3 ms·

The whole point is that it never is trained on a "Senegal" dataset, is it? Why do we always see these errors where white-centric datasets produce white-centric

The whole point is that it never is trained on a "Senegal" dataset, is it? Why do we always see these errors where white-centric datasets produce white-centric results? While it would be a symmetric situation a vacuum, we do not live in a vacuum. And acknowledging dataset bias by itself doesn't address the bias meaningfully. In practice, we often treat the bias as an exogenous factor when it is not, moving it outside the scope of our responsibility. But it is very much the product of our work, a reflection of our choices, values, and beliefs about what to prioritize. We can't abdicate our responsibility for it; the stakes are too high.

6gvONxR4sf7o 6y ago
Does exogenous vs endogenous work in a vacuum either? It’s exogenous with respect to some things and endogenous with respect to others.
fullstackPM 6y ago
Well that is the whole point. The entire controversy could have been cleared up if the original researchers or even Gebru would have trained the model on a "Senegal" dataset, or a dataset diverse enough to their own liking and then make the findings public. That, to me is research. Throwing a fit on twitter because people won't buy that the outcome of this model is racist is not research. I am not sure why the wording around this has to be so abstract.
visarga 6y ago
This is a great goal but the time and place for it is not in a super-resolution paper. There are specific conferences, benchmarks and projects where it can be achieved with more efficiency.
PeterisP 6y ago
The whole point is that working on more accurate methods for doing X is one direction of research and working on methods to correct for dataset bias in X is another direction of research. It's completely reasonable to work on either one of those separately, and it's unreasonable to demand (you may suggest, but not demand) that people working on the former direction fix the latter. Better algorithms and better datasets are both necessary but quite orthogonal. As you say, it's a reflection of our beliefs about what to prioritize. If you're interested in developing methods of image upscaling that generalize better and preserve facial properties that were underrepresented in the training data, that's an interesting area of research, and you're welcome to work on it, but you don't get to demand that people working on something else prioritize this instead, they get to choose their own priorities unless you're paying them for a particular direction of research (e.g. like Google should be able to direct Dr. Gebru while she was working for them). There's a responsibility to correct for bias when implementing models in production (e.g. if you'd be actually deploying it in Senegal, then there would be a responsibility to use a Senegal-appropriate dataset), there's a responsibility to acknowledge the bias of a particular algorithm if one exists - for example if it highly relies on contrast values which would be different for different types of faces, then that's relevant, because it's a statement about the generalization of the algorithm; but there's no proactive duty to "address the bias meaningfully", that's nice thing to do, but it's like charity - a voluntary choice to factilitate a social goal, but not a requirement or responsibility to do that. There's a responsibility to correct harms you caused, there is no responsibility to correct harms caused by others, that's a good thing to do, but not a moral duty. Someone who invests a lot into charity work or addressing bias is doing a good thing, but it doesn't mean that people who are doing other things are abdicating their responsibility - it's never was their responsibility in the first place to fix social issues in the wider society. You can't simply point at random people and declare that they're going to be responsible for something that they didn't personally cause and where they did nothing wrong, that accusatory behavior is unacceptable, so naturally there's a backlash to people who try to assert personal responsibility of others without a basis to do so.

6gvONxR4sf7o 6y ago

Does exogenous vs endogenous work in a vacuum either? It’s exogenous with respect to some things and endogenous with respect to others.

fullstackPM 6y ago

Well that is the whole point. The entire controversy could have been cleared up if the original researchers or even Gebru would have trained the model on a "Senegal" dataset, or a dataset diverse enough to their own liking and then make the findings public. That, to me is research. Throwing a fit on twitter because people won't buy that the outcome of this model is racist is not research. I am not sure why the wording around this has to be so abstract.

visarga 6y ago

This is a great goal but the time and place for it is not in a super-resolution paper. There are specific conferences, benchmarks and projects where it can be achieved with more efficiency.

PeterisP 6y ago

The whole point is that working on more accurate methods for doing X is one direction of research and working on methods to correct for dataset bias in X is another direction of research. It's completely reasonable to work on either one of those separately, and it's unreasonable to demand (you may suggest, but not demand) that people working on the former direction fix the latter. Better algorithms and better datasets are both necessary but quite orthogonal. As you say, it's a reflection of our beliefs about what to prioritize. If you're interested in developing methods of image upscaling that generalize better and preserve facial properties that were underrepresented in the training data, that's an interesting area of research, and you're welcome to work on it, but you don't get to demand that people working on something else prioritize this instead, they get to choose their own priorities unless you're paying them for a particular direction of research (e.g. like Google should be able to direct Dr. Gebru while she was working for them). There's a responsibility to correct for bias when implementing models in production (e.g. if you'd be actually deploying it in Senegal, then there would be a responsibility to use a Senegal-appropriate dataset), there's a responsibility to acknowledge the bias of a particular algorithm if one exists - for example if it highly relies on contrast values which would be different for different types of faces, then that's relevant, because it's a statement about the generalization of the algorithm; but there's no proactive duty to "address the bias meaningfully", that's nice thing to do, but it's like charity - a voluntary choice to factilitate a social goal, but not a requirement or responsibility to do that. There's a responsibility to correct harms you caused, there is no responsibility to correct harms caused by others, that's a good thing to do, but not a moral duty. Someone who invests a lot into charity work or addressing bias is doing a good thing, but it doesn't mean that people who are doing other things are abdicating their responsibility - it's never was their responsibility in the first place to fix social issues in the wider society. You can't simply point at random people and declare that they're going to be responsible for something that they didn't personally cause and where they did nothing wrong, that accusatory behavior is unacceptable, so naturally there's a backlash to people who try to assert personal responsibility of others without a basis to do so.