When principles become policy: the institutional translation of responsible AI in national AI strategies

Abstract This study examines how Responsible AI (RAI) principles are translated as they move from ethical discourse into governance, standards, and national policy contexts. Drawing on comparative analysis of influential AI ethics guidelines, policy syntheses, and technical glossaries, the research traces how widely cited principles are reformulated across domains, using transparency and fairness as cases of how institutional translation operates in practice. It shows that while these principles originate as broad normative commitments, they become progressively institutionalized as policy categories, procedural requirements, and measurable technical properties. The comparison shows that the underlying mechanism generalizes across principles, while the resulting institutional forms remain principle-specific. Focusing on national AI strategies as a key site of institutionalization, the study analyzes how RAI principles are embedded within state agendas, regulatory approaches, and oversight mechanisms. This analysis reveals variation in how principles are prioritized and operationalized, reflecting differences in governance priorities, administrative capacity, and political context. The findings highlight that principles most amenable to standardization and measurement tend to dominate policy implementation, while more expansive ethical concepts are less visible. By mapping these shifts across institutional settings, the paper contributes to a deeper understanding of how AI ethics is reconfigured in practice and offers insights for policymakers seeking to balance technical feasibility with normative ambition in the governance of AI.