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I agree, reading the article (https://syncedreview.com/2020/06/30/yann-lecun-quits-twitter-amid-acrimonious-exchanges-on-ai-bias/ https://syncedreview.com/2020/

I agree, reading the article (https://syncedreview.com/2020/06/30/yann-lecun-quits-twitter-amid-acrimonious-exchanges-on-ai-bias/ https://syncedreview.com/2020/06/30/yann-lecun-quits-twitter...) and the thread I also don't see how one puts this on Timnit. Coming back to the original tweet: if you changed the training data have more black people instead of white, would it perform the same but with inverted racial biases? Maybe? You really can't know without doing it. It might generate faces with a dark complexion but also big distortions or unrealistic colors. The original model doesn't just produce white people because of the training data, the hyperparameter tuning, perhaps the entire architecture, would have been modified until it produced acceptable outputs... using white training data. Ultimately the engineers and other humans behind the scenes are the arbiters of success, loss functions are chosen by humans, and swapping training data on the same model won't change those early decisions. In another context I'm sure LeCun could have offered his somewhat reductionist take on this example of bias (something I might have done myself – reductionism flows in technologists' veins!) A discussion could have ensued, and everyone could have come out with a better understanding. A hot Twitter thread isn't where that will happen. Neither LeCun nor Timnit have the power to change what Twitter is. LeCun (reasonably!) doesn't like the nature of the discussion, and he leaves, and I think that's OK.

m00x 6y ago
> Ultimately the engineers and other humans behind the scenes are the ultimate arbiters of success, loss functions are chosen by humans, and swapping training data on the same model won't change those early decisions How would changing loss functions alter this? This makes no sense. Hyperparameter tuning is done iteratively, and used get the best score on the test dataset. Do you really believe the engineers hand picked examples and purposefully trained it so that looked more "white" and disregarded the test scores? Training/test data is 100% the cause for this bias.
- 6gvONxR4sf7o 6y ago
  Learning conditional mean vs conditional median for superresolution might be differently affected by a large chunk of outgroup. That was what I remember folks talking about during this twitter feud. Or, like with word embeddings, where people have added some balance penalties to the loss function to make it unbiased in the presence of biased data. Dataset bias caused this, but dataset bias can sometimes be well mitigated.
- ianbicking 6y ago
  The goal is to get pleasing face reconstruction, everything follows from that. There's no objectively correct loss function, it's something that is selected because it has a good effect. A simple example could be sensitivity to color differences: with lighter skin tones there is a fairly large difference between eyebrows and eye features and the person's forehead or cheek. Someone with a pretty dark complexion might have features that are distinguished in a very compressed set of colors. Depending on the loss function a dark complexion face could be essentially flat and featureless. This could be fixed of course... but it requires changing the loss function.

m00x 6y ago

> Ultimately the engineers and other humans behind the scenes are the ultimate arbiters of success, loss functions are chosen by humans, and swapping training data on the same model won't change those early decisions How would changing loss functions alter this? This makes no sense. Hyperparameter tuning is done iteratively, and used get the best score on the test dataset. Do you really believe the engineers hand picked examples and purposefully trained it so that looked more "white" and disregarded the test scores? Training/test data is 100% the cause for this bias.

6gvONxR4sf7o 6y ago
Learning conditional mean vs conditional median for superresolution might be differently affected by a large chunk of outgroup. That was what I remember folks talking about during this twitter feud. Or, like with word embeddings, where people have added some balance penalties to the loss function to make it unbiased in the presence of biased data. Dataset bias caused this, but dataset bias can sometimes be well mitigated.
ianbicking 6y ago
The goal is to get pleasing face reconstruction, everything follows from that. There's no objectively correct loss function, it's something that is selected because it has a good effect. A simple example could be sensitivity to color differences: with lighter skin tones there is a fairly large difference between eyebrows and eye features and the person's forehead or cheek. Someone with a pretty dark complexion might have features that are distinguished in a very compressed set of colors. Depending on the loss function a dark complexion face could be essentially flat and featureless. This could be fixed of course... but it requires changing the loss function.