Evidence (2608 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Skills Training
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Artificial intelligence (AI) is rapidly reshaping knowledge-intensive work by automating, augmenting, and reconfiguring core professional activities.
Paper asserts this as a motivating observation based on prior literature and descriptive claims; no original empirical sample or quantified data reported.
Augmented work agency is shaped by whether applications are generative or non-generative, by employees' experiences of anxiety and technostress, and by micro-politics through which teams negotiate AI use and AI ethics.
Thematic findings from semistructured interviews (28 participants) and document review identifying these factors as shaping agency in practice.
The analysis uncovers three central tensions shaping AI-mediated work: autonomy versus orchestration; capability versus dependency; and experimentation versus ethics.
Recurring themes identified through qualitative interviews (28 participants) and document review; interpretive synthesis presented in findings.
AI integration transforms managerial practices, workforce identities and organizational coordination.
Thematic and interpretive analysis of semistructured interviews with 28 managers/professionals across 12 organizations and review of organizational documents.
Accounting for heterogeneity in AI literacy (agents' ability to identify and adapt to inaccurate AI outputs) can produce skill polarization in the long-run steady state.
Analytical/theoretical steady-state distribution analysis of agent skill dynamics with heterogeneous AI literacy parameters; paper reports conditions under which polarization emerges (theoretical, no empirical sample).
Aggregate effects are geographically uneven (geographic unevenness in AI-driven labor market impacts).
Synthesis across studies observing variation by geography and noting non-Anglophone markets and developing economies as under-studied and differentially affected.
Wage polarization characterizes the aggregate pattern of labor market change associated with recent AI advances.
Aggregate characterization from synthesized studies reporting divergent wage outcomes (higher wages for AI-augmented workers, pressures on junior/routine roles) consistent with polarization.
Sectoral effects are heterogeneous: infrastructure, security, and quality-assurance roles have expanded while developer roles have contracted.
Qualitative and quantitative results aggregated across the included studies noting role-level expansions and contractions; no single pooled effect size provided.
Non-routine employment and wages exhibit a crossing pattern: initially higher under fast adoption, then lower — so faster adoption can simultaneously raise long-run wages for survivors while permanently reducing participation.
Comparative dynamic trajectories in the model showing time paths for non-routine employment and wages under fast vs. slow adoption scenarios (analytical and/or simulated model paths).
Even when two economies share the same long-run automation level, adoption speed alone determines transition welfare.
Comparative-welfare analysis in the dynamic theoretical model holding long-run automation level fixed while varying adoption speed (analytical comparative statics).
The rapid emergence of agentic AI tools raises new questions that the political science discipline must address.
Epilogue of the report raises agentic AI tools as a rapidly emerging phenomenon and lists questions for the discipline; based on expert judgment and forward-looking analysis rather than empirical measurement in the introduction/epilogue.
AI will affect political science research and teaching.
Report introduction explicitly notes the report investigates implications for political science research and teaching; based on the task force's review and analysis rather than a quantitative study.
AI will affect public opinion and the information ecosystem.
Introductory chapter enumerates public opinion and the information ecosystem as report topics; based on conceptual synthesis and literature review.
AI will affect the labor market.
Report introduction identifies the labor market as an area the task force examines; presented as a conceptual claim without primary-sample estimates in the introduction.
AI will affect international relations.
Introductory chapter lists international relations as a topic the report investigates; claim arises from conceptual analysis and synthesis by task force authors.
AI will affect national security.
Report introduction stating a section addressing national security implications; based on expert assessment and literature review rather than a specific empirical sample.
AI will affect public administration.
Report introduction describing a section focused on how AI will affect public administration; based on expert synthesis rather than reported empirical study.
AI will affect democracy (i.e., democratic processes and institutions).
Report introduction listing a section of the report devoted to democracy and AI; conceptual argumentation rather than reported empirical tests.
AI has the potential to reshape politics and political science, similar to how it is transforming other social phenomena and academic fields.
Introductory chapter of the APSA Presidential Task Force report; conceptual framing and literature synthesis by the task force authors (no primary empirical sample reported).
Routine automation primarily dismantles specialised physical skills, enhancing mobility only within homogeneous manual clusters.
Simulation results distinguishing effects of the routine-task automation exposure measure vs. AI exposure; analysis of which skill types are eroded and resulting changes in mobility within occupational clusters.
There is substantial heterogeneity in the productivity effects across settings.
Meta-analytic heterogeneity assessment reported in the paper (subgroup/moderator analyses indicate variability by context). The paper states 'substantial heterogeneity across settings.'
Program outcomes are moderated by a person's prior occupational skill set, their area of work, and features of the local economy.
Heterogeneity analyses across subgroups defined by prior occupational skill composition, industry/area of work, and local labor-market conditions in the WIOA administrative data (2017-2023) show variation in outcomes.
Fluent users' failures occur alongside greater success on complex tasks.
Combined analysis of task complexity, success outcomes, and failure incidence in the 27K transcripts showing that fluent users both attempt and have greater success on complex tasks even while experiencing more failures.
Fluent users adopt a fundamentally different interactional mode: they iterate collaboratively with the AI, refining goals and critically assessing outputs, whereas novices take a passive stance.
Qualitative and quantitative analysis of the same 27,000 annotated WildChat transcripts, with annotations describing interactional mode and user behavior (iteration, goal refinement, critical assessment vs. passivity).
Generative AI-powered tools like ChatGPT are reshaping market skill demands while also offering new forms of on-demand learning support to meet those demands.
Framed in paper as background/motivation; asserted from prior literature and the paper's motivating claims rather than reported as a quantified result in this study.
The rapid growth of AI and automation offers Sub-Saharan Africa economic opportunities as well as labor market challenges.
Systematic review of the literature reported in the paper; scope and number of studies not specified in the abstract/summary provided.
AI adoption leads both to job displacement and job creation, including the emergence of new occupational categories.
Abstract states the review examines empirical evidence on both job displacement and creation and the emergence of new occupations; no numeric counts or sample sizes provided in abstract.
The study identifies short-term transitional risks and long-term productivity gains associated with AI integration in the workforce.
Abstract states the paper evaluates both short-term risks and long-term productivity gains from AI integration based on the reviewed literature; no empirical quantification given in abstract.
AI-driven automation and augmentation are reshaping employment landscapes, with emphasis on sector-level disruption, skill transformation, and socioeconomic consequences.
Abstract states this as a conclusion of the review drawing on interdisciplinary empirical literature; no specific studies or sample sizes cited in abstract.
The accelerating deployment of artificial intelligence across industries has fundamentally altered the structure of global labour markets.
Statement in abstract summarizing a systematic review of interdisciplinary literature (economics, computer science, organizational behaviour, public policy); no specific sample size reported in abstract.
Demographic characteristics intersect with AI exposure—i.e., exposure varies by demographic groups.
Paper reports that it examines how demographic characteristics intersect with exposure based on recent empirical studies; no demographic breakdowns or sample sizes provided in the abstract.
Recent studies combine task-level exposure metrics with employment and usage data to assess AI exposure and impacts.
Paper notes that it draws on studies that use task-level exposure metrics alongside employment and usage data; methodological claim rather than a quantitative result.
Generative large language models (LLMs) present organizations with a transformative technology whose labor market implications remain nascent yet consequential.
Statement in paper synthesizing emerging empirical research; no specific study, method, or sample size reported in the abstract.
The adoption of AI in Israel constitutes a systemic transformation of employment relations, necessitating doctrinal adaptation and institutional reform to keep the labor market aligned with foundational legal principles.
Synthesis and conclusion from the paper's combined legal and empirical analysis; presented as the author's overarching interpretive claim rather than as a specific quantified finding.
Within the public sector, there is an emerging policy trend to incorporate AI considerations into workforce planning, including examining whether human positions may be substituted by technological solutions prior to recruiting new employees.
Paper reports an observed policy trend in public-sector workforce planning; specific policy documents, jurisdictions, or counts not provided in the excerpt.
The distribution of complementary (non-AI) skills across the workforce shapes whether AI improvements generate productivity bottlenecks or concentration-driven inequality.
Derived from the task-based model analysis described in the article; framed as a theoretical mechanism with reference to empirical patterns but without specific empirical study details in the excerpt.
AI influences innovation performance in organizations.
Discussion and synthesis of studies and reports on AI adoption and innovation performance presented in the review.
AI adoption is producing organizational implications, including changes in project management practices.
Findings synthesized from conference papers, case studies and industry reports included in the review.
Automation, generative AI, and intelligent systems are reshaping task structures, leading to both job displacement risks and the creation of new AI-driven roles.
Synthesis of empirical studies, conference findings, and industry reports reporting both displacement risks and new role emergence (review paper).
AI is rapidly transforming the nature of work, the demand for skills, and the professional roles of Information Technology (IT) practitioners.
Stated as a synthesis result from a narrative review of recent empirical studies, conference findings, and industry reports (review paper).
Survey evidence suggests public attitudes towards AI combine optimism with apprehension, and most respondents oppose granting AI systems final authority over hiring and dismissal decisions.
Review cites multiple public opinion and survey studies reporting mixed (optimistic and apprehensive) attitudes and opposition to AI final authority in employment decisions (survey evidence summarized).
There are important regional differences—especially in developing contexts—that necessitate context-specific approaches to improving women’s participation in AI-enabled work.
Observation reported in the review drawing on geographically diverse studies and policy analyses; the abstract does not quantify differences or report sample sizes for cross-region comparisons.
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.
AI applications—ranging from recruitment algorithms to workplace automation—can either reinforce gender disparities or promote equitable employment outcomes.
Stated in the review based on collated findings from multiple studies and analyses that document both harms (e.g., biased recruitment algorithms) and potential benefits (e.g., tools designed to reduce bias); no single empirical study or pooled effect size provided in the abstract.
Artificial Intelligence (AI) is rapidly transforming workplaces across the globe, offering both novel opportunities and unique challenges for women in technology-driven industries.
Stated in the paper's introduction/abstract as a summary conclusion based on a narrative literature review of peer-reviewed studies, policy analyses, and preprint research; no specific sample size or primary empirical method reported in the abstract.
The study proposes a sectoral risk classification to better understand vulnerability patterns and workforce implications.
Paper reports development/proposal of a sectoral risk classification as a contribution (the classification itself and validation details are not described in the abstract).
The rapid integration of Artificial Intelligence (AI) across industries is fundamentally reshaping occupational structures and redefining employment dynamics.
Stated as an overall conclusion of the paper based on a systematic review of recent literature from major academic databases (details of included studies not provided in the abstract).
Coding patterns are bimodal: in 41% of sessions, agents author virtually all committed code ("vibe coding"), while in 23%, humans write all code themselves.
Empirical analysis of authorship attribution across the 6,000 sessions in the SWE-chat dataset; percentages derived from session-level classification.
Given the results, educators should revisit pair programming as an educational tool in addition to embracing modern AI.
Authors' recommendation in the paper's conclusion based on experimental findings (performance, workload, emotion, retention outcomes).
Experienced developers maintain control through detailed delegation while novices struggle between over-reliance and cautious avoidance.
Observed behaviors and accounts from the AI-assisted debugging task (10 juniors) and senior participants in ACTA/Delphi and blind review phases (5 + 5 seniors).