Ethical considerations

From training data that reflects the world as it was. A model learns the patterns in past decisions, so historical unfairness is reproduced at scale — and wearing the appearance of objectivity, which makes it harder to challenge.

Because other fields stand in for it — a postcode can imply background, a school can imply income — and the model finds those substitutes on its own.

A model can be 95% accurate overall and far worse for a small group, because that group barely affects the average. The headline number hides exactly the problem you are looking for.

When a model causes harm it is unclear who answers for it — the developer, the organisation using it, or the operator. 'The system decided' gives the affected person nobody to appeal to.

Transparency, since a complex model cannot easily explain its decision; privacy and consent, because data given for one purpose is used to train models; and environmental impact, since training large models uses very large amounts of energy and water.

Because they were written for the technology that existed at the time. Something genuinely new does not break the old rules — it sits outside them, doing something nobody thought to allow or forbid.

Individual rights: can a person refuse, and appeal? Privacy: what is collected, and did bystanders agree? Equity: who is left out or served worse? Society: what changes when everyone has it?

It would eventually break much of today's encryption. Because encrypted data can be stolen and stored now and decrypted later, the risk begins before the machines exist — so organisations need quantum-resistant encryption already.

The glasses go everywhere the wearer goes, recording everyone they meet without any consent, and they can also alter what the wearer sees — which raises the question of who chooses that.

Because of the accumulation. When jobs, loans, healthcare and policing all involve models, large parts of a person's life are decided by systems they cannot see, question or appeal against.