Headline finding: I audited 17 AI Safety Talent programmes. Zero of 17 have published any comparison group, rejected-applicant follow-up, matched control or randomisation. Not one. Every programme that mentions a counterfactual does it by asking participants to self-report.
Background
At least $70 million...
Author: Grace Ryba (she/her), Executive Director
TL;DR:
* BluePerch is a new animal welfare grantmaker.
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In celebration of still being alive and fighting, we are giving away 1,000 Amazon e-books of “If Anyone Builds It, Everyone Dies”. Feel free to send a copy to yourself, a loved one, or a friend—we need all hands on deck.
Today marks exactly one year since If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All, by Eliezer Yudkowsky and Nate Soare...
Act utilitarians choose actions estimated to increase total happiness. Rule utilitarians follow rules estimated to increase total happiness (e.g. not lying). But you can have the best of both: act utilitarianism where rules are instead treated as moral priors. For example, having a strong prior that killing someone is bad, but which can be overridden in extreme circumstances (e.g. if killing the person ends WWII).
These priors make act utilitarianism more safeguarded against bad assessments. They are grounded in Bayesianism (moral priors are updated the same way as non-moral priors). They also decrease cognitive effort: most of the time, just follow your priors, unless the stakes and uncertainty warrant more complex consequence estimates. You can have a small prior toward inaction, so that not every random action is worth considering. You can also blend in some virtue ethics, by having a prior that virtuous acts often lead to greater total happiness in the long run.
What I described is a more Bayesian version of R. M. Hare's "Two-level utilitarianism", which involves an "intuitive" and a "critical" level of moral thinking.