Back to Topic 4.4 — Ethical considerations
4.4.1Computer Science SL5 flashcards

Ethics of machine learning

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Card 1 of 54.4.1
4.4.1
Question

Where does bias in a machine-learning model come from?

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All 5 Flashcards — Ethics of machine learning

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Card 1concept

Question

Where does bias in a machine-learning model come from?

Answer

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.

Card 2concept

Question

Why does removing a sensitive field not remove bias?

Answer

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.

Card 3concept

Question

Why must fairness be measured per group rather than overall?

Answer

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.

Card 4concept

Question

What is the accountability problem with machine learning?

Answer

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.

Card 5definition

Question

Name three ethical concerns about machine learning beyond bias.

Answer

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.

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