I have an intuition that while impact measures as a way of avoiding negative side effects might work well in toy models, it will be hard or impossible to get them to work in the real world, because what counts as a negative side effect in the real world seems too complex to easily capture. It seems like AUP tries to get around this by aiming at a lower bar than “avoid negative side effects”, namely “avoid catastrophic side effects”, and aside from whether it actually succeeds at clearing this lower bar, it would mean that an AI that is only “safe” because of AUP can’t be safely used for ordinary goals (e.g., invent a better widget, or make someone personally more successful in life) and instead we have to somehow restrict them to being used just for goals that relate to x-risk reduction, where it’s worthwhile to risk incurring less-than-catastrophic negative side effects.
As a side note, it seems generally the case that some approaches to AI safety/alignment aim at the higher bar of “safe for general use” and others aim at “safe enough to use for x-risk reduction”, and this isn’t always made clear, which can be a source of confusion for both AI safety/alignment researchers and others such as strategists and policy makers.
I have an intuition that while impact measures as a way of avoiding negative side effects might work well in toy models, it will be hard or impossible to get them to work in the real world
Do you think there any experiments that could be performed that would change your view on this point? Or is an impact measure the type of thing that does not generalize well from testing environment to the real world?
I have an intuition that while impact measures as a way of avoiding negative side effects might work well in toy models, it will be hard or impossible to get them to work in the real world, because what counts as a negative side effect in the real world seems too complex to easily capture.
I have an intuition that while impact measures as a way of avoiding negative side effects might work well in toy models, it will be hard or impossible to get them to work in the real world, because what counts as a negative side effect in the real world seems too complex to easily capture. It seems like AUP tries to get around this by aiming at a lower bar than “avoid negative side effects”, namely “avoid catastrophic side effects”, and aside from whether it actually succeeds at clearing this lower bar, it would mean that an AI that is only “safe” because of AUP can’t be safely used for ordinary goals (e.g., invent a better widget, or make someone personally more successful in life) and instead we have to somehow restrict them to being used just for goals that relate to x-risk reduction, where it’s worthwhile to risk incurring less-than-catastrophic negative side effects.
As a side note, it seems generally the case that some approaches to AI safety/alignment aim at the higher bar of “safe for general use” and others aim at “safe enough to use for x-risk reduction”, and this isn’t always made clear, which can be a source of confusion for both AI safety/alignment researchers and others such as strategists and policy makers.
Do you think there any experiments that could be performed that would change your view on this point? Or is an impact measure the type of thing that does not generalize well from testing environment to the real world?
Although a far cry from “[avoiding side effects] in the real world”, see Avoiding Side Effects in Complex Environments as another piece of evidence to update on.