Evidence (3224 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 |
Labor Markets
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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 study establishes statistically significant relationships between organizational AI adoption and compensation dynamics.
Econometric estimates (difference-in-differences and propensity score matched comparisons) using the combined datasets listed in the paper and controlling for industry, firm size, geography, occupation characteristics, and macroeconomic variables.
The study establishes statistically significant relationships between organizational AI adoption and changes in occupational structures.
Same econometric approach (difference-in-differences and propensity score matching) applied to combined datasets (Anthropic Economic Index, Census Business Trends and Outlook Survey, Federal Reserve regional surveys, labor market analytics), with controls for industry, firm size, location, occupation-level characteristics, and macroeconomic environment.
The study establishes statistically significant relationships between organizational AI adoption and changes in employment patterns in the United States during 2022–2025.
Econometric analysis using multiple large-scale data sources (Anthropic Economic Index, U.S. Census Bureau Business Trends and Outlook Survey, Federal Reserve regional surveys, labor market analytics) and methods described as difference-in-differences estimation and propensity score matching controlling for industry (NAICS 2-digit), firm size, geography, occupation characteristics, and macro conditions.
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).
AIGC is reshaping the rights and obligations of platforms and workers.
Argument in the paper describing legal and practical impacts of AIGC on platform-worker relationships; based on doctrinal/legal analysis and discussion of platform practices rather than reported quantitative empirical data.
The study explores implications of algorithmic enterprises for competitive advantage, labour markets, and regulatory policy.
Declared scope of the paper in the abstract; exploration is conceptual and analytical rather than reporting empirical findings or quantified effects.
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).
AI is associated with a shift toward younger, relatively less educated workers.
Reported association in the paper's baseline empirical results linking AI presence/pervasiveness to changes in workforce composition (age and education).
Results also reveal divergences between the two interaction scenario types.
Abstract statement that divergences vary across different interaction contexts / scenario types.
Results reveal divergences between purely simulated and human study datasets.
Abstract reports that findings diverge between simulation experiments and the human-subjects dataset; comparisons drawn across the two datasets (simulation N=2000, human N=290).
The rapid advancement of artificial intelligence (AI) technologies, particularly generative AI and large language models, has reignited debates about the future of work and the potential for widespread labor market disruption.
Statement in the paper's introduction/abstract citing recent empirical studies, industry reports, and ongoing debates; no original sample or numerical evidence reported in the abstract.
Outcomes of AI deployment in labor-market settings depend on complementary organizational practices, workers’ access to skills, and the regulatory environment.
Synthesis-derived moderator/ mechanism claim from qualitative analysis of the 19 included studies identifying organizational practices, skill access, and regulation as contextual moderators.
This work establishes a foundation for understanding how generative AI systems not only augment cognitive performance but also reshape self-perception and perceived expertise.
Paper's stated contribution presenting theory and conceptual groundwork; no empirical validation provided in the abstract.
The LLM fallacy has implications for education, hiring, and AI literacy.
Implications and argumentation presented in the paper; these are prospective and conceptual rather than supported by empirical data in the abstract.
The analysis reveals a non-linear, U-shaped relationship between changes in frontier skill intensity and employment growth.
Statistical linkage of changes in frontier skill intensity (OTSS changes) to employment growth using administrative data from 2012–2023; reported functional form is U-shaped.
Frontier technologies remain concentrated in specialised occupations, while digital technologies are widespread.
Distributional analysis of OTSS across occupations showing concentration patterns of frontier technologies versus ubiquity of digital technologies.
For the average worker in 2023, manual technologies account for the largest share of skill content (42 per cent), followed by digital (38 per cent) and frontier technologies (20 per cent).
Computed OTSS applied to occupation-level data for Germany in 2023; reported shares for the "average worker".
The local labor market will follow a dual trajectory: low-skill, routine jobs face high automation risk while demand will rise for AI-collaborative, higher-skill roles.
Paper's analytical prediction based on distinguishing current job roles into routine/repetitive vs cognitive/non-routine and projecting likely impacts; no numeric forecasts or sample sizes provided in the excerpt.
Professional and Technical Services, Information, and Finance and Insurance account for approximately 86 percent of the base-case direct contribution.
Sectoral decomposition of base-case direct contribution in the model; paper explicitly reports the three sectors' combined share as ~86%.
While AI may reduce certain traditional roles, it also enhances job quality and creates new career pathways within the commerce sector.
Reported finding from the paper's synthesis of existing studies and sectoral observations (qualitative literature synthesis).
AI exhibits a dual nature—both as a disruptor and an enabler of employment in the commerce sector.
Paper-level synthesis of contradictory findings and sectoral patterns reported across reviewed literature (qualitative literature synthesis).
The effects of generative AI on work and organisations are heterogeneous and context-dependent, shaped by job roles, skill levels, and institutional environments.
Synthesis across the included studies noting variation in outcomes conditional on role, skill, and institutional context.
If employment losses are relatively small and productivity gains are realised, AI adoption could boost Exchequer revenues. But if job displacement is sizeable, tax receipts fall while welfare spending rises, resulting in potentially large pressures on the public finances.
Conditional fiscal scenarios simulated in the report combining employment, wage and benefit changes with the public finance implications (tax receipts and welfare spending); reported as scenario-based outcomes.
Ireland’s tax and welfare system absorbs most of the income loss for lower income households, and roughly half of the loss for households at the top of the income distribution.
Microsimulation using SWITCH to model taxes and transfers applied to simulated income changes across income groups; reported as a finding in the report.
India exhibits a distinctive polarisation pattern: a shrinking middle-skill workforce alongside a persistently large low-skill labour segment.
Descriptive analysis of secondary data and official reports from 2020–2024 comparing occupational and skill distributions in India.
Mathematics (SAFI: 73.2) and Programming (71.8) receive the highest automation feasibility scores; Active Listening (42.2) and Reading Comprehension (45.5) receive the lowest.
SAFI benchmark results reported for specific O*NET skills (numerical SAFI scores provided in the paper).
Chinese Marxism's dialectical approach—rooted in the yin‑yang principle—constitutes an alternative epistemology that fundamentally differs from Western either/or logic, and this epistemology underpins the semi‑core's policy and strategic stance.
Philosophical and textual analysis of contemporary Chinese Marxist thought presented in the paper, interpreted in relation to Bauman's philosophical work; no empirical measurement reported, presented as conceptual/theoretical evidence.
AI adoption significantly reshaped task profiles for 73% of respondents, particularly affecting routine data processing, administrative tasks, and scheduling activities.
Survey data and secondary data analysis reported in this study (sample size not stated); self-reported change in task profiles with reported percentage (73%).
AI adoption across firms is heterogeneous, varying across sectors such as finance, technology, and manufacturing.
Survey of 150 leading Nigerian firms across finance, tech, and manufacturing showing variation in AI integration; supported by qualitative interviews and policy analysis.
The rapid, heterogeneous integration of Artificial Intelligence (AI) technologies is profoundly reshaping the dynamics of work across the Nigerian business sector, generating both significant economic opportunities and acute labor market challenges.
Mixed-methods study combining a quantitative survey of 150 leading Nigerian firms across finance, tech, and manufacturing and qualitative analysis of government policy and workforce interviews.
As technological progress devalues labor, the welfare benefits of steering initially increase but, beyond a critical threshold, decline and optimal policy shifts toward greater redistribution.
Analytical result from the paper's theoretical model that compares planner's optimal technology choice under varying degrees of labor devaluation and redistribution costs.
For the short-run optimization problem of AI deployment given fixed job responsibilities and worker skill levels, the firm’s optimal strategy for an m-step job can be computed in time O(m^2) using dynamic programming; the long-run joint optimization including task assignment to workers can also be solved in polynomial time up to an arbitrarily small error term.
Algorithmic results and complexity analysis derived in the theoretical sections and appendices of the paper (dynamic programming construction and polynomial-time solution statements).
Appending a neighboring step to an existing AI chain adds no additional human verification burden (verification is a fixed cost at the chain level), which can make appending steps to a chain optimal even if manual execution is individually preferable for the appended step.
Theoretical model setup and formal argument showing verification is incurred only at the last augmented step of a chain; illustrative examples (data scientist workflow) and comparative-cost reasoning in the paper.
AI chaining can overturn standard comparative advantage logic in assignment: when multiple adjacent steps are executed as an AI chain, a step may be assigned to AI (as part of the chain) even if manual human execution would be preferred for that step in isolation.
Theoretical model of production as an ordered sequence of steps with firms endogenously bundling contiguous steps into tasks and jobs; formal comparative-static arguments and illustrative examples in the paper showing how fixed verification costs per chain change marginal assignment incentives.
The general public supports both targeted programs and broader interventions (including job guarantees and UBI), contrasting with economists' preferences.
Survey comparisons across groups contrasting normative policy support (textual summary in Key Findings; exact public-group percentages not provided in excerpt).
Unconditional forecasts are relatively close to historical trends, but under the rapid scenario the range of plausible outcomes expands (greater uncertainty).
Comparison of unconditional (all-things-considered) survey forecasts to conditional rapid-scenario forecasts; dispersion metrics referenced qualitatively in Key Findings (detailed variance numbers not provided in excerpt).
Poaching by a dominant undertaking can, under certain conditions, constitute exclusionary abuse and structural abuse in both product and labor markets (drawing on Section 2 Sherman Act 'predatory hiring' scholarship and case law).
Paper's analytical claim based on comparative legal scholarship and case law (described in abstract); no empirical sample/experiment specified in abstract.
An Evolutionary Game Theory (EGT) framework produces a 'Red Queen' co-evolutionary dynamic between platforms' algorithmic control and worker behavior in which neither side reaches a stable static equilibrium.
Analytical EGT model and numerical simulations of a population-level game between workers (choices: compliance vs. algorithmic gaming) and a platform varying surveillance strictness; model-based result (no empirical sample size).