this post was submitted on 28 Nov 2023
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That explanation makes no fucking sense and makes them look like they know fuck all about AI training.
The output keywords have nothing to do with the training data. If the model in use has fuck all BME training data, it will struggle to draw a BME regardless of what key words are used.
And any AI person training their algorithms on AI generated data is liable to get fired. That is a big no-no. Not only does it not provide any new information from the data, it also amplifies the mistakes made by the AI.
They are not talking about the training process, to combat racial bias on the training process, they insert words on the prompt, like for example "racially ambiguous". For some reason, this time the AI weighted the inserted promt too much that it made Homer from the Caribbean.
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
You started your initial rant based on a misunderstanding of what was actually said. Stumbling into the correct answer != knowing what you’re reacting to
Yes. The training data has a bias, and they are using a cheap hack (prompt manipulation) to try to patch it.
Any training data almost certainly has biases. For awhile, if you asked for pictures of people eating waffles or fried chicken they'd very likely be black.
Most of the pictures I tried of kid-type characters were blue eyed.
Then people review the output and say "hey this might still racist, so they tweak things to "diversity" the output. This is likely the result of that, where they've "fixed" one "problem" and created another.
Behold, Homer in brownface. D'oh!
So the issue is not that they don't have diverse training data, the issue is that not all things get equal representation. So their trained model will have biases to produce a white person when you ask generically for a "person". To prevent it from always spitting out a white person when someone prompts the model for a generic person, they inject additional words into the prompt, like "racially ambiguous". Therefore it occasionally encourages/forces more diversity in the results. The issue is that these models are too complex for these kinds of approaches to work seamlessly.
There are 2 problems with not having enough diversity in training data:
The AI will be worse at depicting diversity when prompted, eg. If the AI hasn't seen enough pictures of black people it may not be able to depict black hair properly as it doesn't "know what it looks like"
The AI will not show as much diversity when not prompted. The AI is working off statistics so if you tell it to depict a person and most of the people it's "seen" are white men it will almost always depict a white man because that's statistically what a person is according to its data.
This method combats the second problem, but not the first. The first can mostly be solved by generally scaling the training data though, which is mostly what these companies have been doing. Even if only 1% of your images are of POC, if you have 1b images 10mil will be of POC which may be enough to train it. The second problem would remain unsolved though since the AI will always go with the statistically safe 99%.
though this isn't pertinent to the post in question, training AI (and by AI I presume you mean neural networks, since there's a fairly important distinction) on AI-generated data is absolutely a part of machine learning.
some of the most famous neural networks out there are trained on data that they've generated themselves -> e.g., AlphaGo Zero
They could try to compensate the imbalance by explicitly asking for the lesser represented classes in the data... It's an idea, not quite bad but not quite good either because of the problems you mentioned.