The Question That Haunted Me

“But doesn’t AI get it wrong?”

It was a fair question. My answer wasn’t.

At the time I was deep in the AI hype — drinking the Kool-Aid, evangelising the technology, convinced the outputs spoke for themselves. So when someone pushed back and asked whether AI could really be trusted, I defended it. Brushed past the concern. Missed the moment entirely.

What I should have done was lean in. Agreed. Had the honest conversation about bias, about missing and unrepresentative training data, about what it actually means when AI gets it wrong at scale — not just technically wrong, but wrong in ways that shape real people’s access to resources, opportunities, and fair treatment. Wrong in ways that can ruin lives.

That question has haunted me for over three years.

Because “AI getting it wrong” isn’t one problem. It’s two very different ones, and treating them as the same is its own kind of mistake.

There are hallucinations — the model confidently generating plausible but factually wrong outputs. A technical failure. The model doesn’t know what it doesn’t know.

And there is bias — the model learning and amplifying the prejudice, inequality, and exclusion already baked into its training data. Not a malfunction. The model working exactly as designed, just on contaminated inputs. Faithfully. At scale.

The person asking that question deserved an answer that acknowledged both. Instead they got a defence of the technology.

Three years later, older and wiser, I’m finally leaning in.

AI doesn’t operate in a vacuum. It operates inside the same social structures, institutions, and power dynamics that have always shaped who gets access and who gets excluded. And because AI learns from data — data generated by humans, in a world with a complicated history — it inherits everything recorded in that data. The assumptions. The gaps. The inequalities baked in long before any algorithm touched it.

That’s why keeping AI ethical isn’t optional, and it isn’t a feature you bolt on at the end. It has to be built in by design — from the data up.

This series is about what that actually means.

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Next: Part 2 — Bias and Fairness.