Comment on To explore AI bias, researchers pose a question: How do you imagine a tree?
merde@sh.itjust.works 2 days ago
Now imagine how you might prompt an LLM like ChatGPT to give you a picture of your tree. When Stanford computer science PhD candidate Nava Haghighi, the lead author of the new study, asked ChatGPT to make her a picture of a tree, ChatGPT returned a solitary trunk with sprawling branches – not the image of a tree with roots she envisioned.
she needs to get out and draw/paint some trees.
Eq0@literature.cafe 2 days ago
Did you read the rest of the article? The tree drawing was just the triggering element to an evaluation of the AI capabilities, in particular underlining how “tree” (bit also “human”, “success”, “importance”) are being strongly restricted in their meaning by the AI itself, without the user noticing it. Thus, a user receives an answer that has already undergone a filtering of sorts. Not being aware of this risks limiting our understanding of AI and increasing its damage.
Theoretical research in AI is both necessary and hard at the moment, with funding being giving more to new results over the understanding of the properties of old ones.
merde@sh.itjust.works 2 days ago
yes, i did. Can i comment on just this part?
“without the user noticing it” is where i disagree. When you work with ai you encounter all kinds of limitations (and bias too).
Can you see the bias cameras too intrinsically have? They too never photograph roots unless we uncover the roots and direct the camera at them.
Eq0@literature.cafe 2 days ago
AI is getting a much more widespread use than people with a technical background. So its application, namely in education but in all other non-CS disciplines will be through people with limited understanding of the biases. It is importing them to make them explicit, to underline that an LLM will produce the same biases it deduced from testing data and its loss function. But lots functions and test data are not public knowledge, studies need to be performed to understand how the coders’ own biases influenced the LLM scheme itself.
A photo has less bias because we know what it is representing: a photo only shows what can be seen. But the same understanding is not clear AI. Why showing a photo-realistic tree versus a biological diagram? Choices have been made, of which a broader audience needs to be aware of.
merde@sh.itjust.works 2 days ago
i agree with you on ai but the above statement is ignoring what photography is and biases intrinsic to it.
You see, that understanding you expect to be developed for ai is not there for you for photography.
vacuumflower@lemmy.sdf.org 2 days ago
Wouldn’t this be a predictable trait of a system predicting next most likely token based on lossy compression of specific datasets and other lossy optimization?
Eq0@literature.cafe 2 days ago
Depends. For an expert, that is self evident (even if it might not be clear which biases have been incorporated). But that is not how it has been marketed. Chatgpt and similar are perceived as answering “the truth” at all times, and that skews the user’s understanding of the answers. Researching how deeply the answers are affected by the coders’ bias is the focus of their research and a worthwhile undertaking to avoid overlooking something important
spankmonkey@lemmy.world 1 day ago
I am far from an expert, but it seemed obvious to ne.