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IIRC, if you remove the 'uh' and 'hmm' from audio samples to train for speech recognition, you get lesser performance than if you leave them in. So it is not th

IIRC, if you remove the 'uh' and 'hmm' from audio samples to train for speech recognition, you get lesser performance than if you leave them in. So it is not that easy to differentiate a-priori between actual data and noise. If you look at the history of NLP we went from 'sentences are instances of a well defined grammar' to 'sentences are a bunch of statistically related tokens'. Embracing the mess is how we got better results ! And as for de-biasing gender terms, It makes the assumption that the gender differences in language only have nefarious purposes, but without an actual proof that this is the case, you may very well throw the baby with the bathwater. A good example of this is when the french government mandated that Resumes must be anonymous and not mention sex, gender, etc. It actually resulted in worse outcome for people from lower socio-economic background. It turned out that on average people were more forgiving of bad Resumes if it came from people who could be expected to be disadvantaged.

rspeer 9y ago
You've given an example of where a human decision in a data-processing pipeline harms the result. There are many examples where human decisions are a benefit. Look at every other decision in a successful speech-recognition pipeline for examples. We're not just putting raw waveforms into a black box handed to us by aliens. If you read the paper, you'll see that de-biasing is not based on assumptions, it is also based on data. Bolukbasi ran a pretty substantial crowdsourced survey to find the comparisons that word vectors make that people consider inappropriate. Don't make hollow demands for proof when you're not even aware of what's already been shown. Crowdsourcing comes with its own set of biases, of course, and we may want to revisit this data sometime, but so far this is a pretty reasonable proxy for whether an ML model will cause problems when it is deployed. And it's much better than nothing, the option that I'm struggling to understand why you prefer.
- fvdessen 9y ago
  I read the Bolukbasi paper and I now see where you are getting at. I think that the problem is not one of bias but rather that words vector embed the whole meaning regardless of the context. And that debiasing is rather stop-gap solution as there is an endless list of bias to correct depending on the usage of the model. You may have fixed PROGRAMMER - MAN + WOMAN = HOMEMAKER, and still get SOLDIER - AMERICAN + ARAB = TERRORIST, the list is endless
  - rspeer 9y ago
    Yep, so there's a lot of work to do. Bolukbasi, Cheng, et al. will straightforwardly admit that their model only applies to the one axis of gender bias. Fortunately there are other people working on this too, and I am one of them. The baked-in assumption that Arabs or Muslims are terrorists, or that terrorists are Arabs or Muslims, is something that the de-biasing process in ConceptNet Numberbatch (which I make) attempts to mitigate at the same time as gender and racial bias. And of course there is much more to do. It's a fascinating and productive field of research. Why did you have such a negative initial reaction to it?
    - fvdessen 9y ago
      Because I find in AI's candid responses to the questions we ask a fascinating opportunity to understand humanity better. When we try to conform those answers to our desired biases we learn less from it. But now I understand that such aspects are a subset of the greater problem of getting our models to answer in a specific context. Ultimately the Model would need to detect & learn the contexts by itself. And more to the point; I see you are publishing data sets after correcting them for fairness. I think the unfair results can sometimes be more useful. I once prototyped a short-story tagging & recommendation system based on word2vec and I would not be surprised if raw vectors gave better results. People's taste in litterature (especially romantic) are very dependant on gender stereotypes.

rspeer 9y ago

You've given an example of where a human decision in a data-processing pipeline harms the result. There are many examples where human decisions are a benefit. Look at every other decision in a successful speech-recognition pipeline for examples. We're not just putting raw waveforms into a black box handed to us by aliens. If you read the paper, you'll see that de-biasing is not based on assumptions, it is also based on data. Bolukbasi ran a pretty substantial crowdsourced survey to find the comparisons that word vectors make that people consider inappropriate. Don't make hollow demands for proof when you're not even aware of what's already been shown. Crowdsourcing comes with its own set of biases, of course, and we may want to revisit this data sometime, but so far this is a pretty reasonable proxy for whether an ML model will cause problems when it is deployed. And it's much better than nothing, the option that I'm struggling to understand why you prefer.

fvdessen 9y ago
I read the Bolukbasi paper and I now see where you are getting at. I think that the problem is not one of bias but rather that words vector embed the whole meaning regardless of the context. And that debiasing is rather stop-gap solution as there is an endless list of bias to correct depending on the usage of the model. You may have fixed PROGRAMMER - MAN + WOMAN = HOMEMAKER, and still get SOLDIER - AMERICAN + ARAB = TERRORIST, the list is endless
- rspeer 9y ago
  Yep, so there's a lot of work to do. Bolukbasi, Cheng, et al. will straightforwardly admit that their model only applies to the one axis of gender bias. Fortunately there are other people working on this too, and I am one of them. The baked-in assumption that Arabs or Muslims are terrorists, or that terrorists are Arabs or Muslims, is something that the de-biasing process in ConceptNet Numberbatch (which I make) attempts to mitigate at the same time as gender and racial bias. And of course there is much more to do. It's a fascinating and productive field of research. Why did you have such a negative initial reaction to it?
  - fvdessen 9y ago
    Because I find in AI's candid responses to the questions we ask a fascinating opportunity to understand humanity better. When we try to conform those answers to our desired biases we learn less from it. But now I understand that such aspects are a subset of the greater problem of getting our models to answer in a specific context. Ultimately the Model would need to detect & learn the contexts by itself. And more to the point; I see you are publishing data sets after correcting them for fairness. I think the unfair results can sometimes be more useful. I once prototyped a short-story tagging & recommendation system based on word2vec and I would not be surprised if raw vectors gave better results. People's taste in litterature (especially romantic) are very dependant on gender stereotypes.