[A] new dataset [Margaret Mitchell] helped create [tests] how AI models continue perpetuating stereotypes. Unlike most bias-mitigation efforts that prioritize English, this dataset is malleable, with human translations for testing a wider breadth of languages and cultures. You probably already know that AI often presents a flattened view of humans, but you might not realize how these issues can be made even more extreme when the outputs are no longer generated in English.
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[W]hy are these kinds of extreme biases still prevalent? It’s an issue that seems under-addressed.
That’s a pretty big question. There are a few different kinds of answers. One is cultural. I think within a lot of tech companies it’s believed that it’s not really that big of a problem. Or, if it is, it’s a pretty simple fix. What will be prioritized, if anything is prioritized, are these simple approaches that can go wrong.
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Related on the SLP
It ends up being both a cultural issue and a technical issue of finding how to get at deeply ingrained biases that aren’t expressing themselves in very clear language.
