7 ms·

You're committing a very common semantic sin (so common because many, many people don't even recognize it): substituting one meaning of "biased" for another. S

You're committing a very common semantic sin (so common because many, many people don't even recognize it): substituting one meaning of "biased" for another. Sociopolitically, "biased" in this context clearly refers to undue discrimination against people with disabilities or various other marginalized identities. The meaning of "biased" you are using ("accurately maps input to output") is perfectly correct (to the best of my understanding) within the field of ML and LLMs. The problem comes when someone comes to you saying, "ChatGPT is biased against résumés that appear disabled", clearly intending the former meaning, and you say, "It is not biased; the output is correct according to the input." Because you are using different domain-specific meanings of the same word, you are liable to each think the other is either wrong or using motivated reasoning when that's not the case.

ruined 2y ago
no assertion about this situation, but be aware that confusion is often deliberate. there is a group of people who see the regurgitation of existing systemic biases present in training data as a convenient way to legitimize and reinforce interests represented by that data. "alignment" is only a problem if you don't like what's been sampled.
- tbrownaw 2y ago
  > there is a group of people who see the regurgitation of existing systemic biases present in training data as a convenient way to legitimize and reinforce interests represented by that data. Do you have a link to someone stating that they see this as a good thing?
- danaris 2y ago
  I'm aware that there are people like this. I prefer to assume the best in people I'm actively talking to, both because I prefer to be kind, and because it cuts down on acrimonious discussions.
tbrownaw 2y ago
That "sin" can be a very useful bit of pedantry if people are talking about social/moral bias as a technical flaw in the model.

ruined 2y ago

no assertion about this situation, but be aware that confusion is often deliberate. there is a group of people who see the regurgitation of existing systemic biases present in training data as a convenient way to legitimize and reinforce interests represented by that data. "alignment" is only a problem if you don't like what's been sampled.

tbrownaw 2y ago
> there is a group of people who see the regurgitation of existing systemic biases present in training data as a convenient way to legitimize and reinforce interests represented by that data. Do you have a link to someone stating that they see this as a good thing?
danaris 2y ago
I'm aware that there are people like this. I prefer to assume the best in people I'm actively talking to, both because I prefer to be kind, and because it cuts down on acrimonious discussions.

tbrownaw 2y ago

That "sin" can be a very useful bit of pedantry if people are talking about social/moral bias as a technical flaw in the model.