This kind of seems like a non-article to me. LLMs are trained on the corpus of written text that exists out in the world, which are overwhelmingly standard English. American dialects effectively only exist while spoken, be it a regional or city dialect, the black or chicano dialect, etc. So how would LLMs learn them? Seems like not a bias by AI models themselves, rather a reflection of the source material.
It’s not an article about LLMs not using dialects. In fact, they have learned said dialects and will use them if asked.
What they did was, ask the LLM to suggest adjectives associated with sentences - and it would associate more aggressive or negative adjectives with African dialect.
Seems like not a bias by AI models themselves, rather a reflection of the source material.
All (racial) bias in AI models is actually a reflection of the training data, not of the modelling.
I would assume the small amount of training data written that way doesn’t contain that many professional research papers, corporate emails or calm poetry, but would consist mostly of social media posts and comments which have a rather heavy bias towards aggressive and negative.
I feel like not everyone is conscious of these biases and we need to raise the awareness and try preventing for example HR people from buying AI-based screening software that has a strong bias that is not disclosed by their vendors (because why would you advertise that?)
I was confused how a resume or application would be largely affected, but the article points out that software is often used to look over social media now as part of hiring (which is awful).
The bias when it determined guilt or considered consequences for a crime is concerning as more law enforcement agencies integrate black box algorithms into investigative work.
AIs are trained on what is written in the Internet. Latin is not spoken, it’s written. But even then, it’s rarely used. African american is a dialect, it’s only present in speech.
You need to get out more. I totally get that you would think that’s the case, but only if you’re not exploring parts of the internet outside your bubble. It’s absolutely written.
There are actually quite a few books written in AAVE…the earliest I’m aware of is their eyes were watching god, from the 1930s. The Color Purple, Beloved, The Sellout, the books of Chester Himes…
But it’s also more insidious than that, because the far reaching implications of this bias often cannot be predicted. For example, excluding all gender data from training ended up making sexism worse in this real world example of financial lending assisted by AI and the same was true for apple’s credit card and we even have full-blown articles showing how the removal of data can actually reinforce bias indicating that it’s not just what material is used to train the model but what data is not used or explicitly removed.
This is so much more complicated than “this is obvious” and there’s a lot of signs pointing towards the need for regulation around AI and ML models being used in places it really matters, such as decision making, until we understand it a lot better.
Yeah this seems like a non-issue to me as well; the source material for the models is probably the cause of this bias.
I also don’t think there’s a lot of sources for this manner of speaking. Let’s also not forget that there’s oftentimes instructions given to the LLM that ask it to avoid certain topics which it will in fact do.
This kind of seems like a non-article to me. LLMs are trained on the corpus of written text that exists out in the world, which are overwhelmingly standard English. American dialects effectively only exist while spoken, be it a regional or city dialect, the black or chicano dialect, etc. So how would LLMs learn them? Seems like not a bias by AI models themselves, rather a reflection of the source material.
It’s not an article about LLMs not using dialects. In fact, they have learned said dialects and will use them if asked.
What they did was, ask the LLM to suggest adjectives associated with sentences - and it would associate more aggressive or negative adjectives with African dialect.
All (racial) bias in AI models is actually a reflection of the training data, not of the modelling.
I would assume the small amount of training data written that way doesn’t contain that many professional research papers, corporate emails or calm poetry, but would consist mostly of social media posts and comments which have a rather heavy bias towards aggressive and negative.
That’s what is usually meant by AI bias: a bias in the material used to train the model that reflects in its behavior
But why is it even mentioned then? It’s FUCKING OBVIOUS. It’s like saying “AIs are biased towards english and neglect latin” or smth ffs
I feel like not everyone is conscious of these biases and we need to raise the awareness and try preventing for example HR people from buying AI-based screening software that has a strong bias that is not disclosed by their vendors (because why would you advertise that?)
I was confused how a resume or application would be largely affected, but the article points out that software is often used to look over social media now as part of hiring (which is awful).
The bias when it determined guilt or considered consequences for a crime is concerning as more law enforcement agencies integrate black box algorithms into investigative work.
Great comparison, a dialect used by millions of people to a dead language. It really shows how much you care about the people who speak that dialect…
AIs are trained on what is written in the Internet. Latin is not spoken, it’s written. But even then, it’s rarely used. African american is a dialect, it’s only present in speech.
You need to get out more. I totally get that you would think that’s the case, but only if you’re not exploring parts of the internet outside your bubble. It’s absolutely written.
There are actually quite a few books written in AAVE…the earliest I’m aware of is their eyes were watching god, from the 1930s. The Color Purple, Beloved, The Sellout, the books of Chester Himes…
What is obvious to you is not always obvious to others. There are already countless examples of AI being used to do things like sort through applicants for jobs, who gets audited for child protective services, and who can get a visa for a country.
But it’s also more insidious than that, because the far reaching implications of this bias often cannot be predicted. For example, excluding all gender data from training ended up making sexism worse in this real world example of financial lending assisted by AI and the same was true for apple’s credit card and we even have full-blown articles showing how the removal of data can actually reinforce bias indicating that it’s not just what material is used to train the model but what data is not used or explicitly removed.
This is so much more complicated than “this is obvious” and there’s a lot of signs pointing towards the need for regulation around AI and ML models being used in places it really matters, such as decision making, until we understand it a lot better.
Yeah this seems like a non-issue to me as well; the source material for the models is probably the cause of this bias.
I also don’t think there’s a lot of sources for this manner of speaking. Let’s also not forget that there’s oftentimes instructions given to the LLM that ask it to avoid certain topics which it will in fact do.