• WaterWaiver@aussie.zone
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    1 year ago

    I’ve been encountering this! I thought it was the topics I was using as prompts somehow being bad – it was making some of my podcast sketches look stupidly racist, admittedly though some of them it seemed to style after some not-so-savoury podcasters, which made things worse.

      • WaterWaiver@aussie.zone
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        1 year ago

        Gah English.

        “My sketches” as in “me using the AI software to draw pictures”. It’s not my podcast, I was trying to guess at what the presenters looked like based off the topics they discuss.

  • 520@kbin.social
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    1 year ago

    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.

    • driving_crooner@lemmy.eco.br
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      1 year ago

      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.

      • 520@kbin.social
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        1 year ago

        They are not talking about the training process

        They literally say they do this “to combat the racial bias in its training data”

        to combat racial bias on the training process, they insert words on the prompt, like for example “racially ambiguous”.

        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.

        • jacksilver@lemmy.world
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          9 months ago

          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.

        • Primarily0617@kbin.social
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          1 year ago

          but that is not a fix

          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

  • TWeaK@lemm.ee
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    1 year ago

    The number of fingers and thumbs tells me this maybe isn’t an AI image.