These insights are based on 102 former advisees, including 43 women.
Their expressed interest when they applied for our services are as follows:
44 Global health & development
38 AI safety
22 Animal welfare
17 biorisks prevention
2 nuclear arms risk prevention
11 climate change mitigation
Note that these are not mutually exclusive as people can express interest in multiple cause areas when applying. (Later, they engage in cause prioritization as part of our program.)
In terms of our own MLE, we had in-depth interviews with 24 past advisees and have a bi-annual anonymous survey. We have occasionally run surveys to gain insights on specific things (e.g., recently, on how to make EA community building events more inclusive of mid-career people).
Appreciate this update. I’d love to know more about what data you’re drawing from in Section 3 to draw your lessons learnt e.g.
How many people total did you draw these insights from, and what was the spread of cause areas & geographies?
Did you do research outside of what knowledge you gained through career advising /​ interviews (e.g. did you run surveys)?
Thanks for the questions, Vaidehi.
These insights are based on 102 former advisees, including 43 women.
Their expressed interest when they applied for our services are as follows:
44 Global health & development
38 AI safety
22 Animal welfare
17 biorisks prevention
2 nuclear arms risk prevention
11 climate change mitigation
Note that these are not mutually exclusive as people can express interest in multiple cause areas when applying. (Later, they engage in cause prioritization as part of our program.)
In terms of our own MLE, we had in-depth interviews with 24 past advisees and have a bi-annual anonymous survey. We have occasionally run surveys to gain insights on specific things (e.g., recently, on how to make EA community building events more inclusive of mid-career people).
Thank you for this—really appreciate the specificity :)