Training data should accurately *represent* real-world. And real-world is full of biases. Want to remove them? Fix the biases in real-world first.
"Debiasing" accurate data is replacing the real world with a fantasy one.
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"Labeled Faces in the Wild" was 78% male and 84% white. Is this data disproportionately biased, or "real world"? When we talk about removing the bias, we talk about ensuring that it stays proportionate, not equal.
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Isn't this exactly why data bias is a problem?
Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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I don't know. Models aren't only about accurate real world modeling. It can also be about company policy. It's discomfiting if the only variable you change on a credit application is the person's race or gender and the algorithm goes from 'approve' to 'deny' or vice versa.
Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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Conveniently stated in hard-to-dispute form, but a content-free sentence. Debiasing is what you do when your data is unrepresentative and biased. But you knew that.
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Tämä twiitti ei ole saatavilla.
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Not a very good statement as it depends on the application intended and how well the training data represents that application. Can you give concrete examples where you will be against debiasing?
Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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So are we to understand what he actually meant by “conservative ethics” in an earlier tweet is just “make America great again”? Most data on human activity is “accurately unfair”, i.e. accurately represents a world that all people of goodwill should want to leave behind
Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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No, adressing bias is making sure you train algorithms to do what's right, ethical or what's desirable as a society.
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The problem is, who decides what is right, ethical or desiderable as a society? Besides very clear cases, the concept of "right" might differ quite a lot, depending on a lot of factors. Culture above all, I would say...
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