5 ms·

If you ask Stable Diffusion for a picture of a cat it always seems to produce images of healthy looking domestic cats. For the prompt "cat" to be unbiased Stabl

by kypro 3y ago

If you ask Stable Diffusion for a picture of a cat it always seems to produce images of healthy looking domestic cats. For the prompt "cat" to be unbiased Stable Diffusion would need to occasionally generate images of dead white tigers since this would also fit under the label of "cat". I'm being absurd of course, but my point is even if you assume the results are biased we also need to consider that generally people want the biased result. For example in the UK our media vastly over represents black individuals. But we do this purposefully because companies want to be inclusive so even if it's not representative of reality we still want to include a variety of races. So is it even bad that Stable Diffusion seems to have a bias towards images of healthy cats rather than sick or dead cats? The answer here if anything is to reduce undesirable human bias and allow RLHF to do its job.

croes 3y ago
Does is always show the same breed? Healthy vs sick/dead isn't biased.
dragonwriter 3y ago
I think the correct position on AI bias is that it is important to have biases, to the extent possible, known, disclosed, and vetted for appropriateness to the application domain (including mitigation in other parts of the system than the model in which the bias conpared to the desired state exists), not to eliminate them, which is impossible, becauase bias is always relative to a particular reference, and there is no one true authoritative reference for evaluating bias (which is why to document bias, you have to document what it is biased compared to.)
- nonethewiser 3y ago
  So to inject preferred biases instead of whatever is embedded in the data.
  - dragonwriter 3y ago
    All biases are injected by humans, whether in curation of training data or at other points; training data isn’t a naturally occurring collection without human influence (not that it would really matter if it was.) As with any tool, the issue is fitness for purpose, and as with anything being supplied by a party with potentially different interests than the user/consumer, and important part of that is disclosure/transparency of relevant information that may not be apparent on the surface.
    - 90-00-09 3y ago
      This is an obvious trap as biases are individual but you are proposing (or at least hinting at) injecting biases into a universally used model. Or should the ultimate goal be injecting hyper local biases and then selecting the right bias group based on the location of the prompter (or even their race/gender/etc)?
bradley13 3y ago
Exactly this. Generative AI shows that most people doing technical work are men? It probably also shows that most construction workers are men, most social workers are women, etc.. Guess what, that reflects reality. If you want something else? You can ask for it. "Picture of a woman welding." "Picture of a black, male social worker." You'll get it, no problem. Your comment about UK media is spot on: DEI fans want to bend reality to match their ideals. It's annoying and arguably counterproductive.
- space_fountain 3y ago
  The article compared stable diffusion to job statistics. It was bad even if you controlled for that. I think this might be representative of some of the pushback to DEI. People can feel like minorities are being pushed on them even when they aren’t.
alpinisme 3y ago
The difference of course is that while we roughly mean to ask for a “normal instance,” (and only occasionally for a “representative” instance strictly speaking), there are some circumstances where we disagree about what “normal” is or (crucially) should be. When we ask each other questions, we expect not only a factually but a normatively correct answer as well. And norms are inherently open to dispute as part of the normal course of conversation. But not so with generative ai. Yes, you can correct the ai during your session. But when I disagree with you in conversation I hope that my disagreement (if we resolve the matter) will outlive our interaction and you will no longer say the thing I objected to when talking to others, or if you do, you’ll do it in a different manner with a different tone.
t0bia_s 3y ago
Try google "white couple" in google images. Over half of results are not "white couple".

by kypro 3y ago

croes 3y ago

Does is always show the same breed? Healthy vs sick/dead isn't biased.

dragonwriter 3y ago

I think the correct position on AI bias is that it is important to have biases, to the extent possible, known, disclosed, and vetted for appropriateness to the application domain (including mitigation in other parts of the system than the model in which the bias conpared to the desired state exists), not to eliminate them, which is impossible, becauase bias is always relative to a particular reference, and there is no one true authoritative reference for evaluating bias (which is why to document bias, you have to document what it is biased compared to.)

nonethewiser 3y ago
So to inject preferred biases instead of whatever is embedded in the data.
- dragonwriter 3y ago
  All biases are injected by humans, whether in curation of training data or at other points; training data isn’t a naturally occurring collection without human influence (not that it would really matter if it was.) As with any tool, the issue is fitness for purpose, and as with anything being supplied by a party with potentially different interests than the user/consumer, and important part of that is disclosure/transparency of relevant information that may not be apparent on the surface.
  - 90-00-09 3y ago
    This is an obvious trap as biases are individual but you are proposing (or at least hinting at) injecting biases into a universally used model. Or should the ultimate goal be injecting hyper local biases and then selecting the right bias group based on the location of the prompter (or even their race/gender/etc)?

bradley13 3y ago

Exactly this. Generative AI shows that most people doing technical work are men? It probably also shows that most construction workers are men, most social workers are women, etc.. Guess what, that reflects reality. If you want something else? You can ask for it. "Picture of a woman welding." "Picture of a black, male social worker." You'll get it, no problem. Your comment about UK media is spot on: DEI fans want to bend reality to match their ideals. It's annoying and arguably counterproductive.

space_fountain 3y ago
The article compared stable diffusion to job statistics. It was bad even if you controlled for that. I think this might be representative of some of the pushback to DEI. People can feel like minorities are being pushed on them even when they aren’t.

alpinisme 3y ago

The difference of course is that while we roughly mean to ask for a “normal instance,” (and only occasionally for a “representative” instance strictly speaking), there are some circumstances where we disagree about what “normal” is or (crucially) should be. When we ask each other questions, we expect not only a factually but a normatively correct answer as well. And norms are inherently open to dispute as part of the normal course of conversation. But not so with generative ai. Yes, you can correct the ai during your session. But when I disagree with you in conversation I hope that my disagreement (if we resolve the matter) will outlive our interaction and you will no longer say the thing I objected to when talking to others, or if you do, you’ll do it in a different manner with a different tone.

t0bia_s 3y ago

Try google "white couple" in google images. Over half of results are not "white couple".