AI and its economic, financial and social impacts – international workshop

Artificial Intelligence (AI) is increasingly regarded as a transformative general-purpose technology with pervasive implications across industries, institutions, and societies.

This workshop aims to contribute to ongoing research efforts by eliciting papers addressing the inherently multi-dimensional implications of AI. It aims to bring together scholars working on theoretical, empirical, and policy-oriented contributions that may fall within the following broad themes:

• A. Measurement and identification issues in AI research
• Measuring AI adoption, penetration, and use across firms, sectors, places and value
  chains.  Development and implementation of appropriate identification strategies for firm-,
  industry- and country-level research on economic implications of AI.
• Use of AI-related journal articles for constructing task‑ or function‑specific exposure
  measures
• AI generation, heuristics of innovators, biases and reproduction of stereotypes

• B. AI and business dynamism
• Productivity, growth, and the "AI productivity paradox".
• AI investment and accelerated obsolescence in technologies and skills.
• Innovation, creative destruction and obsolescence in the AI era.
• AI and entrepreneurship.
• Market structure, market power, and competition policy in AI ecosystems.
• AI and the firm: Organisation, management practices, and business-model transformation.

• C. AI and the labour market
• AI and the labour market: Impacts on employment, wages and skills.
• AI and labour relations: Worker management, surveillance, monitoring and control.
• Algorithmic hiring and labour market sorting: Does AI improve match quality or amplify segmentation?
• Bias, fairness, and dynamic discrimination: Long-run labour market effects of biased training data.

• D. AI and finance
• Effects of AI-based credit scoring: banking on winners or allocative efficiency?
• Crowding and fragility from correlated AI models: Implications for pro-cyclicality and systemic risk
• Bias, explainability, and discrimination: Long-term credit market effects of opaque scoring models.
• AI related financial regulations and governance

• E. Political economy of AI  
• AI and political economy: The role of institutions and economic policies
• Spatial and regional dimensions of AI.
• Sustainability, energy, and the environmental costs of AI.