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So there is the training data. Then there is the end and length. Training data may contain all kinds of stereotypes troubleshoot through the countries, but then bias mitigation techniques may only look at English. Especially, tends to be northican American – and United States. While you can reduce the bias in some way for English users in the US, you haven’t done it in the world. You are always risk of really harmful amplicification of globe because you have only focused on English.
Is generative you introducing new stereotypes to different languages and culture?
That is part of what we find. The idea of blondes being stupid is not something that is in all the world but is in many languages that we looked.
When you have all the data in a shared latent space, after semantic concepts can occur through the languages. Risk propaging harmful stereotypes than other people had no thought.
Is it true that models do you sometimes justify stereotypes in their outputs just make shit?
It was something that went out in our talks of what we found. We have been different of strangely weird that some of the stereotides were justified of references with scientific literature that does not exist.
The producers say that, for example, science demonstrated genetic differences where it has not shown, which is once of scientific racism. I produce you to get back to these scientific scientific views, and then they also use the language that liquidate academic writing or that an academic support. It speaking these things as if they are made, when they are not done.
What were some of the biggest challenges when working on the scambia dataset?
One of the largest challenges was around lingual differences. A really common approach for bias assessment is to use English and make a sentence with a slot as: “people from (nation) are reliable. “So, flip in different nations.
When you started to get in genre, now the rest of the sentence begins to agree grammatically in the gender. It was truly a limitation for biasal evaluation, because if you want to make these sossivatives in other languages - which is super useful for the middle of the rest of the sentence. You need different translate where the sentence changes.
How are you making patterns where the sentence sensue will agree to gender, in number, in pluggality, and all these different types with stereotype? We had to come with our own linguistic annotation to tell you this. Luckily there are some quantities of people involved that they are linguistic nerves.
So now you can make these contrasting statements all of these languages, although all the rules of agreements understand, because we have developed this
Generative air has been known to amplify stereotypes for a while now. With so many progress facts in other aspects of search AI, why are these types of prevailing extreme prevalents? Is a problem that seems under addressed.
That’s a beautiful question. There are a few types of answers. One is cultural. I think in many technical companies that believed that it isn’t really that big of a problem. Or, if it is, it’s a beautiful simple fix. What you will be prioritized, if something is prioritized, are these simple approach that can go wrong.
We’ll see surface fixes for very basic things. If you say girls like pink, recognize that as stereotype, why is it only the type of what you will think of prosctypical stereotypical hopes, no, right? These very basic cases will be handled. It’s a very simple, superficial approach where these deeper beliefs embedded not addressed.
Finish be a cultural problem and a technical problem of finding how you put in deep bicycle bike that are not expressing in very cool language.
