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Recife's AI Policy and City Strategy (Subnational: Recife)


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Added by:   OECD analyst
Added on:   30 Oct 2026
Updated by:   OECD analyst
Updated on:   30 Oct 2026

Recife’s AI Initiative is a city-led programme that guides how artificial intelligence is used in public services to better serve citizens, businesses, and public institutions. It was created to improve service quality, increase transparency, and ensure responsible use of AI. The initiative sets clear rules and practical tools so innovation can grow while protecting people’s rights and public trust.

Name in original language

Política Municipal de IA e Estratégia Municipal de IA

Initiative overview

Recife’s AI Initiative was created to address a clear challenge: public institutions were under growing pressure to deliver faster, more personalised, and more transparent services, while lacking structured guidance on how to use artificial intelligence responsibly. At the same time, the city recognised AI as an opportunity to improve decision-making, expand access to services, and strengthen innovation across government, academia, and the private sector. The initiative seeks to balance innovation with safeguards, ensuring that technology improves people’s lives without compromising rights or public trust.

The core objectives are threefold. 

  • First, to establish clear governance rules for the ethical and transparent use of AI in public services, including risk assessment, data protection, and human oversight. 
  • Second, to build institutional capacity by providing practical tools, shared infrastructure, and training for civil servants. 
  • Third, to stimulate an ecosystem approach, encouraging collaboration among public institutions, universities, start-ups, and civil society so that AI solutions can address real social and economic needs.

Implementation combines regulation and experimentation. The initiative is anchored in a municipal AI decree that defines principles and responsibilities, while also promoting pilot projects in areas such as digital public services, data-driven policy, and citizen support systems. It is supported by governance structures for data management, monitoring mechanisms for algorithmic risks, and partnerships with local and international organisations. Over time, these elements are being integrated into standard administrative processes, making AI governance part of routine public management rather than an isolated programme.

Since its inception, the initiative has evolved from isolated digital transformation efforts into a structured policy framework for responsible AI adoption. Future steps include expanding cross-sector partnerships, refining risk evaluation methods, and scaling successful pilots into permanent services. The long-term vision is to position Recife as a reference for practical, citizen-centred AI in government, while creating a model that can be adapted by other cities facing similar challenges.

Other relevant details

Results, outcomes and impacts: The initiative has already delivered tangible results through open innovation programmes that generated AI-based solutions and formal public contracts. It strengthened partnerships with universities and the private sector, leading to joint research projects and pilot deployments. Impacts are measured by the number of challenges launched, solutions contracted, partnerships signed, and investments attracted. The city has expanded its innovation network, attracted new talent, and increased technology-driven investments in the territory. Challenges and lessons learned: Key challenges included limited institutional capacity, uncertainty about legal boundaries, data quality gaps, and resistance to change within public administration. There were also concerns about privacy, bias, and overreliance on automated systems. These risks were addressed through clear governance rules, human oversight requirements, training programmes for civil servants, and structured risk assessment processes before deployment. A major lesson learned is that AI adoption must combine regulation, experimentation, and capacity-building. Political leadership, cross-sector partnerships, reliable data infrastructure, and public trust are essential conditions for long-term success.