quinngrace
Data Scientist @ Sand Technologies
Healthcare Data Scientist working on novel applications of AI in developing countries, with a special focus on Rural Healthtech. Heard about EA over a decade ago when I witnessed as it first took root in Africa. Became active in 2024 after reading through the forum to see if there was a solution to an issue I kept running into.
(Emphasis mine)
How many AI experts did you interview? How about AIxBio experts?
Thank you for sharing your experiences! This reminds me of the cleanroom attempt I had on the backburner. Time to bring it out again. Similar idea but the twist is common household items in South Africa. It's forced me to think through the problem from first principles, as that list is extremely varied but narrow. Also, I have seen discussions of using roof insulation. There's too much asbestos here for that to even be thinkable as an experiment.
Beautiful. I'd go further: it's not just moral progress that requires considering strange ideas, but progress of any kind. Applying the same thinking to the same context tends to produce the same result. By definition, anything that departs from the status quo looks "strange", at least at first. Not every unusual idea is right, which is why the "tempered with common sense" part matters, but dismissing ideas for sounding odd means trading our binoculars for a rifle.
Food for thought. I learnt that the most essential person in a system wasn't necessarily the leader, it was whoever was preventing it from breaking today. This person is often the invisible glue that keeps everything together. But how do you measure that or even detect it as an outsider?
I want to write something expanding more fully on what we decide is worth measuring and how that impacts decisions.
A simulation tank for topics is pretty nifty
Thank you for raising this question, it is certainly not insensitive. Feel free to ask more questions.
I am also wondering how switching from QALYs would change EA priorities. My guess is that it depends entirely on the weights in the model. I want to do some comparisons with different alternatives to see how they would inform priorities. Some alternatives I would like to test are:
from the studies mentioned in this article. I'm not very clued up on alternative CEA measurements, so I was hoping someone more knowledgeable would mention an alternative.
Once I have done that analysis, I'll post a follow up to this post and that would clear up that confusion. It's not something that will happen quickly though.
The point of this post was to explain gaps in current measurements of health outcomes from the point of view of the tangible day-to-day effects, as well as how what is being measured often doesn't match reality. I'm not an expert in mathematical models or health economics but I am an expert in being chronically ill, so that's the lens I was offering.
One guess is that doing away with negative QALYs would mess with animal welfare calculations because a lot of them rely on negative QALYs. However, if I play devil's advocate, it could be argued that animals should get a very high weight in terms of historical disenfranchisement, in which case, the calculations would change but I suspect animal welfare would still be one of the top issues.
Love hearing stories like this! I went through a similar journey both mentally and physically and realised sometimes the professionals don't know how to solve my issues and then I have to jerryrig my own solution (with a doctor's oversight).
Great stuff! Love the idea of throwing red herrings (fake options) in to compare