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.
If you want, any work that does not encompass the whole world is applying a filter and therefore a bias of some sort. We don’t expect a photo to X-ray the roots of a tree, because we understand the physical constraints of photography. Sure, something could be just out of frame, something else could have been photoshopped out, you can create a different story by selecting different photos and so on. But we understand the “what” a photo represents. I doubt we have the dang understanding of “what” an LLM represents, what are the constraints of the possible answers, and we definitely don’t understand why a specific answer is chosen over the infinite other possibilities.
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).
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.
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.
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.
If you want, any work that does not encompass the whole world is applying a filter and therefore a bias of some sort. We don’t expect a photo to X-ray the roots of a tree, because we understand the physical constraints of photography. Sure, something could be just out of frame, something else could have been photoshopped out, you can create a different story by selecting different photos and so on. But we understand the “what” a photo represents. I doubt we have the dang understanding of “what” an LLM represents, what are the constraints of the possible answers, and we definitely don’t understand why a specific answer is chosen over the infinite other possibilities.