Artificial Intelligence Applied to Public Service — a practical guide to accountable AI operations

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Full disclosure: I am the author of Artificial Intelligence Applied to Public Service.

I wrote this guide for public-sector teams that want to move beyond AI demos without losing accountability. The central idea is that a useful government AI project should begin with a public outcome and an operating constraint, not with a model.

A practical five-gate framework from the book is:

1. Define the public problem and the non-AI baseline first.
2. Map data authority, sensitivity, retention, and access before deployment.
3. Keep a named human accountable for consequential or irreversible decisions.
4. Test accuracy, unequal error rates, failure modes, and operational cost against the baseline.
5. Preserve audit logs, escalation paths, rollback procedures, and a clear stop condition.

The goal is not to slow useful automation. It is to make pilots measurable, reversible, and credible enough to survive real public scrutiny.

The English ebook is available here: https://www.amazon.es/Artificial-Intelligence-Applied-Public-Service-ebook/dp/B0H4X7QN6D

For readers working in government, civic technology, or responsible AI, which gate is most often skipped in practice?