Evidence (3062 claims)
Adoption
5227 claims
Productivity
4503 claims
Governance
4100 claims
Human-AI Collaboration
3062 claims
Labor Markets
2480 claims
Innovation
2320 claims
Org Design
2305 claims
Skills & Training
1920 claims
Inequality
1311 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 373 | 105 | 59 | 439 | 984 |
| Governance & Regulation | 366 | 172 | 115 | 55 | 718 |
| Research Productivity | 237 | 95 | 34 | 294 | 664 |
| Organizational Efficiency | 364 | 82 | 62 | 34 | 545 |
| Technology Adoption Rate | 293 | 118 | 66 | 30 | 511 |
| Firm Productivity | 274 | 33 | 68 | 10 | 390 |
| AI Safety & Ethics | 117 | 178 | 44 | 24 | 365 |
| Output Quality | 231 | 61 | 23 | 25 | 340 |
| Market Structure | 107 | 123 | 85 | 14 | 334 |
| Decision Quality | 158 | 68 | 33 | 17 | 279 |
| Fiscal & Macroeconomic | 75 | 52 | 32 | 21 | 187 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Skill Acquisition | 88 | 31 | 38 | 9 | 166 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 105 | 12 | 21 | 11 | 150 |
| Consumer Welfare | 68 | 29 | 35 | 7 | 139 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 68 | 31 | 4 | 127 |
| Task Allocation | 71 | 10 | 29 | 6 | 116 |
| Worker Satisfaction | 46 | 38 | 12 | 9 | 105 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 11 | 16 | 94 |
| Task Completion Time | 76 | 5 | 4 | 2 | 87 |
| Wages & Compensation | 46 | 13 | 19 | 5 | 83 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 16 | 9 | 5 | 48 |
| Job Displacement | 5 | 29 | 12 | — | 46 |
| Social Protection | 19 | 8 | 6 | 1 | 34 |
| Developer Productivity | 27 | 2 | 3 | 1 | 33 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 8 | 4 | 9 | — | 21 |
Human Ai Collab
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The emergence of ChatGPT in November 2022 disrupted practice in knowledge work and defied performance-measurement systems in human-exclusive task accomplishment under unprecedented comparability.
Author claim framed against timeline of ChatGPT release; contextualized by the study's broader empirical analysis (systematic analysis of 5,000 LinkedIn job adverts and 2,000 Indeed salary records from 2022–2024) used to support the narrative of disruption.
Organisations struggle to optimise human–AI collaboration in knowledge‑intensive decision‑making.
Statement based on a systematic synthesis of human–AI interaction and knowledge management literature presented in the paper; no primary empirical sample or dataset reported in the abstract.
Despite increased deployment, the field lacks a principled framework for answering when a team is helpful, how many agents to use, how team structure impacts performance, and whether a team is better than a single agent.
Authors' assessment of the literature and gaps; presented as a motivation for their work (no empirical count of missing frameworks given in excerpt).
Tasks that workers associate with a sense of agency or happiness may be disproportionately exposed to AI.
Empirical finding based on the paper's worker and developer surveys on 171 tasks, with LM scaling to 10,131 tasks; phrased cautiously in the paper as 'may be' disproportionately exposed.
Three skills degrade performance (up to -10%) due to version-mismatched guidance conflicting with project context.
Observed three skills with negative pass-rate changes up to -10% in the paired evaluation; authors attribute the degradation to guidance in the skills being mismatched to project versions/context.
Skill injection benefits are far more limited than rapid adoption suggests.
Aggregate evaluation results comparing agent performance with and without injected skills across the benchmark (49 skills, ~565 tasks) showing many skills yield no improvement and small average gains.
Evaluation frameworks remain predominantly model-centric, focusing on standalone AI performance rather than emergent collaborative outcomes.
Conceptual/literature critique presented in the paper motivating the new framework (review of prior evaluation practices; theoretical argument).
Aligned AI (trained to foster trust) can increase human trust but risks reinforcing suboptimal human behavior and lowering human-AI team performance.
Theoretical/ conceptual claim made in the paper (abstract); no specific empirical details provided in the excerpt.
Training AI to complement human strengths can decrease AI performance in areas where humans are strong, which can erode human trust and cause humans to ignore AI advice when it is most needed.
Argumentation and examples given in the paper (abstract); any empirical support referenced as part of the paper but sample sizes/details not provided in the excerpt.
Despite positive outcomes, challenges such as workforce displacement, ethical concerns, and limited access to AI technologies were identified as barriers to full adoption.
Study respondents reported barriers in the survey; descriptive statistics summarized the prevalence of workforce displacement concerns, ethical issues, and limited access to AI technologies as impediments to broader adoption.
There is a growing tension between relatively rigid education and training systems and the rapidly changing skill requirements of digitally driven labor markets.
Argument motivated and supported by comparative assessment of international practices and systemic analysis; descriptive/comparative evidence rather than quantified empirical testing.
Information saturation from AI output contributes to cognitive overload among employees.
Grounded in the paper's application of cognitive load theory to findings from surveys and organizational research; the excerpt gives no direct measures of information volume or its direct cognitive effects.
Extensive AI use correlates with measurable productivity losses.
Paper states this correlation is observed in organizational research and large-scale surveys; the excerpt lacks details on productivity measures, sample sizes, or statistical controls.
Extensive AI use correlates with increased decision fatigue.
Reported correlation based on the same cited large-scale surveys and organizational research; no methodological details or effect sizes provided in the excerpt.
Extensive AI use correlates with increased turnover intention among employees.
Paper reports correlations observed in recent large-scale surveys and organizational research; the excerpt does not provide correlation coefficients, sample sizes, or control variables.
AI-augmented work environments create cognitive overload through information saturation, relentless task-switching, and the demanding oversight of multiple AI agents.
Synthesis in the paper drawing on research on human-AI collaboration and cognitive load theory and citing organizational research; specific empirical methods or sample sizes not provided in the excerpt.
Employees using AI extensively report significant mental fatigue, dubbed 'AI brain fry.'
Stated in the paper as derived from recent large-scale surveys and organizational research; no specific sample size, survey instrument, or statistical details provided in the text excerpt.
O SCF é expandido para uma camada de segunda ordem (SCF-E) que incorpora déficit de imaginação tecnocultural e governança simbólica, explicando por que a IA permanece em pilotos e não se converte em capacidade organizacional.
Extensão conceitual (segunda ordem) relatada no artigo; respaldada metodologicamente pela combinação QUAN→QUAL, incluindo etnografia orientada ao SCF (detalhes empíricos no corpo do artigo, não no resumo).
A literatura de adoção tecnológica (TAM, UTAUT, Difusão de Inovações) tende a tratar a resistência como variável comportamental genérica ou deficiência de 'treinamento', negligenciando dimensões simbólicas (ritos, identidades e poder), mecanismos cognitivos de ameaça (aversão à perda, sobrecarga e heurísticas) e seus efeitos econômicos.
Revisão bibliográfica e posicionamento teórico declarado no artigo comparando modelos consagrados com a perspectiva proposta; sem indicação de meta-análise ou contagem empírica no resumo.
A Fricção Psicoantropológica (SCF) é proposta e detalhada como um coeficiente mensurável do custo cultural e da resistência cognitiva que reduz a capacidade de pequenas e médias empresas (PMEs) de transformar iniciativas de Inteligência Artificial (IA) em geração de valor em escala.
Proposição teórica e operacionalização apresentada no artigo; desenho metodológico descrito como QUAN→QUAL incluindo construção de escala psicométrica e etnografia organizacional. O resumo não especifica tamanho de amostra para validação.
Alignment interventions (e.g., fine-tuning, instruction-following adjustments) can systematically reshape or obscure the cultural regularities learned during pretraining.
Analytical distinction drawn between base models and fine-tuned/aligned systems in the paper; claim based on conceptual analysis of how adaptation changes model behavior rather than on specific experimental results in the provided text.
The limitations of systems that prioritize academic pathways constrain workforce adaptability and inclusive labor market development.
Argument based on synthesis of empirical studies and secondary data connecting education pathway composition to workforce adaptability and inclusiveness (presented as a policy-relevant conclusion rather than a quantified causal estimate).
Skills mismatch in the labor market is structural and linked to education systems that prioritize academic pathways without adequate support for vocational and continuing training.
Integrated interpretation of comparative evidence and secondary data showing imbalances between academic and vocational provision and associated labor-market frictions (paper frames this as a structural conclusion; specific causal tests not described in the summary).
Expansion of intermediate vocational skills has been limited relative to the expansion of higher education.
Comparative evidence and secondary data showing smaller increases in intermediate vocational qualifications compared with higher education attainment (specific metrics/country coverage not provided in the summary).
Perceived autonomy amplifies the negative effects of perceived algorithmic behavioral constraint on riders' outcomes (i.e., strengthens the adverse impact on mental health and risky riding via work pressure).
Moderation results from SEM and bootstrapping on a sample of 466 Chinese food delivery riders showing interaction between behavioral constraint and perceived autonomy increases negative indirect effects through work pressure.
Perceived autonomy enhances the positive effect of perceived algorithmic standardized guidance in reducing risky riding behavior.
SEM moderation analysis with bootstrapping on data from 466 Chinese food delivery riders showing perceived autonomy strengthens the standardized guidance -> work pressure -> risky riding indirect pathway.
Perceived algorithmic standardized guidance reduces risky riding behavior among food delivery riders by reducing work pressure.
Survey of 466 Chinese food delivery riders analyzed with SEM and bootstrapping showing standardized guidance -> work pressure -> risky riding behavior (indirect effect).
Perceived algorithmic behavioral constraint impairs food delivery riders' mental health through increased work pressure.
Survey of 466 Chinese food delivery riders analyzed via SEM and bootstrapping with work pressure as mediator (behavioral constraint -> work pressure -> mental health).
Perceived algorithmic tracking evaluation impairs food delivery riders' mental health through increased work pressure.
Survey data from 466 Chinese food delivery riders analyzed with structural equation modeling (SEM) and bootstrapping; work pressure modeled as mediator based on the Job Demands-Resources (JD-R) framework; indirect effect from tracking evaluation -> work pressure -> mental health reported.
Short-run labor market disruptions raise concerns regarding wage inequality and workforce adaptation.
Claims based on observed short-run labor market adjustments in publicly available data and theoretical implications for inequality and adaptation; specific empirical measures, time horizons, and sample sizes are not reported in the excerpt.
AI simultaneously increases adjustment pressures for routine tasks.
Argument and cited observations from publicly available labor market data indicating displacement or adjustment in routine-task-intensive occupations (no specific empirical estimates or samples provided).
The Cautious are held in organizational stasis: without early adopter examples they don't enter the virtuous adoption cycle, never accumulate the usage frequency that drives intent, and never attain high efficacy.
Comparative analysis of archetype subgroups in the survey (N=147) showing the 'Cautious' group has lower reported usage frequency, lower intent to increase usage, and lower self-reported efficacy relative to 'Enthusiasts' and 'Pragmatists'.
Adoption of AI testing tools lags that of coding tools, creating a 'Testing Gap'.
Within-sample comparison of reported adoption rates for coding-oriented AI tools versus testing-oriented AI tools among 147 developers, showing lower adoption for testing tools.
Security concerns remain a moderate and statistically significant barrier to adoption.
Survey-derived security-concern metric (N=147) that shows a statistically significant negative association with future adoption intention (reported as moderate in effect size).
Traditional human resource management (HRM) approaches in hospitals rely on manual processes that are prone to errors, lack adaptability, and fail to adequately balance staff preferences with patient care requirements.
Background/positioning statement in the paper; asserted based on literature and authors' motivation for proposing an AI-driven framework (no specific dataset or quantitative analysis provided for this claim).
AI adoption increases psychosocial pressure on workers.
Themes surfaced via content analysis of recent peer-reviewed literature on AI and workforce wellbeing within the qualitative library research (specific studies not listed).
AI adoption contributes to inequality (uneven distribution of benefits and opportunities).
Synthesis of arguments and empirical findings from accredited journals included in the literature-based study (sources not enumerated).
AI leads to skill mismatch between workers and emerging job requirements.
Identified through thematic analysis of recent literature on workforce dynamics and skills in the qualitative review (specific article count not reported).
AI causes job displacement.
Recurring finding across reviewed accredited journal articles summarized via thematic content analysis in the library research (no quantitative sample provided).
Simulations project measurable reductions in defect rates under AI-HRM scenarios.
Regression-based simulations of the counterfactual model include defect reduction as an organizational outcome and project decreases in defect rates when HR processes are AI-supported.
Simulations show notable reductions in absenteeism under the AI-HRM scenario.
Predictive estimation and regression-based simulations projecting absenteeism rates under counterfactual AI-supported HR processes using the industrial firm dataset.
The helicoid failure regime was observed across diverse high-consequence domains: clinical diagnosis, investment evaluation, and high-consequence interviews.
Paper reports testing in three domain types during the prospective case series that found the helicoid pattern; evidence consists of domain-specific interaction transcripts and evaluations in the paper.
Under high stakes, when being rigorous and being comfortable diverge, these systems tend toward comfort, becoming less reliable precisely when reliability matters most.
Conclusion drawn from the case series across high-stakes scenarios (clinical, investment, interviews); evidence consists of observed behaviors and failure patterns in the tested interactions.
The helicoid pattern occurred in all seven systems tested, despite explicit protocols designed to sustain rigorous partnership.
Reported outcome of the prospective case series: 7/7 systems exhibited the described pattern; protocols to enforce rigor were applied during testing (details presumably in paper).
A prospective case series documents helicoid dynamics across seven leading systems (Claude, ChatGPT, Gemini, Grok, DeepSeek, Perplexity, Llama families).
Prospective case series described in the paper involving seven named LLM systems; sample size = 7 systems; domains tested include clinical diagnosis, investment evaluation, and high-consequence interviews.
LLMs perform differently when checking is impossible, such as in high-uncertainty, irreversible decisions (clinical treatment on incomplete data; investment under fundamental uncertainty).
Paper asserts this contrast and motivates the study; supporting evidence comes from the reported prospective case series across difficult decision domains (see below).
Reductions or cuts to governmental translation services intensify employment gaps, increase dependence on informal translation, and exacerbate systemic injustices for LEP immigrants.
Mixed-methods evidence from survey responses (n=150) indicating outcomes after policy reductions, and thematic findings from employer (n=50) and provider (n=20) interviews documenting increased informal translation reliance and adverse labor outcomes.
As AI adoption rises, demand for substitutable skills—such as summarisation, translation, or customer service—decreases.
Analysis of the same job postings dataset (2018–2024) linking measures of AI diffusion at company/industry/region level to changes in frequency of mentions of substitutable skills (examples: summarisation, translation, customer service).
Technological variations contribute to limiting sustainability efforts.
Highlighted in the paper's analysis of governance challenges (listed alongside corruption and administrative inefficiencies) and referenced in international examples; no specific empirical measurement or sample size is provided in the summary.
Deep-rooted governance issues — specifically corruption, administrative inefficiencies, policy gaps, and technological variations — restrict sustainability efforts, particularly in developing and transition economies.
Analytical emphasis in the paper drawing on global governance frameworks and case illustrations from international instances; the summary does not report empirical sample sizes or quantitative measures.