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Standards, principles and regulations that guide the ethical and safe adoption of AI in organizations.
Every time an organization deploys an AI system, concrete questions come up: How do we manage risk? What personal data can we use? Who is accountable for algorithmic bias? What legal obligations apply in our jurisdiction? The international and local frameworks collected here offer practical, verifiable answers to those questions.
The selection ranges from voluntary ethical principles to certifiable standards and binding regulations. Adopting one or more of these frameworks reduces legal and reputational risk, makes audits easier and builds trust with customers, citizens and strategic partners.
UNESCO's RAM and the IDB self-assessment measure maturity, whether of a country or of a specific solution, and bring the gaps to light.
Singapore's framework, fAIr LAC in a box and the CAF guide turn principles into roles, processes and tools you can put to work.
NIST's AI RMF and its playbook set out how to govern, map, measure and manage the risks of an AI system. Downloadable as a checklist.
The World Economic Forum guidelines address the moment a company buys AI instead of building it: bias, data and governance.
Direct access to the official text of each framework and to the website of the organization that publishes it.
We welcome suggestions of AI standards, principles and regulations with global, regional or national scope. If you work in regulation, compliance or technology governance, get in touch and we will consider it for the library.