A comprehensive hands-on guide to building AI governance infrastructure in Python. Covers four production-ready components: a model card generator (EU AI Act Annex IV compliant), a bias detection pipeline using Fairlearn with demographic parity and disparate impact metrics, a hash-chained audit trail logger, and a confidence-threshold human-in-the-loop escalation system. Also covers datasheet generation per Gebru et al., LLM demographic perturbation testing, and a release checklist mapped to the EU AI Act and NIST AI RMF. Motivated by real-world failures including Air Canada's chatbot liability ruling, the COMPAS recidivism algorithm, and Amazon's biased recruiting tool.
Table of contents
Table of ContentsPrerequisitesWhat AI Governance Actually Means for DevelopersThe Regulatory Environment: What You Can't IgnoreHow to Build a Model Card GeneratorHow to Build a Bias Detection PipelineHow to Build an Audit Trail SystemHow to Implement Human-in-the-Loop EscalationHow to Test an LLM Application for BiasHow to Integrate Governance into Your CI/CD PipelineThe Pre-Release Governance ChecklistConclusionWhat to Explore NextSort: