Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men
31–40 of 173 posts
Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men
#32Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men
#33Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men
#34Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men
#35Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men
#36Don't these kinds of companies have a basic checklist before releasing features like this? And if so, why doesn't this checklist include things like "doesn't identify members of racial group as animals" ? It makes me think that any woke PR coming from Facebook is lip service, if they don't have even basic checks in place.
EDIT: pardon me, what I wrote is wrong. We are all primates after all :)
Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men
#37Surprised they weren't more careful with this after Google had the same problem a few years ago. https://www.theverge.com/2018/1/12/16882408/google-racist-go...
Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men
#38Aren't we all primates?
I wonder what would an AI trained with pictures from India would show.
Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men
#39Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men
#40Earlier quoted context omitted.
Another way to put this is: nobody working in this field has control over what they are building, and can't make any promises about how it will behave. It's like launching a rocket ship that you can't test in a physical simulation first. Something is going to go wrong that can't be predicted because we lack the understanding, but this is seen as an acceptable risk.
Which is inherent to the problem. The domain is the space of _all possible natural images_, it’s so big, it’s ridiculous. Fundamentally, these techniques do not analyze images like humans do, but are rather trained to pick out any salient signal it can latch on to. That seems to be “primates are mostly like humans but darker”, which is superficially true but a pretty weak definition as it includes dark skinned humans…
I expect you'd get better results if you allowed the system to call humans “primates”, then accept “human primate” as “human” when parsing the output. (That is, leave the “is_primate” output line floating while training on pictures of humans.) I don't know whether that would work, though.