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TL;DR• Much of AI safety depends on changing what governments, labs, engineers etc do. That makes it, in large part, a behaviour change problem.• There are decades of evidence on how to change behaviour, including at the level of institutions and systems, and AI safety mostly isn’t drawing on it.• Two common mistakes are relying on raising awareness (the knowledge deficit model) and assuming x-risk interventions can’t be evaluated.• The field needs social science expertise in strategy, grantmaking and evaluation, as well as routes in for social scientists who want to help.I’m a behavioural scientist. I was Deputy Director of Experimental Research at the Yale Program on Climate Change Communication, and I now lead impact measurement across 15 country offices as Head of Impact at an international NGO[1]. I’ve had a few conversations recently with (fantastic, talented!) people in AI safety, that have made me realise how much the field is missing from social science. For example, several people have repeatedly suggested the need for new interventions focused on public education. Their reasoning is that if people knew more about AI safety they would take action, and so would governments. A couple of other people were reluctant to evaluate the x-risk interventions they were working on, because they assumed that they couldn’t be evaluated. These are understandable assumptions. Conservation held them for a long time too, and social scientists have been working on both problems for decades.[2]
Currently it feels like there are three tracks in AI Safety: technical, governance, and “generalist” (which in practice means ops). I’d argue that much of AI safety depends on changing what governments, labs and publics do, and there is a whole field that studies how to do that. Irving & Askell gets cited as proof the field values social science, but it basically just described a handful of roles for computational cognitive scientists, as Vael Gates has pointed out. Emilio recently pr