in the study using the same employee survey dataset (411 valid responses), indicating a statistically significant buffering (negative) moderating effect of transformational leadership on the AI–job insecurity relationship.
Self-efficacy negatively moderates the relationship between AI application and employees' job insecurity by strengthening the insecurity-reducing effect of moderate AI application and weakening the insecurity-enhancing effect of excessive application.
Moderation analysis on the same cross-sectional survey data (411 valid employee questionnaires), reporting a statistically significant negative (buffering) interaction of self-efficacy with AI application intensity on job insecurity.
GenAI usage significantly decreased intrinsic task motivation.
Randomized experiment reported in the paper with 82 participants; authors report a statistically significant decrease in intrinsic task motivation for participants using GenAI.
AACT also triggers higher cognitive load.
Reported measurement of cognitive load in the same house price prediction case study comparing AACT to traditional AI support (details and sample size not provided in abstract).
Foucaultcu perspektiften algoritmik yönetimsellik, bireyi yalnızca denetlenen bir özne haline getirmekle kalmayıp, aynı zamanda davranışsal fazlanın üreticisi olan bir veri-nesnesine dönüştürmektedir.
Foucault teorik çerçevesiyle yapılan kavramsal analiz; literatüre dayalı argüman; no empirical sample provided in abstract.
Organizations implementing AI without responsible transition mechanisms may worsen workforce anxiety, skill obsolescence, inequality, and trust erosion.
Paper's theoretical/conceptual assertion about risks of poorly-managed AI adoption; no empirical validation reported in the excerpt.
Integrations of AI that neglect human factors are associated with increased anxiety, burnout, and disengagement among users.
Aggregate findings from the systematic review reporting associations in the literature between non-human-centered AI integration and negative psychological/work outcomes.
In most cases, workers wanted systems that are precise, insightful, or personal, but instead received systems that are basic, simple, or general.
Qualitative/quantitative comparison of preferred traits (from 202 workers) versus traits observed in AI systems in incident reports (LLM-coded); reported dominant preference traits versus dominant delivered traits.
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.
AI adoption and accelerating automation amplify employment precarity in labor‑surplus economies.
Conceptual synthesis grounded in economic geography and labor economics, supported by comparative field evidence cited for labor‑surplus contexts (no quantitative sample size reported).
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.
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.
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.
Individuals low in trait self-efficacy experienced the steepest ownership erosion (i.e., AI-authorship reduced psychological ownership most for low self-efficacy participants).
Reported moderation analysis in the preregistered experiment showing trait self-efficacy moderated the authorship effect on psychological ownership; preregistered N = 470. (No numeric effect size reported in the abstract.)
Participants in the LLM condition reported lower perceived importance (d = 1.13).
Same preregistered experiment; reported effect size d = 1.13; preregistered N = 470.
Participants in the LLM condition reported lower commitment (d = 1.19).
Same preregistered experiment comparing self-authored vs LLM-authored goals; reported effect size d = 1.19; preregistered N = 470.
Participants in the LLM condition reported lower psychological ownership (d = 1.38).
Same preregistered experiment (between-subjects comparison of authorship); reported effect size d = 1.38; preregistered N = 470.
Creators associate legible AI assistance with intertwined trust vulnerabilities, including epistemic unreliability, anticipated relational penalties, and platform authenticity regimes.
Thematic findings from 16 interviews in which creators express concerns about AI-generated content being epistemically unreliable, damaging relationships with audiences, and conflicting with platform authenticity norms.
Fear of AI automation is widespread and cuts across educational groups.
Analysis of emerging public opinion data from the 2024 OECD 'Risks that Matter' survey, reported in the paper (survey-based finding).