Evidence (1286 claims)

Transparency (detailed documentation of data, objectives, evaluation processes, and deployment constraints; audit and contest mechanisms) is a necessary mechanism for accountable alignment.

Pluralistic evaluation—using multiple, diverse evaluation criteria and stakeholder‑informed metrics rather than single aggregated alignment scores—will better capture the values and harms at stake.

The Flourishing–Justice–Autonomy (FJA) framework should guide alignment efforts, emphasizing (1) Flourishing (human well‑being and meaningful opportunities), (2) Justice (distributional fairness and protection of vulnerable groups), and (3) Autonomy (informed choice and user control).

Research priorities include empirically quantifying AI's effects on productivity, wages, inequality, and environmental costs; developing standardized sustainability and governance metrics; and evaluating regulatory impacts on innovation and welfare.

AI has progressed from symbolic systems to data-driven, generative architectures and large-scale computational infrastructures, becoming a foundational technology across sectors.

Rigorous research priorities include randomized controlled trials with long-run follow-ups, cost-effectiveness studies, structural adoption models, and validated metrics for feedback quality and learning durability.

Historical transitions in standard work hours (e.g., six-day to five-day week) show that phased implementation, collective bargaining, and complementary policies can make work-time reductions feasible and economically beneficial.

Economists and researchers should measure organizational mediators (governance, mentoring practices, learning processes) alongside AI adoption and use empirical designs such as difference-in-differences with phased rollouts, randomized mentoring/training interventions, matched employer–employee panels, and IV exploiting exogenous shocks to innovation backing to identify causal effects.

The integrated framework links multi-level outcomes: micro (individual skills, task performance), meso (team coordination, workflows), and macro (organizational strategy, innovation, productivity) effects to adaptive structuration processes and affordance actualization.

The paper develops a conceptual framework that integrates Adaptive Structuration Theory (AST) and Affordance Actualization Theory (AAT) to explain how effective human–AI collaboration can be structured within organizations.

Returns to advanced digital skills vary by firm size/type: the wage return in large Chaebol conglomerates is approximately 18.7%, significantly higher than the ~9.5% return in Small and Medium-sized Enterprises (SMEs), indicating a 'skills–scale' complementarity effect.

Workers with only general digital literacy receive a wage premium of approximately 5.8% (after controlling for education, experience, and demographics).

Workers possessing specialized digital skills (e.g., data analysis, programming, automation control) enjoy a significant wage premium of approximately 14.2% after controlling for years of education, work experience, and demographic characteristics.

DARE posits that responsible AI deployment requires the simultaneous and integrated development of Digital readiness, Administrative governance, Resilience & ethics, and Economic equity.

This paper introduces the DARE Framework, a holistic, four-dimensional model for national AI strategy and international cooperation.

The Indian government believes that artificial intelligence (AI) will play an important role in India’s continued economic growth, both through its contribution to productivity in the private sector and through smarter and more data-led government.

Global efforts toward establishing shared norms and multilateral cooperation are underway through initiatives led by the United Nations, OECD, UNESCO, and G7.

Mainstreaming shared input and embracing climate-resilient management approaches are fundamental action items for building institutional resilience.

Medicaid, as the largest public purchaser of healthcare services in the United States, occupies a strategic position to drive systemic change through its supply chain.

The rapid growth of geospatial data and advances in artificial intelligence (AI) have driven GeoAI’s rise as a key paradigm in urban analytics.

The model was prompted to suggest jobs to 24 simulated candidate profiles balanced in terms of gender, age, experience and professional field.

This study evaluates how a state-of-the-art generative model (GPT-5) suggests occupations based on gender and work experience background for under-35-year-old Italian graduates.

Entrepreneurs' expectations about future opportunities were significantly shaped by interpersonal influence (peer effects).

Crisis adaptation among small business owners during COVID-19 was driven less by macroeconomic structure and more by social embedding (social networks, peer influence, and collective identities).

The positive AI → executive pay relationship is robust to endogeneity controls, including instrumental variable approaches, and to multiple robustness checks.

Firm-level AI adoption raises executive compensation in Chinese A-share listed companies (2007–2023).

Two regimes emerge: an inequality-increasing regime when AI is proprietary (concentrated control), rents concentrate because firms capture most gains (low ξ), and complementary assets are concentrated.

Generative AI shifts economic value toward concentrated complementary assets (firm-level capital, proprietary data/algorithms), increasing firm profits and rents captured by asset owners.

The paper identifies gaps and recommends that economists conduct randomized evaluations and quasi-experimental studies to estimate causal effects of interventions (hands-on labs, instructor training, compute subsidies) on competencies and earnings.

The study conducted a cross-sectional online survey of more than 600 higher-education students and educators from multiple world regions.

Digital trade development raises city-level house prices in China in a robust, linear manner.

Canada emphasizes teacher-led assessment, cautious regulation, and a focus on equity and professional development in responding to AI-related assessment issues.

The task frontier expands: new tasks become profitable and are created endogenously as coordination costs decline.

Aggregate output increases when coordination costs fall because reduced frictions and endogenous task creation raise productive capacity.

Lower coordination costs expand managers’ spans of control (managers can supervise more subordinates).

A one standard-deviation increase in AI adoption causally increases employment in occupations requiring complex problem-solving and interpersonal skills by 1.8%.

Overinvestment increases inequality (greater tail concentration of income).

Overinvestment increases measured GDP (output).

The exponential returns to skill and technology create strong private incentives for agents to escalate skill (education) investment toward the high tail of the distribution (an educational arms race).

When wages follow a lognormal distribution, technological progress makes wages increase exponentially in both skill and technology.

The paper proposes a research agenda prioritizing interoperable, ethical‑by‑design platforms; metrics to measure social equity impacts; and adaptation of global standards to local institutional capacities.

High‑income examples (e.g., Estonia, Singapore) demonstrate mature integration of digital/AI systems in e‑government, urban mobility, and e‑health.

Research priorities include developing robust measures of AI adoption and using causal methods (difference-in-differences, synthetic controls, RDD, IV) to estimate effects of AI and regulation on productivity, employment, and inequality.

The American Artificial Intelligence Initiative emphasizes R&D and innovation leadership, standards development, workforce readiness, and fostering 'trustworthy AI' (transparency, fairness, accountability).

The paper introduces a Predictive Skill Gap Intelligence Hub — an AI-driven platform that combines macro- and micro-level indicators with probabilistic growth models and intelligent skill-synthesis to proactively forecast regional and sectoral labor demand–supply gaps.

Vendor support, warranties, and service-level agreements (SLAs) are important for clinical adoption and liability management.

Proprietary systems lead on reliability, maintenance, and validated integrations with clinical systems.

Open-source deployment options (e.g., on-premises) reduce data-sharing exposure and improve privacy.

Open-source models provide greater transparency and inspectability, enabling better auditability and explainability.

Coordinated policy reform, targeted infrastructure investment, workforce training, and equity-focused implementation are strategic priorities to realize AI’s potential in Indonesian healthcare.