[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzc1OTM","title","Engineering Trustworthy AI: Accuracy Is Not Enough","state","published","contactAuthorMethod","email","aboutText","

In the race to deploy artificial intelligence, we have become obsessed with the \"Confusion Matrix.\" We celebrate 99% accuracy scores while ignoring the reality that models are brittle, probabilistic, and prone to \"Adversarial Decay.\" In high-stakes industries, from Fintech to Healthcare, a 1% error isn't just a statistic; it’s a systemic failure that can freeze capital, leak private data, or ruin a brand’s reputation overnight.

Engineering Trustworthy AI is the definitive playbook for moving beyond the lab and into the real world. Written for Principal Engineers, CTOs, and Risk Officers, this book introduces the Trust Stack: a three-tiered framework of Safety, Security, and Sovereignty. Inside, you will discover:

Why Accuracy is a Vanity Metric: How to bridge the \"Reliability Gap\" between test scores and production reality.
Defensive Engineering: Technical deep dives into hardening models against prompt injection, data poisoning, and model inversion.
The Architecture of Integrity: How to implement \"Fairness Circuit Breakers\" and \"Red-Button\" kill switches for autonomous agents.
Governance that Scales: Moving from toothless ethics checklists to automated CI/CD compliance with the NIST AI RMF and EU AI Act.

Stop building AI you can merely demonstrate. Start building AI you can trust.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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