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This is a straw man argument. It's easy to find examples of algorithms that are biased -- let's just look at facial recognition. Sure, there are situations like
by kaitai 7y ago
This is a straw man argument. It's easy to find examples of algorithms that are biased -- let's just look at facial recognition. Sure, there are situations like what you describe, but are you going to argue then that dark-skinned women really must be men, or more manly, or something, just because Amazon's facial recognition algorithm has trouble with dark-skinned women?
The parent is not making a "scientific" claim. They are making a workflow/process claim. It is not the case that all things are "science" or "pseudoscience". There are many other topics humans can discuss.
- xamuel 7y agoI'm not saying no biased algorithms exist. I'm just saying, we shouldn't throw away what initially appears to be discriminatory bias, we should instead ask, "what caused this?" And that goes no matter what color of skin is represented on the dev team.