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.
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.
How are you guys getting it to generate"persons". It simply says It’s against my GOGLE AI PRINCIPLE to generate images of people.
They actually neutered their AI on thursday, after this whole thing blew up.
https://abcnews.go.com/Business/wireStory/google-suspends-gemini-chatbots-ability-generate-pictures-people-107446867
So right now, everyone’s fucked because Google decided to make a complete mess of this.
Not human biases. Biases in the labeled data set. Those could sometimes correlate with human biases, but they could also not correlate.
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.
Who made the data set? Dogs? Pigeons?
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.
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.
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.
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.
In India they’ve been used to determine whether people should be kept on or kicked off of programs like food assistance.
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.