Operationalizing AI Ethics in the Public Sector: A Cross-Context Replication in Brazil

 Background: AI ethics encompasses principles such as privacy, fairness, transparency, accountability, and safety that guide the responsible design and use of AI systems. Responsible AI (RAI) translates these principles into actionable practices across the AI lifecycle. In Brazil, the General Data Protection Law (LGPD) provides a privacy baseline, but there is no national AI ethics framework; meanwhile, Generative AI (GenAI) introduces new socio-technical and governance risks.  Objective: This study replicates and extends  the research by Pant et al. [24] to examine how Brazilian public organizations perceive, interpret, and implement AI ethics. It aims to identify awareness levels, institutional governance maturity, and capability gaps in the context of GenAI adoption. Method. A mixed-method, cross-sectional survey was conducted with 87 civil servants from federal, state, and municipal agencies. The questionnaire adapted Pant et al.’s instrument to the Brazilian context, incorporating LGPD references and GenAI-specific items. Quantitative data were analyzed descriptively, while open-ended responses underwent qualitative content analysis with open coding and constant comparison. Results. Awareness of AI ethics is moderate and concentrated on compliance-oriented principles, whereas participatory dimensions such as Fairness and Contestability remain limited. Governance maturity is low: only 18.6% of organizations have dedicated ethics roles or committees, and over half report never conducting training. Perceived GenAI risks are high across ethics, privacy, and data protection. The main barriers include a lack of AI knowledge, the absence of AI-specific regulation for privacy and data protection, and limited tools to apply LGPD principles. A comparison with Pant et al. shows that, while both studies identify an awareness–practice gap, its cause in Brazil lies primarily in institutional governance immaturity rather than practitioner capability. Conclusion. Brazilian public organizations demonstrate growing recognition of AI ethics but face structural barriers to operationalization. Advancing toward Responsible and Trustworthy AI requires institutional scaffolding—ethics roles, policies, and training programs—alongside regulatory clarification to align GenAI governance with LGPD principles. The study contributes cross-context empirical evidence on AI ethics governance in the Global South and outlines practical levers for embedding ethics-by-design in public-sector AI initiatives.