Evidence (6869 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Governance
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Employers that understand their largeness may act strategically when hiring and setting wages, generating misallocation and harming workers.
Theoretical argument made by the authors; no micro-econometric estimates, experiments, or sample descriptions are provided in the excerpt to substantiate degree or prevalence of strategic behavior.
This micro approach is at odds with the reality of labor markets in which monopsony potentially matters most.
Interpretive claim by the authors contrasting model assumptions with observed market structure; no empirical data, sample size, or specific markets cited in the excerpt.
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).
Digital intelligence significantly reduces carbon dioxide emissions.
Empirical results from the paper using panel VAR and DID analyses on the three-country sample; specific effect sizes, statistical significance levels, and time period not provided in the summary.
E-commerce has significant environmental impacts due to its large carbon footprint.
Background/literature motivation stated in the paper (qualitative claim); no specific sample size or quantitative estimate provided in the summary.
Discussions among faculty on major higher-education subreddits enact negotiations over surveillance regimes, accountability structures, and academic precarity in real time.
Interpretive finding from thematic analysis of Reddit threads: posts and replies about AI-related classroom issues (e.g., cheating, assessment, policy) show active contention over surveillance and accountability practices and concerns about job security/precariat conditions. (Specific thread counts, timestamps, and coder reliability are not provided in the excerpt.)
Findings reveal that discussions of student cheating, AI policies, writing practices, and faculty labor are not merely technical debates but sites where surveillance regimes, accountability structures, and academic precarity are negotiated in real time.
Empirical claim based on thematic content analysis of Reddit discussions that flagged threads about student cheating, AI policy, writing practices, and faculty labor and interpreted them as spaces where concerns about surveillance, accountability, and precarity are articulated and contested. (Specific examples, counts, and illustrative quotes not included in the excerpt.)
AI intensifies asymmetries of power and creates 'algorithmic hierarchies' that reinforce digital dependence, especially in the Global South.
Analytic finding derived from document review and comparative analysis; no quantitative measures or empirical case sample reported in the text to substantiate scale or prevalence.
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.
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.
AI integration into resort-to-force decision-making organizations raises important concerns.
Conceptual claim discussed by the author; the paper does not present empirical data, incident analyses, or quantified risk assessments supporting this claim within the provided excerpt.
Governing the complexity introduced by military AI integration is urgent but currently lacks clear precedents.
Authorative claim grounded in argumentation and review-style reasoning; no systematic review or empirical mapping of precedents is provided in the text.
We can expect increased organizational complexity in military decision-making institutions as AI proliferates.
Theoretical inference presented by the author; no empirical methods or measurements (e.g., complexity metrics, case studies, or sample sizes) are reported.
Current research in this area has a primary focus on methodology and computer science rather than applied occupational health questions.
Authors' synthesis from the review of existing studies (the paper reports that reviewed studies emphasize methodological and computer science aspects; exact counts or proportions not provided in the excerpt).
The application of machine learning in occupational mental health research remains in its preliminary stages.
Claim stated by the paper based on the authors' literature review of the field (review methodology referenced in the paper; number of studies or specific inclusion criteria not provided in the provided excerpt).
The shadow digital economy poses risks to national security.
Argumentative discussion and reviewed examples linking SDE activities to national security risks (method: conceptual/legal/institutional analysis; no national-security incident count or quantified risk assessment provided).
SDE activity extends beyond direct financial loss, eroding consumer trust and damaging brand reputation through data breaches, fraud, and counterfeiting.
Claim is supported by literature review and illustrative examples/case discussions in the paper (methods: qualitative synthesis; no aggregated empirical measurement of trust or reputational loss reported).
Institutional traps that sustain shadow employment exist and the SDE perpetuates informal and illicit labor arrangements.
Analytic argument and institutional analysis presented in the paper identifying mechanisms ('institutional traps'); evidence appears to be conceptual and drawn from reviewed literature and examples rather than stated empirical longitudinal data.
The shadow digital economy (SDE) is a growing phenomenon amid digital transformation and rising information costs.
Framing and literature review presented in the paper; descriptive synthesis of prior definitions and trends (no empirical sample size reported).
Many core university functions can now be achieved through AI-powered alternatives, potentially rendering conventional models obsolete for many learners.
Analytical assessment by the authors, without reported empirical testing or quantified methodology; based on review of AI capabilities and extrapolation.
Universities' core value proposition is challenged and potentially displaced by AI technologies as they alter how knowledge is accessed, created, and validated.
Authors' analytical argument drawing on technological, economic, and social drivers; presented as synthesis rather than empirical proof (no sample size or empirical method reported).
Technology companies, service providers, and civil society share responsibility for protecting children online, but current measures by these actors are insufficient.
Argument in the book summary based on evaluation of stakeholder roles; likely supported by case studies or policy analysis in the full text, but no specific methods, cases, or sample sizes are provided in the excerpt.
Current regulations fall short in effectively protecting children in an evolving digital landscape; there are persistent gaps and a growing need for internationally coordinated approaches.
Conclusion presented in the book's comparative legal analysis; implies review of EU (and US) legal frameworks and identification of gaps, but the excerpt does not list the analytical method, jurisdictions reviewed in detail, or specific legal provisions examined.
Europe has emerged as a major hub for hosting child sexual abuse material (CSAM), including newer forms such as deepfake abuse content and AI-generated 'DeepNudes.'
Asserted in the summary; would be supported by law-enforcement takedown data, hosting statistics, or forensic analyses of seized material, but the excerpt provides no specific datasets, agencies, or sample sizes.
Violations of privacy, exposure to disturbing content, unwanted sexual approaches, and cyberbullying are becoming more common.
Trend claim made in the book summary; would be supported by longitudinal or comparative prevalence data on online harms, but no specific studies, methods, or sample sizes are cited in the provided text.
Nearly one in three reports feeling unsafe.
Specific prevalence statement included in the summary; implies self-report survey data on perceived safety among youth, but the excerpt does not identify the survey instrument, population, timeframe, or sample size.
Psychological barriers — specifically algorithm aversion, AI-induced job insecurity, technostress, and diminished occupational identity — impede effective AI integration across U.S. industries.
Literature synthesis of empirical and theoretical work in AI–HRM and organizational psychology cited in the paper (summary does not report primary-study sample sizes).
Workforce psychological readiness, rather than technological capability alone, constitutes the critical bottleneck in organizational AI adoption.
Synthesis of emerging empirical AI–HRM research and theoretical integration (paper reports 'findings' from this synthesis; no primary-sample-size details provided in the summary).
The integration of AI into U.S. workplaces represents a profound organizational psychology challenge that extends well beyond mere technology adoption.
Conceptual/theoretical argument based on literature synthesis; draws on established theories (Technology Acceptance Model, Human–AI Symbiosis Theory, Job Demands–Resources Model, Organizational Trust Theory) and cited empirical AI–HRM studies (no specific sample sizes or primary data reported in the summary).
What remains needed is rigorous advice to policymakers concerned about rapid increases in labor churn, scientific development, labor–capital shifts, or existential risk.
Normative conclusion drawn by the author from gaps identified in the seven-book review (qualitative assessment of unmet policy-relevant analysis); sample = 7 books.
The reviewed works offer little guidance regarding the transformative scenarios considered plausible by many AI researchers.
Author's evaluative judgment based on the content and emphases of the seven books (qualitative gap analysis); sample = 7 books.
There are significant implementation challenges for Material Passports, particularly for existing buildings.
Aggregate findings from included studies highlighting technical, data-collection, legacy-information, and workflow barriers when applying MPs to existing building stock.
Circular economy (CE) adoption in the Architecture, Engineering, and Construction (AEC) industry is hampered by data scarcity.
Synthesis of included literature and authors' framing in the introduction and analysis sections indicating repeated identification of data scarcity as a barrier to CE adoption in AEC.
Selection of a human-LLM archetype brings important risks and considerations for the designers of human-AI decision-making systems.
Analytic discussion and synthesis of evaluation results and literature review; tradeoffs surfaced in the paper (e.g., decision control, social hierarchies, cognitive forcing strategies, information requirements).
The stability and patience that define long-term investors can breed strategic inertia.
Introductory assertion in the paper (conceptual observation). The paper does not present empirical data or sample analysis to substantiate this causal claim in the provided excerpt.
Conventional thinking often frames AI uncritically as just a tool for efficiency, which is a narrow perspective that overlooks AI's transformative role.
Critical/theoretical argument presented in the paper (conceptual observation). No empirical data, sample, or statistical analysis reported to support this claim.
Across survey and experimental evidence, perceptions that AI will replace labor—regardless of actual labor-market outcomes—may decrease democratic legitimacy and public engagement in shaping AI's future.
Synthesis of correlational findings from the large European survey (N = 37,079) and causal evidence from two preregistered experiments (UK N = 1,202; US N = 1,200).
Controlling for technology-related, political, and sociodemographic factors, perceiving AI as labor-replacing (vs. labor-creating) is associated with lower political engagement with technology.
Multivariable regression analyses on the large European survey (N = 37,079) with controls for technology-related, political, and sociodemographic factors.
Controlling for technology-related, political, and sociodemographic factors, perceiving AI as labor-replacing (vs. labor-creating) is associated with lower satisfaction with democracy.
Multivariable regression analyses on the same large survey (N = 37,079) including controls for technology-related attitudes, political variables, and sociodemographic covariates.
There are ethical concerns surrounding AI and automation including algorithmic decision-making, workforce exclusion, and inequality in access to reskilling opportunities.
Raised as an ethical analysis within the paper's conceptual framework; no empirical study, surveys, or quantified measures of these ethical issues are reported in this paper.
AI is eliminating repeated (routine) jobs.
Stated as part of the paper's argument about AI's dual impact; supported by conceptual analysis rather than new empirical evidence in this manuscript (no sample size or empirical method reported).
Artificial intelligence and automation are reshaping jobs, transforming them from a steady source of income to a dynamic process highly influenced by technology, flexibility, and uncertainty.
Central analytical claim made in the paper based on conceptual reasoning; the paper does not report empirical measures, datasets, or sample sizes to support the transformation quantitatively.
AI and automation pose significant challenges to employment stability, skill relevance, and human dignity.
Claim presented within the paper's conceptual and analytical discussion of AI's dual impacts; no empirical study, sample size, or quantitative measures provided in this paper.
Jurisdictions that implemented employee classification requirements experienced an 18% reduction in platform labor supply.
Comparative policy analysis across jurisdictions within the 24-country dataset comparing platform labor supply before and after employee-classification reforms using administrative and platform transaction records.
Median gig-worker hourly pay ($14.20) is approximately 22% below comparable traditional employment wages.
Comparison of adjusted median hourly gig earnings (platform records) to comparable hourly wages in traditional employment from labor force and administrative wage data for the same populations across the 24 countries.