Ethics of machine learning
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Flip to reveal answersWhere does bias in a machine-learning model come from?
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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.
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.
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.
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.
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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Topic 4.4 hub
Ethical considerations
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