A Policy Analysis of the Danish National AI Strategy: Ethical and Governance Implications for AI Ecosystems

The Danish National AI Strategy presents a structured approach to building an ethical and innovative AI ecosystem. It emphasizes four main pillars: ethical AI development, public data utilization, skills development, and strategic technology investment. The strategy has achieved notable success, especially in the education sector, where ethical principles like fairness, transparency, and accountability are well-integrated. However, issues such as algorithmic bias and fairness remain, indicating the need for ongoing refinement of ethical frameworks. Public data plays a central role in AI innovation, particularly in healthcare and education. Yet, challenges related to data privacy and access continue to pose obstacles, highlighting the importance of robust data governance. Skills development programs have helped prepare the workforce for AI-related roles, though limited employer participation, especially among small businesses, suggests the need for more inclusive outreach. Furthermore, while government and private funding have supported advanced AI research, the transition from innovation to practical application still faces gaps. This study employed a qualitative descriptive approach, utilizing document analysis and thematic analysis based on data from government publications and expert interviews with 20 stakeholders, including policymakers and AI specialists. The findings provide valuable insights into Denmark’s AI journey and serve as a reference for other countries aiming to implement responsible, inclusive, and sustainable AI strategies.

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Publication Details

Journal
International Journal of Artificial Intelligence
Published
2025-06-28
DOI
https://doi.org/10.36079/lamintang.ijai-01201.802
Primary Topic
Innovation, Sustainability, Human-Machine Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

A Policy Analysis of the Danish National AI Strategy: Ethical and Governance Implications for AI Ecosystems

L Hageman Pinborg, H. B. Lauritsen, Charlotta Pisinger, David Hestbjerg
International Journal of Artificial Intelligence
Innovation, Sustainability, Human-Machine Systems
article

A Policy Analysis of the Danish National AI Strategy: Ethical and Governance Implications for AI Ecosystems

L Hageman Pinborg, H. B. Lauritsen, Charlotta Pisinger, David Hestbjerg
article en

Abstract

The Danish National AI Strategy presents a structured approach to building an ethical and innovative AI ecosystem. It emphasizes four main pillars: ethical AI development, public data utilization, skills development, and strategic technology investment. The strategy has achieved notable success, especially in the education sector, where ethical principles like fairness, transparency, and accountability are well-integrated. However, issues such as algorithmic bias and fairness remain, indicating the need for ongoing refinement of ethical frameworks. Public data plays a central role in AI innovation, particularly in healthcare and education. Yet, challenges related to data privacy and access continue to pose obstacles, highlighting the importance of robust data governance. Skills development programs have helped prepare the workforce for AI-related roles, though limited employer participation, especially among small businesses, suggests the need for more inclusive outreach. Furthermore, while government and private funding have supported advanced AI research, the transition from innovation to practical application still faces gaps. This study employed a qualitative descriptive approach, utilizing document analysis and thematic analysis based on data from government publications and expert interviews with 20 stakeholders, including policymakers and AI specialists. The findings provide valuable insights into Denmark’s AI journey and serve as a reference for other countries aiming to implement responsible, inclusive, and sustainable AI strategies.

International Journal of Artificial IntelligenceVol. 12(1)
Openalex Percentile: Top 80%
Innovation, Sustainability, Human-Machine Systems
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