AI governance.
AI viewed through decisions, risks and human responsibility.
A way of looking.
This is how I read artificial intelligence when it touches real decisions. The full treatment, with tools and a dossier, is on the AI page.
Entries in the corpus.
The speed of AI and the pace of governance
AI systems can change more frequently than the bodies that govern them. Between one review and the next, the inventory, model or context of use may have changed. Governing AI requires accountable people, traceability and review triggers between meetings.
The state of algorithmic governance in Latin America
Artificial intelligence models in production are updated every few weeks; the human committees that should govern them deliberate quarterly. The asymmetry is measurable: public provider release notes versus committee minutes. The report maps the field and compares the three frameworks in which that asymmetry becomes governable — ISO/IEC 42001, EU AI Act, NIST AI RMF.
AI auditing and governance in Argentina and Latin America
Saying AI is responsible costs nothing; demonstrating it does. For boards, public bodies, universities and technology providers, the question is no longer whether to adopt it: it is what written evidence proves that each system has an owner, an authorised purpose, human oversight and the capacity for correction before causing harm. This report turns that question into a dossier of ten questions a board can demand answers to this week.
Do you have a case that can be read through this criterion?
If this topic intersects with your work — a decision, a source, a question — write to me and tell me the context.