I certainly wouldn’t say that all correct ideas take decades to become widely accepted. For example, often somebody proves a math theorem, and within months there’s an essentially-universal consensus that the theorem is true and the proof is correct.
Still, “bias in ML” is an interesting example. I think that in general, “discovering bias and fighting it” is a thing that everyone feels very good about doing, especially in academia and tech which tend to be politically left-wing. So the deck was stacked in its favor for it to become a popular cause to support and talk about. But that’s not what we need for AGI safety. The question is not “how soon will almost everyone be saying feel-good platitudes about AGI safety and bemoaning the lack of AGI safety?”; the question is “how soon will AGI safety be like bridge-building safety, where there are established, universally-agreed-upon, universally-followed, legally-mandated, idiot-proof best practices?”. I don’t think the “bias in ML” field is there yet. I’m not an expert, but my impression is that there is a lot of handwringing about bias in ML, and not a lot of established universal idiot-proof best practices about bias in ML. I think a lot of the discourse is bad or confused—e.g. people continue to cite the ProPublica report as a prime example of “bias in ML” despite the related impossibility theorem (see Brian Christian book chapter 2). I’m not even sure that all the currently-popular best practices are good ideas. For example, if there’s a facial recognition system that’s worse at black faces than white faces, my impression is that best practices are to diversify the training data so that it gets better at black faces. But it seems to me that facial recognition systems are just awful, because they enable mass surveillance, and the last thing we should be doing is making them better, and if they’re worse at identifying a subset of the population then maybe those people are the lucky ones.
So by my standards, “bias in ML” is still a big mess, and therefore 5ish years hasn’t been enough.
I think the ML bias folks are stuck with too hard a problem, since they’ve basically decided that all of justice can/should (or should not) be remedied through algorithms. As a result the technical folks have run into all the problems philosophy never solved, and so “policy” can only do the most obvious interventions (limit use of inaccurate facial recognition) which get total researcher consensus. (Not to mention the subfield is left-coded and thus doesn’t win the bipartisan natsec-tech crowd.) That said, 5 years was certainly enough to get their scholars heavily embedded throughout a presidential administration.
I certainly wouldn’t say that all correct ideas take decades to become widely accepted. For example, often somebody proves a math theorem, and within months there’s an essentially-universal consensus that the theorem is true and the proof is correct.
Still, “bias in ML” is an interesting example. I think that in general, “discovering bias and fighting it” is a thing that everyone feels very good about doing, especially in academia and tech which tend to be politically left-wing. So the deck was stacked in its favor for it to become a popular cause to support and talk about. But that’s not what we need for AGI safety. The question is not “how soon will almost everyone be saying feel-good platitudes about AGI safety and bemoaning the lack of AGI safety?”; the question is “how soon will AGI safety be like bridge-building safety, where there are established, universally-agreed-upon, universally-followed, legally-mandated, idiot-proof best practices?”. I don’t think the “bias in ML” field is there yet. I’m not an expert, but my impression is that there is a lot of handwringing about bias in ML, and not a lot of established universal idiot-proof best practices about bias in ML. I think a lot of the discourse is bad or confused—e.g. people continue to cite the ProPublica report as a prime example of “bias in ML” despite the related impossibility theorem (see Brian Christian book chapter 2). I’m not even sure that all the currently-popular best practices are good ideas. For example, if there’s a facial recognition system that’s worse at black faces than white faces, my impression is that best practices are to diversify the training data so that it gets better at black faces. But it seems to me that facial recognition systems are just awful, because they enable mass surveillance, and the last thing we should be doing is making them better, and if they’re worse at identifying a subset of the population then maybe those people are the lucky ones.
So by my standards, “bias in ML” is still a big mess, and therefore 5ish years hasn’t been enough.
I think the ML bias folks are stuck with too hard a problem, since they’ve basically decided that all of justice can/should (or should not) be remedied through algorithms. As a result the technical folks have run into all the problems philosophy never solved, and so “policy” can only do the most obvious interventions (limit use of inaccurate facial recognition) which get total researcher consensus. (Not to mention the subfield is left-coded and thus doesn’t win the bipartisan natsec-tech crowd.) That said, 5 years was certainly enough to get their scholars heavily embedded throughout a presidential administration.