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> EDIT: Why is this being flagged and downvoted? If this kind of question is out of bounds when discussing this topic then why allow the topic at all? I assume

> EDIT: Why is this being flagged and downvoted? If this kind of question is out of bounds when discussing this topic then why allow the topic at all? I assume because a superficial reading of your post it appears it be in bad faith. In your first example the only "evidence" presented is racial identity. In the second, you have actual forensic evidence. The implication you created is that racial identity is evidence of a crime. I chalk it up to a misunderstanding, or such. But I know many people forget to aggressively assume good faith, and instead just angry downvote.

krageon 2y ago
It is not reasonable to assume good faith in cases where it never is. You must assume where it might be, but that is where it stops.
- lcnPylGDnU4H9OF 2y ago
  > where it never is This is precisely where the presumption of good faith works its magic. You may learn a new point of view even if you disagree with it.
  - Muromec 2y ago
    Everybody knows that viewpoint already
    - lcnPylGDnU4H9OF 2y ago
      What viewpoint? It's not until one has actually discovered this that it becomes reasonable to realize the argument is being made in bad faith. The assumption of bad faith is never helpful unless one is intending to avoid discussion.
gotoeleven 2y ago
Yeah, that was the point of the toddler example. It's very obvious the toddler covered in chocolate likely stole the fudge. My question is how does this training to remove bias not also make it worse at identifying toddler fudge thieves? This bias training afaict is literally training the LLM to not understand what likely means. In the example from the article, "C" is in my opinion not a good answer--it certainly isn't objectively correct like people are trying to assert. If I'd like my LLM to not rely on circumstantial or statistical evidence and only use hard forensic evidence to answer me, then that seems like something I should be able to ask for but making it the default mode of operation will make the answers strictly less correct.
- grayhatter 2y ago
  does it? I wouldn't expect an LLM that was trained with care to answer based on context, and to exclude bias to still be able to answer correctly when provided with context. Did I miss something and there's a reason to suspect that fine tuning to remove bias would also prevent it from predicting based on provided context? Or did you just make up that example because it might be interesting if it was true?

krageon 2y ago

It is not reasonable to assume good faith in cases where it never is. You must assume where it might be, but that is where it stops.

lcnPylGDnU4H9OF 2y ago
> where it never is This is precisely where the presumption of good faith works its magic. You may learn a new point of view even if you disagree with it.
- Muromec 2y ago
  Everybody knows that viewpoint already
  - lcnPylGDnU4H9OF 2y ago
    What viewpoint? It's not until one has actually discovered this that it becomes reasonable to realize the argument is being made in bad faith. The assumption of bad faith is never helpful unless one is intending to avoid discussion.

gotoeleven 2y ago

Yeah, that was the point of the toddler example. It's very obvious the toddler covered in chocolate likely stole the fudge. My question is how does this training to remove bias not also make it worse at identifying toddler fudge thieves? This bias training afaict is literally training the LLM to not understand what likely means. In the example from the article, "C" is in my opinion not a good answer--it certainly isn't objectively correct like people are trying to assert. If I'd like my LLM to not rely on circumstantial or statistical evidence and only use hard forensic evidence to answer me, then that seems like something I should be able to ask for but making it the default mode of operation will make the answers strictly less correct.

grayhatter 2y ago
does it? I wouldn't expect an LLM that was trained with care to answer based on context, and to exclude bias to still be able to answer correctly when provided with context. Did I miss something and there's a reason to suspect that fine tuning to remove bias would also prevent it from predicting based on provided context? Or did you just make up that example because it might be interesting if it was true?