I remember checking like a year ago and they still had the word "gorilla" blacklisted (i.e. it never returns anything even if you have gorilla images).
Gotta love such a high quality fix. When your upper high tech, state of the art algorithm learns racist patterns just blocklist the word and move on. Don't worry about why it learned such patterns in the first place.
But this is not an algorithm. It's a trained neural network which is practically a black box. The best they can do is train it on different data sets, but that's impractical.
That's exactly the problem I was trying to reference. The algorithms and data models are black boxes - we don't know wat they learned or why they learned it. That setup can't be intentionally fixed, and more importantly we wouldn't know if it was fixed because we can only validate input/output pairs.
You do understand that this has nothing to humans in general right? This isn't AI recognizing some evolutionary pattern and drawing comparisons to humans and primates -- it's racist content that specifically targets black people that is present in the training data.
I don't know nearly enough about the inner workings of their algorithm to make that assumption.
The internet is surely full of racist photos that could teach the algorithm. The algorithm could also have bugs that miss-categorize the data.
The real problem is that those building and managing the algorithm don't fully know how it works or, more importantly, what it had learned. If they did the algorithm would be fixed without a term blocklist.
Do we have enough info for to say that decisively?
Ideally we would see the training data, though its probably reasonable to assume a random collection of internet content includes racist imagery. My understanding, though, is that the algorithm and the model of data learned is still a black box that people can't parse and understand.
How would we know for sure racist output is due to the racist input, rather than a side effect of some part of the training or querying algorithms?
