I'm not convinced by your skepticism about correcting prejudiced bias. Debiasing certainly gives the AI a understanding of the world than the original (biased) language dataset, but it's not necessarily less complete - or less accurate. After all, any one corpus is incomplete, and has biases based on the items that were chosen for it - which are likely to reflect the biases of the past, and of the person choosing the corpus. It may not be a "complete" or "accurate" reflection of today's world - let alone the future. So it's not at all clear to me that efforts to undo the bias will necessarily make it less "accurate".
> Debiasing certainly gives the AI a understanding of the world than the original (biased) language dataset, but it's not necessarily less complete - or less accurate.
If you're translating from an ungendered language and have to choose, the only way you're going to get anything sensible is from context and common usage. Which is going to choose "she is a nurse" because an algorithm that can deduce that fathers are most likely male can also deduce that nurses are most likely female. But without that you get bad translations like "she is a father" and "he is a fine ship" and "John is her own person."
> "She is a nurse" is also not a bias. It's a prior ...
Assuming that lower-status professions are female and higher-status professions are male ("he is a doctor") when translating ungendered words is indeed a bias.
> the system will be right 93% of the time.
And "this person is a doctor, that person is a nurse" will be right 100% of the time.
It's a bias in the sense that it accurately reflects a fact you dislike. It's not a bias in the statistical sense, namely something that causes the answer to be wrong systematically in a particular direction. See my other post here discussing the distinction.
The phrase "this person is a doctor" has a different meaning than "she is a doctor" - "she" and "he" refers to (I'm probably messing up the terminology here) contextually implicit person. "This person" does not.
