The AI Safety Checklist: Why You Need to Be the "Department of First Things First"
We are living through the wildest "Gold Rush" in the history of HR technology. Every vendor—from your massive ERP to that tiny startup you met at HR Tech—is sprinting to sell you a "Copilot," an "Agent," or an "Assistant." They promise magic. They promise to give you your weekends back. They promise it’s safe.
Do not believe them.
I am a recovering "Yes Man." For years, I measured my success by my ticket closure rate. I thought being a good business partner meant saying, "Sure, we can build that!" to every request that came across my desk. I was an order-taker, a configurator, a path-of-least-resistance machine.
But in the age of Agentic AI, being a "Yes Man" isn't just inefficient; it’s dangerous.
If we say "yes" to every shiny new AI tool without ruthlessly prioritizing the actual business value, we aren't innovating. We are just automating chaos. We are building faster ways to do the wrong things.
The Shift: From Order-Taker to Architect
Your job right now isn't to be the "Department of No." But it absolutely cannot be the "Department of Yes."
You need to become the "Department of 'Define the Problem, Not the Solution'."
We have to stop implementing solutions to undefined problems. When a stakeholder rushes in demanding an "AI Agent for Recruiting," the old me would have started gathering technical requirements. The new me stops and asks for the business requirements:
"What specific bottleneck are we solving?"
"Is the underlying process actually documented?"
"Is the data this agent needs clean, or is it a dumpster fire?"
If you can't answer those questions, we aren't writing a single line of code. We aren't turning on a single feature. We are going to prioritize ruthlessly, focusing only on the tools where the business needs are in order and the foundation is solid.
And once those business requirements are met, we move to safety. Here is the 5-point interrogation checklist I use before I let any robot read my HR data.
The AI Safety Checklist
1. The "Vampire" Question: Is my data training your public model?
We call this the Vampire question because once your data enters a public Large Language Model (LLM), you can never really get it back. It’s immortal.
The Danger: Many "free" or low-cost AI tools are free because you are the product. They ingest your prompts—which might contain sensitive salary data, strategy documents, or PII—to train their base models. You are effectively paying them to steal your IP.
The Trap Answer: "We take privacy seriously!" (This means nothing).
The Only Acceptable Answer: "We have a zero-retention policy for inference. Your data stays in your tenant/instance and is never used to train our foundational models or shared with other customers."
2. The "Receipts" Question: Where is your independent bias audit?
We are past the era of "trust me." With legislation like NYC Local Law 144 and the EU AI Act coming online, internal testing is legally worthless.
The Danger: An AI recruiting tool that "accidentally" learns to penalize gaps in employment (which disproportionately affects women) or specific zip codes. If you use this tool, you are liable for the discrimination, not the vendor.
The Trap Answer: "Our data science team rigorously tests for fairness." (That’s like a student grading their own homework).
The Only Acceptable Answer: "Here is the PDF of our 2025 audit conducted by [Reputable Third-Party Firm], specifically testing for adverse impact on race, gender, and age."
3. The "Glass Box" Question: Can you explain why it made that decision?
In HR, "The computer said so" is not a legal defense.
The Danger: A manager asks why an employee was flagged as a "flight risk." The AI says "High Risk," but can't say why. The manager then treats that employee differently, creating a self-fulfilling prophecy based on a black box.
The Trap Answer: "The algorithm is proprietary," or "It's too complex to explain."
The Only Acceptable Answer: "Explainability is native. Every output comes with a 'reasoning chain' or citation that points back to the specific data points (e.g., 'Flagged due to: No compensation increase in 24 months + Decline in peer feedback scores')."
4. The "Blast Radius" Question: Does this agent respect my existing security groups?
This is the single biggest technical failure point in Retrieval-Augmented Generation (RAG).
The Danger: You ask an AI agent, "Show me the budget for the Marketing team." The agent, trying to be helpful, pulls data from a global "System User" account that has access to everything, inadvertently showing you the CMO's salary, which you definitely shouldn't see.
The Trap Answer: "It connects via API!" (APIs often have super-user permissions).
The Only Acceptable Answer: "The agent operates strictly within the security context of the logged-in user. It inherits your existing Workday/HRIS security groups. It cannot read what you cannot read."
5. The "Kill Switch" Question: How do I turn it off instantly?
AI hallucinates. It makes mistakes. It can be tricked ("jailbroken") by clever users. When (not if) this happens, you need a handbrake.
The Danger: Your internal chatbot starts spewing offensive jokes or incorrect benefits advice at 2:00 PM on a Tuesday. You call support, and they say, "We'll escalate a ticket to engineering." Meanwhile, screenshots are circulating on Slack.
The Trap Answer: "Just submit a P1 ticket."
The Only Acceptable Answer: "You have a toggle in the admin console. You can disable the feature for the entire organization instantly, without our help."
Conclusion
Innovation without governance isn't strategy; it's liability. As HRIS leaders, we have to be the ones brave enough to ask the awkward questions.
Because if we don't protect our house, no one else will.
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