Debiasing word vectors is not guaranteed to yield less discriminatory results in every application. In many languages, gender is expressed grammatically and there are different words for e.g. male and female scientists. If you debias the word vectors to remove associations with gender, it becomes impossible to distinguish those two. Now if you translate between two languages with that distinction, the model will have to fix itself to one of the two possibilities, and the translation error will be minimized by always picking the more common form. "First year of all-female Nobel prize winners and machine translation gets it totally wrong!" By debiasing the word vectors, the model has become more biased and less accurate overall.

If you create a discriminatory machine learning model, it's unlikely to be because you didn't debias your input representation. More often, you're training it on a task whose real-world statistics are biased, and the model learns to accurately reflect that bias to solve your task. The solution to that is not to modify the input, but rather the output you expect the model to provide.

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, never de-bias anything, except the final output of something where your company benefits from showing fairness: that is, a demonstration at a conference talk about fairness

- The time to de-bias real applications is always "later" and everyone can say they are working on doing the right thing

People who release data artifacts have the ability to de-bias now instead of later. It's not perfect, but it is good. If you rely on the developer after you to do the de-biasing that you're not doing, you're ensuring that de-biasing won't happen, because they won't do it either.

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 don't think that's really helpful. Like security, bias in machine learning needs to be addressed on the level of complete systems, it's not something you can leave to some simple input sanitizer.

Ah. In that case, I agree with where you're coming from, but not on the details:

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.

That's because what's called 'debiasing' is not actually removing bias but adding another opposite one at the superficial layers we can actually notice.

That's possibly a fair description of the kind of as-late-as-possible de-biasing that I find insufficient.

I think there is not nearly enough research into de-biasing at training time, perhaps because it would be discouraged by the reward structure of our field.

("You mean I can make my model more complex and my paper longer, and in return I lose the 0.15% accuracy gain that's the entire reason I could call the result 'state of the art'? Sure, I'll get right on that!")