AI Governance Specialist: An Intermediate's Guide to Documentation That Has to Survive an Audit Nobody Is Coming to Run — Ebook (EPUB + PDF)

You already know the pitch, because you've probably said it in an interview: an AI Governance Specialist translates regulatory obligation into something an engineer can build, and buildable behavior into something a lawyer or a board member can trust. What that pitch leaves out is the actual week — an intake gate that has to classify a system nobody built a playbook for, two fairness metrics disagreeing with each other on the same model, a vendor packet scored against a weighted framework by Friday, and the plain fact that for the overwhelming majority of high-risk systems under the EU AI Act, no outside auditor is ever coming to check your work. Conformity assessment is an internal self-assessment against your own documentation. You're the last line, which means you're effectively the whole line.

This book is for the reader who already has the mission statement and needs the mechanics: the named toolkit a hiring manager actually screens for, the four-part documentation pack that has to stay current or start accruing per-day penalties, and the judgment calls — which fairness metric governs, when to escalate instead of closing a call alone, whether to trust a vendor's attestation or re-run the check yourself — that separate a governance function with real authority from a committee that's never once said no.

Inside

• The named toolkit hiring managers actually screen for — the EEOC's Four-Fifths rule, IBM's AI Fairness 360, Microsoft's Fairlearn, and SHAP/LIME explainability — run through a bank's auto-loan model and a warehouse-supervisor résumé screener, not just defined
• A four-part conformity documentation pack (impact assessment, fairness testing protocol, training data audit, risk mitigation plan) built as a living file, with the per-day penalty math behind NYC's Local Law 144 and Colorado's ADMTA that makes "annual audit" the wrong mental model
• The four-criteria weighted framework for scoring a vendor's AI system when you have no access to their training data — only an attestation you have to decide whether to trust or verify yourself
• The real accountability gap behind the CMO/CDO/CIO/Chief AI Officer split (87% of organizations increasing AI budgets, only 14% with defined C-suite accountability) and the single question that tells you whether a governance committee has ever actually blocked a launch
• The AIGP-versus-AAIA certification math for a career-changer — why AIGP paired with an existing privacy, security, or audit background clears a $169,700 median against $151,800 for AIGP alone, and why AAIA is a later specialization, not a starting credential
• Three fully staged interview scenarios — a reduction-in-force model's age-proxy finding, a transparency-versus-privacy tradeoff, and a five-beat incident walkthrough — built around the same stakeholder-sequencing logic every governance interview is actually testing

Written for the reader who already has a privacy, security, or audit background, or has worked through the beginner-level foundations of AI governance — not for someone starting from zero, and not for someone who still needs the four functions of the NIST AI RMF explained from scratch.

What you get: the complete ebook as EPUB (Kindle, Kobo, Apple Books and every reader app) and as PDF, delivered the moment you pay. Book 2 of the From Experience to Evidence AI Governance Specialist series.

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