It's in the same bucket as "Affirmative Action" and "positive discrimination." Euphemisms to express that one likes this particular discrimination. To better describe the action, drop your own point of view and just say "bias" instead of "debias."
Saying biasing implies infinite possibilities to which the data can be made biased towards. It instantly raises the question why bias towards this and not something else. It almost sounds like a bad thing.
Saying debiasing implies there is a correct result which needs to be achieved by removing bias. It raises no questions, we want correct, we don’t want incorrect. Doing a good thing implied.
Don’t misinterpret me, I don’t think public models should spew commonly harmful content out of the box. Just explaining the PR trick, which is what the word “de”biasing de-facto is in this context.
> Unless you think there is such a thing as an objectively neutral position
I do. Why, you don't? There are as much as possible objective assessments of complex things. Then, there are possible sets of assumption that can be applied to those objective assessments. All of those can be put on the analytic table.
This is an extremely broad question so I'll limit my reply to the current context.
What would an "objective neutral AI model" look like?
The training data itself is just a snapshot of the internet. Is this "neutral"? It depends on your goals but any AI trained on this dataset is skewed towards a few clusters. In some cases you get something that merely approximates a Reddit or 4chan simulator. If that's what you want - then great but you can see why some people would want to "debias" that outcome!
You might argue the "world as it truly exists" is the correct target. But bear in mind we are talking about human culture - not physics and chemistry - you're going to struggle to get both consensus and any sane methodology for getting that into an AI.
I'm simply saying we are being asked to choose the bias we prefer. However one choice might be "more biased" (despite this concept itself throwing up more questions than it answers).
Given a biased corpus, de-biasing is the process of ensuring a less biased outcome. We can measure bias fairly well, so it seems absurd to conflate the two by suggesting that unbiased behaviour is simply another form of biased behaviour. For all practical purposes, there is a difference.
Surely the two are synonyms? Unless you think there is such a thing as an objectively neutral position?
It's in the same bucket as "Affirmative Action" and "positive discrimination." Euphemisms to express that one likes this particular discrimination. To better describe the action, drop your own point of view and just say "bias" instead of "debias."
Saying biasing implies infinite possibilities to which the data can be made biased towards. It instantly raises the question why bias towards this and not something else. It almost sounds like a bad thing.
Saying debiasing implies there is a correct result which needs to be achieved by removing bias. It raises no questions, we want correct, we don’t want incorrect. Doing a good thing implied.
Don’t misinterpret me, I don’t think public models should spew commonly harmful content out of the box. Just explaining the PR trick, which is what the word “de”biasing de-facto is in this context.
My position is clearly the rational neutral position. Duh.
> Unless you think there is such a thing as an objectively neutral position
I do. Why, you don't? There are as much as possible objective assessments of complex things. Then, there are possible sets of assumption that can be applied to those objective assessments. All of those can be put on the analytic table.
This is an extremely broad question so I'll limit my reply to the current context.
What would an "objective neutral AI model" look like?
The training data itself is just a snapshot of the internet. Is this "neutral"? It depends on your goals but any AI trained on this dataset is skewed towards a few clusters. In some cases you get something that merely approximates a Reddit or 4chan simulator. If that's what you want - then great but you can see why some people would want to "debias" that outcome!
You might argue the "world as it truly exists" is the correct target. But bear in mind we are talking about human culture - not physics and chemistry - you're going to struggle to get both consensus and any sane methodology for getting that into an AI.
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Isn't that the point? "Debias" implies there IS an objectively neutral position and that that AI safety can take us there.
I'm simply saying we are being asked to choose the bias we prefer. However one choice might be "more biased" (despite this concept itself throwing up more questions than it answers).
Given a biased corpus, de-biasing is the process of ensuring a less biased outcome. We can measure bias fairly well, so it seems absurd to conflate the two by suggesting that unbiased behaviour is simply another form of biased behaviour. For all practical purposes, there is a difference.
> Given a biased corpus, de-biasing is the process of ensuring a less biased outcome.
The point is that people who evaluate what is considered bias are, in and of themselves, introducing bias.
> We can measure bias fairly well...
Really? What's the absolute standard that you are measuring that bias against?
Citation needed.
If you think that the output of current LLM is the ground truth, then yes, what are they doing is biasing.
Bias tilting.
The opposite direction is "checking and reasoning".