Earlier quoted context omitted.
Ah yes. This approach to ML bias is popular in some large corporate research groups: - Say a lot of nice things about fairness - Talk about the importance of de-biasing and doing it as well as possible - Show (legitimately) that the farther downstream in your ML process you apply de-biasing, the more sound the results are - Assume that any successful ML model will be used downstream in something else - Therefore, nev…
My point is that the appropriate debiasing is task-specific. You can completely eradicate bias one one task, but as soon as someone builds on top of your results, the bias is going to creep right back in, and likely to be worse due to the information loss in previous layers. If you just hand a bias-conscious developer a bunch of debiased word vectors with the implication that it will make their model less biased, I d…
I think there's no reason to believe that bias from multiple layers of ML end up neatly rolled up into one wad of bias that can be extracted at the end of the process. If there is an empirical demonstration that this can happen, I'd love to see it.
And I think the cost of de-biasing is much lower than you think it is. I mean, I de-bias word vectors. The accuracy loss on intrinsic evaluations from doing so is tiny; it's much smaller than what you gain from easy wins that not everyone does, like improving your OOV lookup strategy (another way to improve ML results by intervening manually on your insufficent data!).
A simple example for a thought experiment: let's say you're classifying movie reviews for sentiment, so you've got a word-embedding layer (trained on general word embeddings) and a classifier layer (trained on the specific data).
The word-embedding layer will learn from corpora that it is negative to say "gay". The classifier layer will learn from the specific dataset of movie reviews that it's negative to say "Steven Seagal" (this actually happens in simple classifiers trained on the MovieLens data set). And sure, I could accept this one as objective truth, it's just an amusing and memorable example.
I think that if you save all the de-biasing for the end, you are likely to be able to fix the "Seagal" problem but not the "gay" problem, whose representation has become too complicated by going through another step of machine learning.
So I don't see the moral hazard of doing more de-biasing, as long as nobody presents it as a one-stop fix to the problem, which I sure don't.