4 posts tagged “ai-governance”.
We've covered what the model is (prediction), that it invents (hallucination), and where your data goes (deployment) — risks that live inside the machine. This pillar is about the risk you add: doing what the AI says without checking. Blind acceptance is the single most expensive habit in applied AI, and the fix isn't a better model. It's a human in the right place at the handoff.
Read post →Five pillars cataloged how AI goes wrong — it predicts, invents, leaks, gets blindly trusted, and costs more than the sticker. This final pillar is about the aftermath: when a decision turns out wrong, can you reconstruct what the system did, explain why, and prove who was responsible? That's accountability, and it rests on three properties — reproducibility, explainability, traceability. It's what makes the other five survivable, and increasingly it's the law.
Read post →Pillar 2 told you to ground a model in your own verified data. But to do that, you have to give the model your data — and that opens a different risk. When people ask whether an AI is safe, the part actually in your control is simpler than the model: where does your data go, who can see it, and is it allowed to be there? That's not a question about the model. It's a deployment decision — cloud, local, or a deliberate hybrid — and it's the one place where a choice made up front closes the risk almost entirely.
Read post →In the first pillar we landed on a hard fact: a language model predicts the next word, and it optimizes for plausible, not true. A hallucination is what happens when those two come apart — and it isn't the machine breaking. It's the machine doing exactly what it does, in the same confident voice it uses for everything else. You don't patch that out. You contain it. This pillar is about how.
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