Should recent ai progress change the plans of people working on global health who are focused on economic outcomes?
I think so, see here or here for a bit more discussion on this
If you think that AI will go pretty well by default (which I think many neartermists do)
My guess/âimpression is that this just hasnât been discussed by neartermists very much (which I think is one sad side-effect from bucketing all AI stuff in a âlongtermistâ worldview)
Great question!
One can claim Gift Aid on a donation to the Patient Philanthropy Fund (PPF), e.g. if donating through Giving What We Can. So a basic rate taxpayer gets a 25% âreturnâ on the initial donation (via gift aid). The fund can then be expected to make a financial return equivalent to an index fund (~10% p.a for e.g. S&P 500).
So, if you buy the claim that your expected impact will be 9x larger in 10 years than today, then a ÂŁ1,000 donation today will have an expected (mean) impact of ÂŁ11,250, for longtermist causes (ÂŁ1,000 * 1.25 * 9)[1]
Therefore I think the question of:
âdonate now and claim gift aidâ OR âinvest then donate laterâ
...can be reframed as:
âdonate now and claim gift aidâ OR âdonate to (e.g. PPF) now and claim gift aid, for the PPF to invest and then donate laterâ
(I.e. I think gift aid considerations donât favour one option over the other)
Of course, one may reasonably disagree on giving now vs giving laterâthis is a much more messy question, and one that I wonât attempt to answer here.
Iâm not sure about paying into an organisationâs fund.
I think that conditional on giving later, the PPF is a better option than individually taking an âinvesting to giveâ approach (roughly for reasons described here)
(disclaimer: I work on the operations side of the PPF)
A ÂŁ1,000 donation becomes $1,250 for a basic rate taxpayer. Over 10 years, expected impact will increase by 9x (using the Investing to Give report modelâs mean estimate)
Using the same logic for global health or animal welfare, your expected (mean) impact from a ÂŁ1,000 donation in 10 years would be ÂŁ2,625 (ÂŁ1,000 * 1.25 * 2.1x) and ÂŁ5,250 (ÂŁ1,000 * 1.25 * 4.2x).
Note however that no âPPF equivalentâ for global health or animal welfare currently exists, AFAIK
I think this could be an interesting avenue to explore. One very basic way to (very roughly) do this is to model p(doom) effectively as a discount rate. This could be an additional user input on GiveWellâs spreadsheets.
So for example, if your p(doom) is 20% in 20 years, then you could increase the discount rate by roughly 1% per year
[Techinically this will be somewhat off since (Iâm guessing) most peopleâs p(doom) doesnât increase at a constant rate, in the way a fixed discount rate does.]
Rob Besinger of MIRI tweets:
...Iâm happy to say that MIRI leadership thinks âhumanity never builds AGIâ would be the worst catastrophe in history, would cost nearly all of the futureâs value, and is basically just unacceptably bad as an option.
Just to add that the Research Institute for Future Design (RIFD) is a Founders Pledge recommendation for longtermist institutional reform
(disclaimer: I am a researcher at Founders Pledge)
OpenPhil might be in a position to expand EAâs expected impact if it added a cause area that allowed for more speculative investments in Global Health & Development.
My impression is that Open Philanthropyâs Global Health and Development team already does this? For example, OP has focus areas on Global aid policy, Scientific research and South Asian air quality, areas which are inherently risky/âuncertain.
They have also take a hit based approach philosophically, and this is what distinguishes them from GiveWellâsee e.g.
Hits. We are explicitly pursuing a hits-based approach to philanthropy with much of this work, and accordingly might expect just one or two âhitsâ from our portfolio to carry the whole. In particular, if one or two of our large science grants ended up 10x more cost-effective than GiveWellâs top charities, our portfolio to date would cumulatively come out ahead. In fact, the dollar-weighted average of the 33 BOTECs we collected above is (modestly) above the 1,000x bar, reflecting our ex ante assessment of that possibility. But the concerns about the informational value of those BOTECs remain, and most of our grants seems noticeably less likely to deliver such âhitsâ.
[Reposting my comment here from previous version]
OpenPhil might be in a position to expand EAâs expected impact if it added a cause area that allowed for more speculative investments in Global Health & Development.
My impression is that Open Philanthropyâs Global Health and Development team already does this? For example, OP has focus areas on Global aid policy, Scientific research and South Asian air quality, areas which are inherently risky/âuncertain.
They have also take a hit based approach philosophically, and this is what distinguishes them from GiveWellâsee e.g.
Hits. We are explicitly pursuing a hits-based approach to philanthropy with much of this work, and accordingly might expect just one or two âhitsâ from our portfolio to carry the whole. In particular, if one or two of our large science grants ended up 10x more cost-effective than GiveWellâs top charities, our portfolio to date would cumulatively come out ahead. In fact, the dollar-weighted average of the 33 BOTECs we collected above is (modestly) above the 1,000x bar, reflecting our ex ante assessment of that possibility. But the concerns about the informational value of those BOTECs remain, and most of our grants seems noticeably less likely to deliver such âhitsâ.
GiveWell have looked into Global Health regulationâsee more here: https://ââwww.givewell.org/ââresearch/ââpublic-health-regulation-update-August-2021
Thanks for writing this Lizka! I agree with many of the points in this [I was also a visiting fellow on the longtermist team this summer]. Iâll throw my two cents in about my own reflections (I broadly share Lizkaâs experience, so here I just highlight the upsides/âdownsides things that especially resonated with me, or things unique to my own situation):
Vague background:
Finished BSc in PPE this June
No EA research experience and very little academic research experience
Introduced to EA in 2019
Upsides:
Work in areas that are intellectually stimulating and feel meaningful (e.g. Democracy, AI Governance).
Become a better researcher. In particular, understanding reasoning transparency, reaching out to experts, the neglected virtue of scholarship, giving and receiving feedback, and being generally more productive. Of course, there is a difference between 1. Understanding these skills, and 2. internalizing & applying them, but I think RP helped substantially with the first and set me on the path to doing the second.
Working with super cool people. Everyone was super friendly, and clearly supportive of our development as researchers. I also had not written an EA forum post before RP, but was super supported to break this barrier.
Downsides:
Working remotely was super challenging for me. I underestimated how significant a factor this would be to begin with, and so I would not dismiss this lightly. However, I think there are ways that one can remedy this if they are sufficiently proactive/âagent-y (e.g. setting up in-person co-working, moving cities to be near staff, using Focusmate, etc). Also, +1 to getting a fast computer (and see Peterâs comment on this).
Imposter syndrome. One downside of working with super cool, brilliant, hard working people was (for me) a feeling that I was way out of my depth, especially to begin with. This is of course different for everyone, but one thing I struggled to fully overcome. However, RP staff are very willing to help out where they can, should this become a problem.
Ugh fields. There were definitely times when I felt somewhat overwhelmed by work, with sometimes negative spirals. This wasnât helped by personal circumstances, but my manager (Michael) was super accommodating and understanding of this, which helped alleviate guilt.
If itâs helpful, I might write-up a shortform on some of these points in more depth, especially the things I learnt about being a better researcher, if thatâs helpful for others.
Overall, I also really enjoyed my time at RP, and would highly recommend :)
(I did not speak to anyone at RP before writing this).
This is super helpful, thank you!
Which departments/âroles do you think are most important to work in from an EA perspective? The Cabinet Office, HM Treasury and FCDO seem particularly impactful, but are also the most crowded and competitive. Are there lesser known departments doing neglected but important work? (e.g. my impression is DEFRA would be this for animal welfare policyâare there similar opportunities in other cause areas?). Thanks!
This looks super interesting, thanks for posting! I especially appreciate the âHow to applyâ section
One thing Iâm interested in is seeing how this actually looks in practiceâspecifying real exogenous uncertainties (e.g. about timelines, takeoff speeds, etc), policy levers (e.g. these ideas, different AI safety research agendas, etc), relations (e.g. between AI labs, governments, etc) and performance metrics (e.g âp(doom)â, plus many of the sub-goals you outline). What are the conclusions? What would this imply about prioritization decisions? etc
I appreciate this would be super challenging, but if you are aware of any attempts to do it (even if using just a very basic, simplifying model), Iâd be curious to hear how itâs gone