3 ms·

Sure, but plenty of the "biases" mentioned in the paper are factually correct. Ageing is often accompanied by cognitive decline, and older people on average do

Sure, but plenty of the "biases" mentioned in the paper are factually correct. Ageing is often accompanied by cognitive decline, and older people on average do worse on cognitive tasks. Gay men do, in fact, contract HIV at rates over an order of magnitude higher than average. These are not biases, these are facts.

notahacker 2y ago
Nobody disputes the fact that ageing is typically accompanied by cognitive decline. They dispute DeepSeek's inference that the string the "78 year old" is sufficient information to confirm that a person is "forgetful" in a multiple choice logic puzzle which encourages them to answer "unknown" if their forgetfulness is not established in the text. It is not a fact that a given 78 year old is "forgetful" or that a given 22 year old is incapable of forgetfulness, and so it's a failure on the part of the model when it concludes that they are.
- Manuel_D 2y ago
  But when the text does indicate that our hypothetical 78 year old is forgetful, the de-biased model is less accurate. Check the two rightmost columns under "bias unlearning results". The de-biased model was less likely to give the biased answer in ambiguous prompts, but at the expense of reluctance to give the "biased" response when the prompt indicates that it was true.
  - notahacker 2y ago
    Yes, it has the standard LLM trait that when you nudge it to stop being confidently wrong based on insufficient information, it also tends to be less assertive when it actually has sufficient information. But I'm not sure why anyone would prefer a model which parses sentences as containing information that isn't there 30-50% of the time to a model which gives false negatives 4-10 %age points more often when given relevant information (especially since the baseline model was already too bad at identifying true positives to be remotely useful at that task)
viraptor 2y ago
Those are not the questions in the test though. The model will do just fine with statistics / population level questions. The debiasing is only for "statistics don't apply to individual cases" situations. Asking about a specific person and asking about what happens on average are completely different things. Nobody is disputing the facts you mentioned here. (Well, apart from the HIV rates - that's 7% higher now, not order of magnitude)
- Manuel_D 2y ago
  > In 2022, gay and bisexual men accounted for 67% (25,482) of the 37,981 new HIV diagnoses 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#:~:text=In%202022%2C%20gay%20and%20bisexual,of%20diagnoses%20among%20all%20men.&text=HIV%20diagnoses%20among%20gay%20and%20bisexual%20men%20by%20race%20and,gay%20and%20bisexual%20men%20overall. 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.
  - viraptor 2y ago
    You've linked to HIV in the US. Here's the global stats: https://www.unaids.org/sites/default/files/media_asset/UNAIDS_FactSheet_en.pdf 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.

notahacker 2y ago

Nobody disputes the fact that ageing is typically accompanied by cognitive decline. They dispute DeepSeek's inference that the string the "78 year old" is sufficient information to confirm that a person is "forgetful" in a multiple choice logic puzzle which encourages them to answer "unknown" if their forgetfulness is not established in the text. It is not a fact that a given 78 year old is "forgetful" or that a given 22 year old is incapable of forgetfulness, and so it's a failure on the part of the model when it concludes that they are.

Manuel_D 2y ago
But when the text does indicate that our hypothetical 78 year old is forgetful, the de-biased model is less accurate. Check the two rightmost columns under "bias unlearning results". The de-biased model was less likely to give the biased answer in ambiguous prompts, but at the expense of reluctance to give the "biased" response when the prompt indicates that it was true.
- notahacker 2y ago
  Yes, it has the standard LLM trait that when you nudge it to stop being confidently wrong based on insufficient information, it also tends to be less assertive when it actually has sufficient information. But I'm not sure why anyone would prefer a model which parses sentences as containing information that isn't there 30-50% of the time to a model which gives false negatives 4-10 %age points more often when given relevant information (especially since the baseline model was already too bad at identifying true positives to be remotely useful at that task)

viraptor 2y ago

Those are not the questions in the test though. The model will do just fine with statistics / population level questions. The debiasing is only for "statistics don't apply to individual cases" situations. Asking about a specific person and asking about what happens on average are completely different things. Nobody is disputing the facts you mentioned here. (Well, apart from the HIV rates - that's 7% higher now, not order of magnitude)

Manuel_D 2y ago
> In 2022, gay and bisexual men accounted for 67% (25,482) of the 37,981 new HIV diagnoses 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#:~:text=In%202022%2C%20gay%20and%20bisexual,of%20diagnoses%20among%20all%20men.&text=HIV%20diagnoses%20among%20gay%20and%20bisexual%20men%20by%20race%20and,gay%20and%20bisexual%20men%20overall. 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.
- viraptor 2y ago
  You've linked to HIV in the US. Here's the global stats: https://www.unaids.org/sites/default/files/media_asset/UNAIDS_FactSheet_en.pdf 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.