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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The compliance layer can also create a stable approval boundary that political successors learn to navigate while preserving the appearance of lawful administration.
Stated conclusion/insight from the paper's formal argument and conceptual framing (theoretical, no empirical sample).
Under our definition, contestants with types below certain threshold (low types) always engage in benchmark hacking, whereas those above the threshold do not.
Theoretical result (characterization/theorem) derived from the contest model showing threshold behavior in equilibrium across contestant types.
A vulnerability class is characterised for expected-utility maximisers that makes them susceptible to adversarial gambles.
Formal characterization/definition and analytical derivation in the paper describing which expected-utility maximisers are vulnerable to adversarial (Pascal-type) offers; theoretical examples provided rather than empirical tests.
Ungoverned coupling between humans and AI can produce fragility, lock-in, polarization, and domination basins.
Theoretical/modeling analysis showing destabilizing dynamics and multiple basins of attraction when governance regularization is absent or weak; no empirical sample.
Classical robot ethics framed around obedience (e.g. Asimov's laws) is too narrow for contemporary AI systems.
Literature synthesis and conceptual argument drawing on developments in adaptive, generative, embodied, and embedded AI; no empirical sample reported.
The regulatory architecture is in place; the verification instrument is not.
Paper's high-level diagnosis asserting that regulations establish obligations but lack a technical instrument for quantitative verification of acceptable risk.
The systems most in need of oversight are opaque statistical inference engines that resist white-box scrutiny.
Paper's characterization/analysis of contemporary high-risk AI systems as opaque statistical models that are difficult to inspect via white-box methods.
This gap is not theoretical: as the EU AI Act moves into full enforcement, developers face mandatory conformity assessments without established methodologies for producing quantitative safety evidence.
Argument in paper linking imminent enforcement of EU AI Act to practical conformity-assessment requirements for developers and asserting lack of established methodologies for quantitative safety evidence.
None [of these regulatory frameworks] specifies what 'acceptable risk' means in quantitative terms, and none provides a technical method for verifying that a deployed system actually meets such a threshold.
Paper's critical analysis of existing regulatory instruments, arguing absence of quantitative definitions and verification methods.
Ethical concerns—such as transparency, explainability, psychological effects, and responsible AI governance—are critical factors influencing employability outcomes.
Review synthesis highlighting ethical issues from empirical and industry literature as influential on employability outcomes.
There are significant AI adoption challenges in education and industry that affect employability and role transformation.
Synthesized evidence from industry reports and empirical studies discussed in the review highlighting barriers to adoption in education and industry.
Technological interdependence is not dissolving but being selectively restructured, producing a durable condition of partial, segmented decoupling in which interdependence persists under increasingly politicized rules of access.
Interpretation based on case-study observations of export controls, allied coordination, Chinese countermeasures, and emergent supply-chain and regulatory changes described in the paper.
When the United States employs export controls and allied coordination to manage perceived technological risks, China responds through defensive reconfiguration aimed at reducing asymmetric vulnerability, in addition to retaliation in rare-earth export controls in certain instances.
Case-study evidence centered on advanced-technology sectors (particularly semiconductors) and observed policy responses following U.S. export restraints after the first Trump administration (qualitative policy and reaction examples described in the paper).
From the perspectives of 'personal subordination' and 'economic subordination', AIGC deeply and implicitly controls the labor process through mechanisms such as dynamic path planning, blurring the boundaries of determination.
Analytical/legal argument in the paper linking conceptual standards of subordination to specific algorithmic mechanisms (e.g., dynamic path planning); supported by mechanistic discussion but no reported empirical measurement or sample.
AIGC constantly challenges traditional standards for determining labor relations.
Paper's analytic claim based on conceptual/legal argument that algorithmic features of AIGC complicate application of existing labor-relation tests; no quantitative validation or sample size provided.
The transformation toward algorithmic enterprises raises critical concerns regarding agency, accountability, data monopolization, and algorithmic bias.
Presented as a principal concern in the paper's conceptual discussion and interdisciplinary critique; based on analysis of governance and ethical literature rather than new empirical evidence in the abstract.
Algorithmic management and monitoring have reduced employees’ autonomy and perceived work meaningfulness, contributing to 'AI anxiety' characterised by concerns about job loss, skill obsolescence, and diminished control.
Qualitative studies, survey evidence, and theoretical literature reviewed that document impacts of algorithmic management on autonomy, meaningfulness, and worker anxiety (mixed-methods literature).
Automation has intensified income inequality between high-skilled and low-skilled workers.
Synthesis of empirical literature linking automation adoption to widening wage and income gaps across skill groups (literature review).
Displacement effects have extended from manufacturing into cognitive roles such as clerical work and customer service.
Review of empirical studies documenting automation/substitution effects in cognitive, clerical, and customer-service roles (literature synthesis).
Automation has put downward pressure on wages.
Cited empirical studies and wage analyses in the reviewed literature indicating wage suppression associated with automation adoption (literature review).
AI and robotics have led to contractions in low-skilled occupations.
Synthesis of empirical literature reporting occupational contractions in low-skilled jobs following automation adoption (literature review).
Extensive empirical evidence shows that AI and robotics can substitute for rule-based, codifiable routine tasks.
Review cites extensive empirical studies demonstrating substitution of rule-based, codifiable routine tasks by AI/robotics (literature synthesis).
Artificial intelligence and robotic technologies are fundamentally reshaping labour markets and pose multifaceted challenges to workers engaged in routine and low-skilled tasks.
Narrative review of domestic and international scholarly literature over the past decade (literature review / synthesis).
In post-AGI economies the presupposition of agent autonomy becomes nontrivial because artificial systems may exhibit varying degrees of autonomy, functioning as tools, delegates, strategic market actors, manipulators of choice environments, or possible welfare subjects.
Theoretical argumentation and conceptual classification in the paper; no empirical data reported (modeling/motivating discussion).
Market incompleteness distorts the efficient development of AI (i.e., distorts innovation/output).
Claim made in the abstract as a theoretical implication of the asset-pricing model; no empirical data provided.
Market incompleteness distorts valuations.
Stated in the abstract as an implication of the model (theoretical analysis); no empirical quantification provided.
There is a stark geopolitical divide between 'AI Core' nations and the Global South; the Global South faces acute risks of 'Digital Dependency' and eroded digital sovereignty.
Cross-study synthesis in the systematic review (2018-2026) identifying geopolitical patterns and risks; abstract does not quantify the number of studies or present empirical effect sizes.
The 'black box' nature of automated systems undermines the democratic social contract and principles of procedural justice, epitomised by the Australian 'Robo-debt' scandal.
Case study material and literature synthesized in the systematic review referencing the Australian Robo-debt case as an exemplar; abstract does not provide primary data or sample sizes.
Agentic AI introduces novel challenges related to market stability, regulatory compliance, interpretability, and systemic risk.
Survey discussion synthesizing literature on systemic and governance risks of autonomous systems in markets; draws on conceptual and empirical prior work but does not present new quantitative results.
Consolidation of corporate control of critical technologies (driven by AI industrial strategies that do not center democratic economic governance) threatens key democratic and societal objectives.
Stated implication in the paper's opening argument; supported by the paper's conceptual framing and (as indicated) review of how past and emerging tech/AI industrial strategies interact with democratic objectives. No quantitative sample size provided in the excerpt.
Unless governments develop industrial policy strategies centered on strengthening democratic economic governance, they risk consolidating corporate control of critical technologies.
Main argumentative claim of the paper as stated in the abstract/introduction; presented as a normative risk argument supported in the paper by conceptual analysis and review of policy trends and historical examples (no empirical sample size reported in the excerpt).
Under-represented groups tend to be systematically under-observed because of historical exclusion and selective feedback, which exacerbates uncertainty for those groups.
Conceptual claim supported by illustrative examples (e.g., lending context) and simulations demonstrating selective feedback effects; literature citation likely included in paper.
Policies that ignore the unobserved (counterfactual) space can harm decision makers (via unrealized gains or losses) and subjects (via compounding exclusion and reduced access).
Theoretical argumentation and illustrative examples (e.g., loan denial counterfactuals) and modelled simulations showing downstream harms when ignoring unobserved outcomes.
Experiments on simulated data with varying bias show that unequal uncertainty and selective feedback produce disparities across groups.
Simulation experiments described in the paper manipulate bias and feedback patterns and report resulting group disparities (synthetic datasets; experiment details in methods/results sections).
Industrial firms face a dual challenge: (1) the development and deployment of digital technologies and (2) the proliferation and integration of the corresponding skills portfolios.
Conceptual framing and literature synthesis presented in the paper (identification by authors); not tied to a specific quantitative sample in the provided text.
Renewable energy adoption further reinforces the beneficial effect of digital trade on emissions under stronger regulatory stringency (mediation via renewable energy and regulation).
Structural equation modelling (SEM) on the monthly panel (38 OECD economies, 2000–2024) assessing mediation paths through renewable energy adoption and regulatory stringency; reported as reinforcing the digital trade effect.
There is a carbon-pricing threshold at USD 40 per tonne, above which emissions decline significantly (Δ = −15%, p < 0.01).
Carbon-pricing threshold analysis applied to the monthly panel of 38 OECD economies (2000–2024); threshold identified and associated pre/post comparison reports a 15% decline with p < 0.01.
The environmental effect of digital trade becomes stronger (more negative on emissions) when combined with AI-enhanced logistics (interaction effect).
Econometric models including interaction terms for AI-enhanced logistics and digital trade on the monthly panel (38 OECD economies, 2000–2024); interaction effects identified via regression and machine-learning threshold techniques.
GVC participation is significantly associated with lower CO2 emissions (β = −0.064, p < 0.01).
Econometric analysis on a monthly panel of 38 OECD economies from 2000–2024 using fixed-effects models; coefficient and p-value reported in paper.
LLMs are not only less accurate on ideologically contested economic questions, but systematically less reliable in one ideological direction than the other, underscoring the need for direction-aware evaluation in high-stakes economic and policy settings.
Synthesis of empirical findings: lower accuracy on contested items, higher accuracy for intervention-aligned cases in 18/20 models, and error skew toward intervention-oriented predictions; policy recommendation follows from these empirical patterns.
This directional skew is not eliminated by one-shot in-context prompting.
Intervention of one-shot in-context prompting applied to models; evaluation shows the intervention-oriented error skew persists despite one-shot prompting.
Ideology-contested items are consistently harder than non-contested ones.
Comparison of model performance (accuracy) on contested subset (1,056 items) versus non-contested items in the 10,490-triplet benchmark; reported consistent lower accuracy on contested items.
A threat model taxonomy mapping misuse vectors to hardware, software, institutional, and liability layers illustrates why no single governance mechanism suffices.
Threat model taxonomy developed in the paper (conceptual taxonomy; illustrative mapping rather than empirical testing).
Restricting access to open-weight models deepens asymmetries while driving proliferation into unsupervised settings.
Argumentation and threat-model reasoning in the paper describing likely consequences of restrictions (theoretical analysis; no empirical sample cited).
Access restrictions, without governed alternatives, may displace risks rather than reduce them.
Theoretical argument and threat-model analysis in the paper showing possible risk displacement (conceptual reasoning; no empirical sample reported).
Beyond technical barriers there are organizational ones: a persistent AI literacy gap, cultural heterogeneity, and governance structures that have not yet caught up with agentic capabilities.
Interview data (over 30) reporting organizational challenges including limited AI literacy, diverse cultural attitudes across organizations, and lagging governance relative to agentic AI capabilities.
Adoption is constrained less by model capability than by fragmented and machine-unfriendly data, stringent security and regulatory requirements, and limited API-accessible legacy toolchains.
Stakeholder interviews (over 30) reporting barriers to deployment; qualitative synthesis identifies data fragmentation, security/regulatory requirements, and legacy toolchain access as primary constraints.
Providing agents feedback about past performance makes them worse at information aggregation and reduces their profits.
Experimental condition where agents received feedback about past performance; compared aggregation (log error of last price) and profits with and without feedback and found worse aggregation and lower profits when feedback was given.
Increasing the complexity of the information structure has a significant and negative impact on information aggregation, suggesting AI agents may suffer from the same limitations as humans when reasoning about others.
Experimental manipulation of information-structure complexity in the controlled trading experiment; measured change in aggregation performance (log error of last price) as complexity increases.
The value alignment problem for artificial intelligence (AI) is often framed as a purely technical or normative challenge, sometimes focused on hypothetical future systems.
Author's literature-based observation and critique in the paper's introduction (conceptual argument; no empirical sample reported).