For previous work, I point you to @NunoSempere’s ‘Shallow evaluations of longtermist organizations,’ if you haven’t seen it already. (While Nuño didn’t focus on AI safety orgs specifically, I thought the post was excellent, and I imagine that the evaluation methods/approaches used can be learned from and applied to AI safety orgs.)
Thanks! I saw that post. It’s an excellent approach. I’m planning to do something similar, but less time-consuming and limited. The range of theories of change that are pursued in AIS is limited and can be broken down into:
Evals
Field-building
Governance
Research
Evals can be measured by quality and number of evals, relevance to ex-risks. It seems pretty straightforward to differentiate a bad eval org from a good eval org—engaging with major labs, having a lot of evals, and a relation to existential risks.
Field-building—having a lot of participants who do awesome things after the project.
Research—I argue that the number of citations is also a good proxy for the impact of a paper. It’s definitely easy to measure and is related to how much engagement a paper received. In the absence of any work done to bring the paper to the attention of key decision makers, it’s very related to the engagement.
I’m not sure how to think about governance.
Take this with a grain of salt.
EDIT: Also I think that engaging broader ML community with AI safety is extremely valuable and citations tells us how if an organization is good at that. Another thing that would be good to reivew is to ask about transparency of organizations, how thier estimate their own impact and so on—this space is really unexplored and this seems crazy to me. The amount of money that goes into AI safety is gigantic and it would be worth exploring what happens with it.
For previous work, I point you to @NunoSempere’s ‘Shallow evaluations of longtermist organizations,’ if you haven’t seen it already. (While Nuño didn’t focus on AI safety orgs specifically, I thought the post was excellent, and I imagine that the evaluation methods/approaches used can be learned from and applied to AI safety orgs.)
Thanks! I saw that post. It’s an excellent approach. I’m planning to do something similar, but less time-consuming and limited. The range of theories of change that are pursued in AIS is limited and can be broken down into:
Evals
Field-building
Governance
Research
Evals can be measured by quality and number of evals, relevance to ex-risks. It seems pretty straightforward to differentiate a bad eval org from a good eval org—engaging with major labs, having a lot of evals, and a relation to existential risks.
Field-building—having a lot of participants who do awesome things after the project.
Research—I argue that the number of citations is also a good proxy for the impact of a paper. It’s definitely easy to measure and is related to how much engagement a paper received. In the absence of any work done to bring the paper to the attention of key decision makers, it’s very related to the engagement.
I’m not sure how to think about governance.
Take this with a grain of salt.
EDIT: Also I think that engaging broader ML community with AI safety is extremely valuable and citations tells us how if an organization is good at that. Another thing that would be good to reivew is to ask about transparency of organizations, how thier estimate their own impact and so on—this space is really unexplored and this seems crazy to me. The amount of money that goes into AI safety is gigantic and it would be worth exploring what happens with it.