Making fair predictions when we don't know who's who
What if a computer must make a fair choice but we dont know someone's group? Researchers try a trick where one part of the system learns to predict, while another part tries to hide any hint about a person's sensitive traits.
This method is called adversarial training, but you can think it as a game: one side guesses, the other side covers up clues.
The surprise — you dont need lots of examples of the protected group to start, only a small amount of data can do the job.
Also, the kind of examples you pick really matters.
The data you feed the hiding team actually shapes what “fair” means in practice, so the choices people make about data end up deciding outcomes.
That means when companies try to remove bias, they must pick their data with care, because distribution of examples will steer decisions and affect privacy and fairness.
Simple changes in samples can lead to big different results, so watch what you train on.
Read article comprehensive review in Paperium.net:
Data Decisions and Theoretical Implications when Adversarially Learning FairRepresentations
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