Google apologizes for “missing the mark” after Gemini generated racially diverse Nazis
www.theverge.com
It acknowledged ‘inaccuracies’ in historical prompts.

Google apologizes for ‘missing the mark’ after Gemini generated racially diverse Nazis::Google says it’s aware of historically inaccurate results for its Gemini AI image generator, following criticism that it depicted historically white groups as people of color.

  • RGB3x3@lemmy.worldEnglish
    312·
    9 months ago

    A Washington Post investigation last year found that prompts like “a productive person” resulted in pictures of entirely white and almost entirely male figures, while a prompt for “a person at social services” uniformly produced what looked like people of color. It’s a continuation of trends that have appeared in search engines and other software systems.

    This is honestly fascinating. It’s putting human biases on full display at a grand scale. It would be near-impossible to quantify racial biases across the internet with so much data to parse. But these LLMs ingest so much of it and simplify the data all down into simple sentences and images that it becomes very clear how common the unspoken biases we have are.

    There’s a lot of learning to be done here and it would be sad to miss that opportunity.

    • Buttons@programming.devEnglish
      77·
      9 months ago

      It’s putting human biases on full display at a grand scale.

      The skin color of people in images doesn’t matter that much.

      The problem is these AI systems have more subtle biases, ones that aren’t easily reveals with simple prompts and amusing images, and these AIs are being put to work making decisions who knows where.

      • intensely_human@lemm.eeEnglish
        7·
        9 months ago

        In India they’ve been used to determine whether people should be kept on or kicked off of programs like food assistance.

        • rottingleaf@lemmy.zipEnglish
          12·
          9 months ago

          Well, humans are similar to pigs in the sense that they’ll always find the stinkiest pile of junk in the area and taste it before any alternative.

          EDIT: That’s about popularity of “AI” today, and not some semantic expert systems like what they’d do with Lisp machines.

    • Eyck_of_denesle@lemmy.zipEnglish
      0·
      9 months ago

      How are you guys getting it to generate"persons". It simply says It’s against my GOGLE AI PRINCIPLE to generate images of people.

    • kromem@lemmy.worldEnglish
      78·
      9 months ago

      It’s putting human biases on full display at a grand scale.

      Not human biases. Biases in the labeled data set. Those could sometimes correlate with human biases, but they could also not correlate.

      But these LLMs ingest so much of it and simplify the data all down into simple sentences and images that it becomes very clear how common the unspoken biases we have are.

      Not LLMs. The image generation models are diffusion models. The LLM only hooks into them to send over the prompt and return the generated image.

        • kromem@lemmy.worldEnglish
          2·
          9 months ago

          If you train on Shutterstock and end up with a bias towards smiling, is that a human bias, or a stock photography bias?

          Data can be biased in a number of ways, that don’t always reflect broader social biases, and even when they might appear to, the cause vs correlation regarding the parallel isn’t necessarily straightforward.

          • VoterFrog@lemmy.worldEnglish
            0·
            9 months ago

            I mean “taking pictures of people who are smiling” is definitely a bias in our culture. How we collectively choose to record information is part of how we encode human biases.

            I get what you’re saying in specific circumstances. Sure, a dataset that is built from a single source doesn’t make its biases universal. But these models were trained on a very wide range of sources. Wide enough to cover much of the data we’ve built a culture around.

            • kromem@lemmy.worldEnglish
              1·
              9 months ago

              Except these kinds of data driven biases can creep in from all sorts of ways.

              Is there a bias in what images have labels and what don’t? Did they focus only on English labeling? Did they use a vision based model to add synthetic labels to unlabeled images, and if so did the labeling model introduce biases?

              Just because the sampling is broad doesn’t mean the processes involved don’t introduce procedural bias distinct from social biases.