Looking back, I think this part of my first comment was poorly worded: > I imagine that scientists will soon have the ability to be unusually transparent and provide incredibly low rates of fraud/bias, using AI.
I meant > I imagine that scientists will [soon have the ability to] be unusually transparent and provide incredibly low rates of fraud/bias], using AI.
So it’s not that this will lead to low rates of fraud/bias, but that AI will help enable that for scientists willing to go along with it—but at the same time, there’s a separate question of if scientists are willing to go along with it.
But I think even that probably is not fair. A a better description of my beliefs is something like,
I think that LLM auditing tools could be useful for some kinds of scientific research for communities open to them.
I think in the short-term, sufficiently-motivated groups could develop these tools and use them to help decrease the levels of statistical and algorithmic accidents that happen. Correspondingly, I’d expect this to help with fraud.
In the long-run, whenever AI approaches human-level intelligence (which I think will likely happen in the next 20 years, but I realize others disagree), I expect that more and more of the scientific process will be automated. I think there are ways this could go very well using things like AI auditing, whereby the results will be much more reliable than those currently made by humans. There are of course also worlds in which humans do dumb things with the AIs and the opposite happens.
I think that at least, AI safety researchers should consider using these kinds of methods, and that the AI safety landscape should investigate efforts to make decent auditing tools.”
My core hope with the original message is to draw attention to AI science auditing tools as things that might be interesting/useful, not to claim that they’re definitely a major game changer.
This context is useful, thanks.
Looking back, I think this part of my first comment was poorly worded:
> I imagine that scientists will soon have the ability to be unusually transparent and provide incredibly low rates of fraud/bias, using AI.
I meant
> I imagine that scientists will [soon have the ability to] be unusually transparent and provide incredibly low rates of fraud/bias], using AI.
So it’s not that this will lead to low rates of fraud/bias, but that AI will help enable that for scientists willing to go along with it—but at the same time, there’s a separate question of if scientists are willing to go along with it.
But I think even that probably is not fair. A a better description of my beliefs is something like,
I think that LLM auditing tools could be useful for some kinds of scientific research for communities open to them.
I think in the short-term, sufficiently-motivated groups could develop these tools and use them to help decrease the levels of statistical and algorithmic accidents that happen. Correspondingly, I’d expect this to help with fraud.
In the long-run, whenever AI approaches human-level intelligence (which I think will likely happen in the next 20 years, but I realize others disagree), I expect that more and more of the scientific process will be automated. I think there are ways this could go very well using things like AI auditing, whereby the results will be much more reliable than those currently made by humans. There are of course also worlds in which humans do dumb things with the AIs and the opposite happens.
I think that at least, AI safety researchers should consider using these kinds of methods, and that the AI safety landscape should investigate efforts to make decent auditing tools.”
My core hope with the original message is to draw attention to AI science auditing tools as things that might be interesting/useful, not to claim that they’re definitely a major game changer.