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August 22, 20250 citationsOpen Access

Algorithmic Lawmaking and the Question of Democratic Legitimacy: A Conceptual Exploration

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VFVictor Frimpong

Key Points

  • AI integration in lawmaking processes raises significant questions about democratic legitimacy, and challenges how laws are created.
  • Key findings suggest that the Triadic Legitimacy Model emphasizes transparency, human oversight, and citizen inclusion for effective governance.
  • This analysis explores the intersection of AI technology and legislative processes, enhancing efficiency but also introducing potential risks.
  • Implementing AI responsibly in governance may protect democratic principles while fostering public trust and engagement.

Abstract

The integration of artificial intelligence (AI) into legislative processes signifies a significant change in democratic governance. Various jurisdictions, including Estonia, the European Union, and pilot programs in the United States, are using AI to enhance the creation and debate of laws. While these technologies offer benefits like efficiency and data-driven insights, they also pose risks such as a lack of transparency, accountability issues, and potential erosion of democratic principles. This paper examines the role of AI in governance and introduces the Triadic Legitimacy Model (TLM) to address these concerns. The TLM suggests that the legitimacy of AI-assisted lawmaking relies on three key pillars: transparency and explainability, human oversight and accountability, and public engagement and inclusion. It extends procedural justice theories to the realm of algorithmic governance and sets a foundation for future research. The paper concludes with practical recommendations for integrating AI into legislative processes responsibly, stressing that the key to maintaining democratic integrity lies in embracing automation while ensuring transparency, accountability, and active citizen involvement.

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Cite This Study

Victor Frimpong (2025) studied this question.

synapsesocial.com/papers/68af5d75ad7bf08b1eae145ahttps://doi.org/10.20944/preprints202508.1676.v1
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