AI isn’t going to kill us. Agents don’t ‘go rogue’. Safety shouldn’t be an afterthought. AI Governance is how you embed accountability, authority, responsibility, safety, security, ethics, and compliance into the management of AI.
This is important to HR and Recruiting because many of the most serious risks for the organization live in the management of people, machines, and their interactions with each other.
Here are the current pieces in the series. Expect more.
AI Governance 1: Intro The board still owns the decision when a machine makes it. That’s the premise of a new series on AI governance. Accounting tells the board whether the money story is trustworthy. Nothing yet tells it whether delegated machine judgment is authorized and safe. Until that exists, HR and IT are guessing at their own roles.This first post lists the ten-item evidence package a board should be getting. We are not producing most of it.
AI Governance 2: Aderit.AI Research Note 1 Ask HR, payroll, and finance what headcount is. Six answers, no agreement, nobody lying. Same with a job description: recruiting, comp, managers, and L&D each hold a different one, and each is right. The piece says grinding them into one clean truth was the thirty-year mistake. IT, read the part about address line three and the t-shirt sizes.
AI Governance 3: The Aderit.ai Readiness Stack Pilots are built to prove capability. Governance is built to survive consequence. The piece says those are different engineering problems, and PowerPoint has hidden the difference. It lists eighteen layers an agent needs. Most companies have fragments of four. Your HCM vendor’s AI overlay inherits the governance of the platform underneath it. Read the ten control-plane layers and mark the ones you actually own. Nobody sells this. You build it or you don’t have it.
AI Governance 4: Aderit.ai -> Data Quality You may be about to make this mistake. The piece argues against “single source of truth” for HR data. Recruiting, comp, operations, and L&D each describe the same job differently. Each is right for its purpose. HR already knows which fields disagree. IT owns the layer that would flatten them.
AI Governance 5: Aderit Wrapup When we produce “the” job description in litigation, which one do we produce? This piece argues there is no system of record in HR. Every silo keeps its own version. Job descriptions carry several irreconcilable meanings, all in use. Exemption and accommodation turn on that.
The Board Already Owns AI Governance: AI Governance 6 Read this before the next insurance renewal. It argues that “a human reviews it” is not the defense we think it is. Automation bias is well documented. A reviewer who never overrules the system is part of the interface, not a control. The wellness chatbot section is squarely our problem. D&O, cyber, E&O, and EPLI policies are allocating AI risk more explicitly now. There is no useful version of the sentence, “We assumed it was covered.”
The Gap Is Where the Job Gets Learned: AI Governance 7 (coming Thursday) The machine’s recommendation is officially optional and socially mandatory. That sentence is the piece. Policy says a human decides. Disagreeing costs time and attracts attention, so nobody does it. Authority has moved. Accountability has not. A model inventory will not show you this. Neither will a vendor assessment. Start with one AI-assisted workflow. Follow five real cases end to end with the people doing the work. Then fix one condition instead of writing a policy. It’s a discovery problem before it’s a technology problem.
The Governance Series So Far: AI Governance 8 (This piece)
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