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They literally just add "black" and "female" with some weight before any prompt containing person. A comical work around to so called "bias" (isn't the whole p
by kache_ 4y ago
They literally just add "black" and "female" with some weight before any prompt containing person.
A comical work around to so called "bias" (isn't the whole point of these models to encode some bias?). Here's some experimentation showing this.
https://twitter.com/rzhang88/status/1549472829304741888 https://twitter.com/rzhang88/status/1549472829304741888
As competitors with lower price points prop up, you'll see everyone ditch models with "anti bias" measures and take their $ somewhere else. Or maybe we'll get some real solution, that adds noise to the embeddings, and not some half assed workaround to the arbitrary rules that your resident AI Ethicist comes up with.
- danielvf 4y agoAdd after. So you can see the added words by making a prompt like "a person holding a sign saying ", and then the sign says the extra words if they are added.
- kache_ 4y agoYeah actually, good call. The position of the token matters, since these things use transformers to encode the embeddings. https://www.assemblyai.com/blog/how-imagen-actually-works/ https://www.assemblyai.com/blog/how-imagen-actually-works/
- whywhywhywhy 4y agoHow does it deal with bias that is negative? Would only work for positive biases where if they actually want to equalize it then it needs to be adding the opposite to negative biases. To counteract the bias of their dataset they need to have someone sitting there actively thinking in bias to counteract the bias with anti-bias seasoning for every bias causing term. Feel bad for whatever person is tasked with that job. Could always just fix your dataset, but who's got time and money to do that /s