this post was submitted on 28 Nov 2023
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They literally say they do this "to combat the racial bias in its training data"
And like I said, this makes no fucking sense.
If your training processes, specifically your training data, has biases, inserting key words does not fix that issue. It literally does nothing to actually combat it. It might hide issues if the data model has sufficient training to do the job with the inserted key words, but that is not a fix, nor combating the issue. It is a cheap hack that does not address the underlying training issues.
congratulations you stumbled upon the reason this is a bad idea all by yourself
all it took was a bit of actually-reading-the-original-post
?
My position was always that this is a bad idea.
the point of the original post is that artificially fixing a bias in training data post-training is a bad idea because it ends up in weird scenarios like this one
your comment is saying that the original post is dumb and betrays a lack of knowledge because artificially fixing a bias in training data post-training would obviously only result in weird scenarios like this one
i don't know what your aim is here