• Eddyzh@lemmy.world
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    5 months ago

    It is ridiculous. However, how can we know you did not first instruct to only show dark skin? Or select these from many examples that showed something else?

    • stoneparchment@possumpat.io
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      5 months ago

      It’s also like, I guess I would prefer it to make mistakes like this if it means it is less biased towards whiteness in other, less specific areas?

      Like, we know these models are dumb as rocks. We know that they are imperfect and that they mirror the biases of their trainers and training data, and that in American society that means bias towards whiteness. If the trainers are doing what they can to prevent that from happening, whatever, that’s cool… even if the result is some dumb stuff like this sometimes.

      I also don’t think it’s a problem for the user to specify race if it matters? Like “a white queen of England” is a fine thing to ask for, and if it isn’t specified, the model will include diverse options even if they aren’t historically accurate. No one gets bent out of shape if the outfits aren’t quite historically accurate, for example

      • ji59@kbin.social
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        5 months ago

        The problem is that these answers are hugely incorrect and if some child learning about history of England would see this, they would create bias that England was always diverse.
        The same is true for some recent post, where people knowing nothing about Scotland history could learn from images that half of Scotland population in 18th century was black.
        So from my perspective these images are just completely wrong and it should be fixed.
        Also if you want diversity, what about handicapped people?

        • stoneparchment@possumpat.io
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          5 months ago
          • it’s true that this would mislead children, but the model could hallucinate about literally anything. Especially at this stage, no one-- children or adults-- should be uncritically accepting what the model states as fact. That said, I agree LLMs need to improve their factual accuracy

          • Although it is highly debated, some scholars suggest Queen Charlotte might have had African ancestry, or that she would be considered a POC by today’s standards. Of course, she reigned in the 17-1800s, but it isn’t entirely outlandish to have a “Queen of Color”, if we aren’t requesting a specific queen or a specific race

          • People of color did live in England in the middle ages? Like not diverse in the way we conceive now, but here are a few papers discussing the racial diversity at the time. It was surely less intermingled than today, but it’s not like these images are impossible

          • Other things are anachronistic or fantastical about these images, such as clothing. Are we worried about children getting the wrong impression of history in that sense?

          • Of course increasing visibility and representation of all kinds of marginalized people is important. I, myself, am disabled, so I care about that representation too-- thanks for pointing out how we could improve the model further. I do kinda feel like people would be groaning if the model had produced a Queen with a visible disability, though… I would be delighted to be wrong on this front :)

          • KeenFlame@feddit.nu
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            5 months ago

            These are not hallucinations. The image generator system prompt has been intensely altered to mix all races and genders. The model is probably not inaccurate up until it being misused. The misuse could be at any level of interaction, so it’s very misleading to base it on such an example

        • groet@feddit.de
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          5 months ago

          Repeat after me:

          “Current AI is not a knowledge tool. It MUST NOT be used to get information about any topic!”

          If your child is learning Scottish history from AI, you failed as a teacher/parent. This isn’t even about bias, just about what an AI model is. It’s not even supposed to be correct, that’s not what it is for. It is for appearing as correct as the things it has been trained on. And as long as there are two opinions in the training data, the AI will gladly make up a third.