And back to the topic at hand, the de-biased model was less accurate when given unambiguous prompts. In order to avoid being perceived as bias, the de-biased model was less like to say that an elderly person was forgetful even when the prompt unambiguously indicates that the elderly person was forgetful. This is covered in the "Bias Unlearning Results" section. They made the model less likely to give the "biased" answer, even when the prompt indicated that it was the correct answer.
You've linked to HIV in the US. Here's the global stats: https://www.unaids.org/sites/default/files/media_asset/UNAID... Turns out context matters - otherwise the general statement is biased on the specific country's situation and seems to put more weight on the sexuality than necessary. (I.e. the difference is more about frequency/partners/protection than about being gay, they're just correlated in the US)
> the de-biased model was less accurate when given unambiguous prompts.
Correct. And that's not what I wrote about. These are not questions about population, but specific cases and yes, we should try to maximise accuracy while we minimise bias.
MSM make up ~5% of the population but ~2/3rds of HIV diagnoses. Yes, this is an order of magnitude disparity in diagnoses.
https://www.cdc.gov/hiv/data-research/facts-stats/index.html...
And back to the topic at hand, the de-biased model was less accurate when given unambiguous prompts. In order to avoid being perceived as bias, the de-biased model was less like to say that an elderly person was forgetful even when the prompt unambiguously indicates that the elderly person was forgetful. This is covered in the "Bias Unlearning Results" section. They made the model less likely to give the "biased" answer, even when the prompt indicated that it was the correct answer.