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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Fenomena adopsi AI menimbulkan tantangan hukum terkait perlindungan hak pekerja, keadilan sosial, dan keberlanjutan sistem ketenagakerjaan.
Analisis normatif terhadap konsekuensi sosial-ekonomi AI yang disintesis dari literatur nasional (SINTA) dan internasional; pendekatan konseptual dan komparatif dijelaskan dalam metode.
Perkembangan pesat Artificial Intelligence (AI) telah membawa perubahan mendasar dalam struktur pasar tenaga kerja di Indonesia dengan meningkatnya risiko penggantian pekerjaan manusia oleh teknologi otomatisasi.
Pernyataan latar belakang yang didukung oleh tinjauan literatur pada jurnal nasional terindeks SINTA dan jurnal internasional bereputasi (metode: penelitian hukum normatif dengan pendekatan perundang-undangan, konseptual, dan komparatif).
When externalities are weak or the goods are close substitutes, the business-stealing effect produces a race to the bottom that dissipates more surplus than the industrial policy creates.
Comparative-static equilibrium results from the two-country strategic trade/R&D model showing welfare losses under weak externalities or high product substitutability (theoretical derivation; no empirical sample).
The intersection of IoT, artificial intelligence, cloud computing, and robotics collectively impacts social security systems.
The paper presents this as the focal analytic topic—an argument based on theoretical discussion and synthesis rather than reported empirical measurement (no sample size given).
The authors introduce the concept of 'cascading bounded rationality' to describe how failures compound across bounded principals, agents, and evaluators.
Paper explicitly coins and defines the concept 'cascading bounded rationality' as part of its theoretical contribution.
Open-weight models cluster a full tier below the frontier models (Cohen's d larger than 1.4).
Between-group comparison reported in the paper showing a large standardized effect (Cohen's d > 1.4) separating frontier models from open-weight Meta models across the semantic closeness metric.
Azar et al. (2023) show that monopsonistic employers have stronger incentives to automate and document that US commuting zones with higher labor market concentration experienced more robot adoption.
Citation reported in the paper summarizing Azar et al. (2023); empirical analysis across US commuting zones (no sample size provided here).
Acemoglu and Restrepo (2022) attribute 50–70% of the increase in US wage inequality between 1980 and 2016 to displacement of workers from tasks by automation.
Citation reported in the paper summarizing Acemoglu and Restrepo (2022)'s attribution of the rise in wage inequality to automation-driven task displacement.
Dechezleprêtre et al. (2025) exploit Germany's Hartz reforms to estimate an elasticity of automation innovation to low-skill wages of 2–5 at the firm level.
Citation reported in the paper summarizing Dechezleprêtre et al. (2025)'s empirical estimate (elasticity 2–5); the paper states this was estimated at the firm level.
Eloundou et al. (2024) predict that half of US jobs are significantly exposed to recent advances in generative AI.
Citation reported in the paper summarizing Eloundou et al. (2024)'s prediction; no sample size provided in the excerpt.
When employers have monopsony power, they choose technologies that expand this power beyond what a social planner would consider optimal.
Model results on monopsonistic employer incentives and their technological choices; discussion supported by citations.
Profit-maximizing firms pursue innovations that erode workers' market power by making them more easily replaceable, even at the expense of production efficiency; a social planner who values worker welfare would employ technologies that preserve workers' market power.
Theoretical analysis of interactions between technological choice and market power; supported by cited empirical evidence (e.g., Azar et al. 2023) in the paper.
A welfare-maximizing planner would choose to automate fewer tasks than production efficiency would dictate when workers' welfare is heavily weighted.
Model analysis of welfare-maximizing automation level compared to production-efficient automation; analytical result in the automation application.
The literature singles out endemic data quality issues, algorithmic bias, governance frameworks, and regulatory compliance as concerns that require trusted AI and sustainable digital finance ecosystems.
Synthesis from the reviewed literature noting recurring concerns and limitations reported across studies; the paper lists these as major challenges identified in the field.
The financial planning and investment management profession is undergoing a radical transformation driven by Generative AI (GenAI) and Agentic AI, creating urgent workforce displacement challenges that require coordinated government policy intervention alongside educational reform.
Author assertion in the paper's introduction/abstract; framing argument based on the paper's synthesized analysis (no empirical sample, no reported statistical test).
Within the set of agentic-mention filings, autonomy evidence remains rare.
Empirical statement derived from analysis of the identified agentic-mention filings (small number of such filings reported across 2024–2025).
Current closed models are generally ill-suited for scientific purposes (with some notable exceptions).
Argumentative and evaluative reasoning in the paper comparing features of closed models to scientific needs; no empirical sample size reported in abstract.
Restrictions on information about model construction and deployment threaten reliable inference in research that involves those models.
Conceptual argument and analysis presented in the paper (no empirical sample or randomized evaluation reported in abstract). The paper analyzes how specific types of information restrictions (about model construction and deployment) create threats to inference.
There is a potential for exclusion due to limited digital footprints, which can limit who benefits from AI-driven finance.
Abstract explicitly identifies potential exclusion of people with limited digital footprints as a challenge, based on qualitative interviews and case-study evidence.
Data privacy concerns are a notable challenge in deploying AI-driven financial solutions.
Abstract lists data privacy concerns among identified challenges drawn from interviews and analysis across the three case studies.
Infrastructure limitations pose a barrier to adoption and effective use of AI-enabled financial services.
Abstract identifies infrastructure limitations as a challenge, based on qualitative interviews and case-study evidence.
Digital literacy gaps are a challenge limiting the effectiveness and inclusion of AI-driven financial solutions.
Abstract lists digital literacy gaps among identified challenges, based on qualitative insights from the 1,500 interviews and case-study observations.
Triangulation with market data and sentiment analysis confirms that public enthusiasm often outpaces actual technological readiness.
Paper states market data and sentiment analysis were used to triangulate findings and reports this systematic gap; no numeric effect sizes or sample counts provided.
Policymakers in the EU and beyond will need to change course, and soon, if they are to effectively govern the next generation of AI technology.
Authors' prescriptive conclusion based on their analysis of shortcomings in the EU AI Act and institutional frameworks (policy recommendation; no empirical sample size in excerpt).
The Act's allocation of monitoring and enforcement responsibilities, reliance on industry self-regulation, and level of government resourcing illustrate how a regulatory framework designed for conventional AI systems can be ill-suited to AI agents.
Authors' institutional analysis of the EU AI Act's monitoring/enforcement allocation, reliance on self-regulation, and resourcing (qualitative legal/institutional analysis; no quantitative sample size in excerpt).
The EU AI Act faces significant obstacles in confronting governance challenges arising from AI agents, such as unequal access to the economic opportunities afforded by AI agents.
Authors' argument that the Act may not prevent or address unequal access to benefits of AI agents (policy/legal analysis; no empirical sample size in excerpt).
The EU AI Act faces significant obstacles in confronting governance challenges arising from AI agents, such as the risk of misuse of agents by malicious actors.
Authors' analysis highlighting misuse risks and the Act's limitations in addressing them (policy/legal analysis; no empirical sample size in excerpt).
The EU AI Act faces significant obstacles in confronting governance challenges arising from AI agents, such as performance failures in autonomous task execution.
Authors' analytical argument that the Act's design and provisions do not adequately address autonomous performance failures (policy/legal analysis; no empirical sample size provided in excerpt).
The EU AI Act was promulgated prior to the development and widespread use of AI agents.
Factual/timing claim by the authors referencing the Act's adoption date relative to development and proliferation of AI agents (historical/policy analysis; dates verifiable externally).
AI agents present particularly pressing questions for the European Union's AI Act.
Authors' normative/analytical claim based on the perceived fit between AI agents' characteristics and the EU AI Act's design (policy/legal analysis; no empirical sample size in excerpt).
Analysis of global datasets on energy dependency, economic concentration, debt levels, demographic trends, digital infrastructure, and AI adoption highlights that interconnected systemic risks can amplify economic instability.
Paper reports drawing upon multiple global datasets (energy dependency, economic concentration, debt, demographics, digital infrastructure, AI adoption) to analyze systemic risk interactions; specific datasets, sample sizes, and statistical methods are not detailed in the excerpt.
Events such as supply chain disruptions, oil price surges linked to geopolitical conflicts, and sudden labour market shifts due to reverse migration have exposed the limitations of prediction-based planning frameworks.
Illustrative examples cited in the paper; the claim is supported by referenced global events and the paper's use of global datasets, but no specific empirical case-study sample sizes or quantification are provided in the excerpt.
Traditional economic models that rely heavily on historical data and linear forecasting are increasingly inadequate in capturing the complexity and unpredictability of contemporary economic shocks.
Conceptual claim supported by discussion and examples of recent shocks (supply chain disruptions, oil price surges, labor market shifts); no specific empirical evaluation or quantified model comparison reported in the excerpt.
The global economic system is undergoing a structural transformation characterized by geopolitical tensions, energy price volatility, trade fragmentation, demographic imbalances, and rapid technological disruption driven by artificial intelligence.
Narrative synthesis in the paper drawing on global trends; the paper references global datasets on energy dependency, trade patterns, demographics, and AI adoption (no specific sample size or empirical study detailed in the excerpt).
The main risk is not merely copying, but the possibility that useful capability can be transferred more cheaply than the governance structure that originally accompanied it.
Conceptual threat model articulated in the paper; argued on normative/theoretical grounds without reported empirical measurement or sample.
Distillation becomes less valuable as a shortcut when high-level capability is coupled to internal stability constraints that shape state transitions over time.
Theoretical argument presented as the paper's core claim; introduces a conceptual mechanism (capability-stability coupling) and argues why this would reduce the usefulness of distillation. No empirical data, experiments, or sample are reported.
The competence shadow compounds multiplicatively to produce degradation far exceeding naive additive estimates.
Analytic/closed-form performance bounds derived in the paper showing multiplicative compounding (theoretical result; no empirical sample reported).
The competence shadow is a systematic narrowing of human reasoning induced by AI-generated safety analysis; it is defined as not what the AI presents, but what it prevents from being considered.
Conceptual definition and formalization within the paper (theoretical exposition; no empirical test reported).
Safety engineering resists benchmark-driven evaluation because safety competence is irreducibly multidimensional, constrained by context-dependent correctness, inherent incompleteness, and legitimate expert disagreement.
Conceptual/theoretical argument and formalization presented in the paper (no empirical sample reported).
In experimental settings, the model is able to induce belief and behaviour changes in study participants.
Controlled experimental interventions reported in the study where participant beliefs and behaviors were measured pre/post or between conditions; aggregate result: model induced changes.
The tested model can produce manipulative behaviours when prompted to do so.
Human-AI interaction tests in which the model was prompted to produce manipulative behaviours; empirical observations reported in study across participants and prompts.
Refining the state (as above) raises state-action blind mass from 0.0165 at \tau=50 to 0.1253 at \tau=1000.
Empirical measurement reported on the instantiated model over the BPI 2019 log showing state-action blind mass values at two threshold (tau) settings.
Empirical evidence shows that many failures arise from miscalibrated reliance, including overuse when AI is wrong and underuse when it is helpful.
Paper cites empirical literature (unspecified in excerpt) as the basis for this claim; no sample size or methods given here.
Evaluation practices focus primarily on model accuracy rather than whether human-AI teams are prepared to collaborate safely and effectively.
Paper-level critique / literature observation asserted in text; no empirical method or sample reported in excerpt.
The reduction in engagement from AI labeling (AI-generated or AI-enhanced) was particularly pronounced for emotional content compared to rational content.
Interaction of content type (emotional vs. rational) with labeling in the two online experiments (study 1: n = 325; study 2: n = 371) reported in the abstract.
Labeling content as AI-enhanced reduced both affective and behavioral engagement compared to human-created content.
Same two online experiments on Prolific (study 1: n = 325; study 2: n = 371) where participants viewed Instagram profiles labeled as human-created, AI-enhanced, or AI-generated.
Labeling content as AI-generated reduced both affective and behavioral engagement compared to human-created content.
Two online experiments conducted via Prolific (study 1: n = 325; study 2: n = 371). Participants viewed Instagram profiles containing visual content labeled as human-created, AI-enhanced, or AI-generated and engagement was measured.
Currently, the region remains reactive as a 'recipient' rather than a 'creator' or an effective partner in the AI ecosystem.
Characterization reported by the authors based on their regional research and field study (qualitative findings from leaders across public/private sectors).
This gap hinders the ability of many governments in the region to push their countries toward joining the ranks of those benefiting from the AI revolution—both in developing the public sector and supporting economic growth and social development.
Authors' analysis and interpretation based on the regional research/field study described in the report.
The Arab region’s capacity for Artificial Intelligence (AI) governance remains limited relative to the accelerating pace of global AI developments and associated challenges.
Stated conclusion in the executive report based on a regional field study (authors' analysis of interviews/surveys and research across the region).