Technology

23 readers

2 users here now

This magazine is dedicated to discussions on the latest developments, trends, and innovations in the world of technology. Whether you are a tech enthusiast, a developer, or simply curious about the latest gadgets and software, this is the place for you. Here you can share your knowledge, ask questions, and engage in discussions on topics such as artificial intelligence, robotics, cloud computing, cybersecurity, and more. From the impact of technology on society to the ethical considerations of new technologies, this category covers a wide range of topics related to technology. Join the conversation and let's explore the ever-evolving world of technology together!

founded 2 years ago

Google apologizes for “missing the mark” after Gemini generated racially diverse Nazis (www.theverge.com)

submitted 9 months ago by DragonFruit@kbin.social to c/tech@kbin.social

8 comments fedilink hide all child comments

It acknowledged ‘inaccuracies’ in historical prompts.

you are viewing a single comment's thread
view the rest of the comments

[–] admiralteal@kbin.social 6 points 8 months ago (1 children)

There's two different fuckups happening here, in my opinion.

Asking an generative model to do something factually accurate is an incorrect use of the tech. That's not what they are for. You cannot expect it to give you accurate images of historical figures unless you ALSO tell it accurately what that historical figure should be.
To wit: if you want it to only generate images of white people, tell it so. This tech clearly has guardrails on it because the developers KNOW it has a highly biased training dataset that they are trying to counter. They are correct to acknowledge and try to balance this biased training dataset. That isn't a scandal, it is exactly what they should be doing.

[–] Ferk@kbin.social 1 points 8 months ago* (last edited 8 months ago)

While the result from generating an image through AI is not meant to be "factually" accurate, its seeking to be as accurate as possible when it comes to matching the prompt that is provided. And the prompt "1943 German Soldier" or "US Senator from the 1800" or "Emperor of China" has some implications in what kind of images would be expected and which kinds wouldn't. Just like how you wouldn't expect a lightsaber when asking for "medieval swords".

I'm not convinced that attempting to "balance a biased training dataset" in the way that this is apparently being done is really attainable or worthwhile.

An AI can only work based on biases, and it's impossible to correct/balance the dataset without just introducing a different bias. Because the model is just a collection of biases that discriminate between how different descriptions relate to pictures. If there was no bias for the AI to rely on, they would not be able to pick anything to show.

For example, the AI does not know whether the word "Soldier" really corresponds to someone dressed like in the picture, it's just biased to expect that. It can't tell whether an actual soldier might just be wearing pajamas or whether someone dressed in those uniforms might not be an actual soldier.

Describing a picture is, on itself, an exercise of assumptions, biases, appearances that are just based on pre-conceived notions of what are our expectations when comparing the picture to our own reality. So the AI needs to show whatever corresponds to those biases in order to match as accuratelly as possible our biased expectations for what those descriptions mean.

If the dataset is complete enough, and yet it's biased to show predominantly a particular gender or ethnicity when asking for "1943 German Soldier" because that happens to be the most common image of what a "1943 German Soldier" is, but you want a different ethnicity or gender, then add that ethnicity/gender to the prompt (like you said in the first point), instead supporting the idea of having the developers force diversity into the results in a direction that contradicts the dataset just because the results aren't politically correct. ..it would be more honest to add a disclaimer and still show the result as it is, instead of manipulating it in a direction that activelly pushes the IA to hallucinate.

Alternativelly: expand your dataset with more valuable data in a direction that does not contradict reality (eg. introduce more pictures of soldiers of different ethnics from situations that actually are found in our reality). You'll be altering the data, but you would be doing it without distorting the bias unrealistically, since they would be examples grounded in reality.