At first I thought "What's the big deal? You also remove the query from recommender systems, for example. It's an obvious uninteresting result!".
But then I read the linked article [Nissim 2019] and it all became much clearer with te example in the title: "Man is to Doctor as Woman is to Doctor". If you remove "Doctor" from the results, you'll get "Nurse" instead, not because the dataset/society/... is biased, but because you inserted the bias in your model!
With this small "optimization" (and others presented in the article, such as the "threshold-method" and hand-picked results from the Top-N words), it's trivial to use the anology method to show that any dataset is biased, since you are filtering the unbiased results!
One a side note, I wish articles like this were more popular. I find that it's really easy to use AI techniques "the wrong way" and there's are a lack of articles pointing to common pitfalls (and, to make things worse, there are a lot of blog posts doing things wrong, which only validates bad methodology).
[Nissim 2019]: https://arxiv.org/pdf/1905.09866.pdf
But then I read the linked article [Nissim 2019] and it all became much clearer with te example in the title: "Man is to Doctor as Woman is to Doctor". If you remove "Doctor" from the results, you'll get "Nurse" instead, not because the dataset/society/... is biased, but because you inserted the bias in your model!
With this small "optimization" (and others presented in the article, such as the "threshold-method" and hand-picked results from the Top-N words), it's trivial to use the anology method to show that any dataset is biased, since you are filtering the unbiased results!
One a side note, I wish articles like this were more popular. I find that it's really easy to use AI techniques "the wrong way" and there's are a lack of articles pointing to common pitfalls (and, to make things worse, there are a lot of blog posts doing things wrong, which only validates bad methodology).
[Nissim 2019]: https://arxiv.org/pdf/1905.09866.pdf