This situation was somewhat predictable and avoidable, in my view. I’ve lamented the early-career problem in the past but did not get many ideas for how to solve it. My impression has been that many mid-career people in relevant organizations put really high premiums on “mentorship,” to the point that they are dismissive of proposals that don’t provide such mentorship.
There are merits to emphasizing mentorship, but the fact has been that there are major bottlenecks on mentorship capacity and this does little good for people who are struggling to get good internships. The result for me personally was at least ~4 internships that were not very relevant to AI governance, were not paid, and did not provide substantial career benefits (E.g., mentorship).
In summary, people should not let the perfect be the enemy of the good: I would have gladly taken an internship working on AI governance topics, even if I had almost no mentorship (and even if I had little or no compensation). I also think there are ways of substituting this with peer feedback/engagement.
I have multiple ideas for AI governance projects that are not so mentorship-dependent, including one pilot idea that, if it worked, could scale to >15 interns and entry-level researchers with <1 FTE experienced researcher in oversight. But I recognize that the ideas may not all be great (or at least their merits are not very legible). Unfortunately, we don’t seem to have a great ecosystem for sharing and discussing project ideas, at least if you aren’t well connected with people to provide feedback through your job or through HAIST/MAIA or other university groups.
Ultimately, I might recommend that someone aggregate a list of past programs and potential proposals, evaluate the importance of various goals and characteristics (e.g., mentorship, skill development, topic education, networks, credentials/CVs), and identify the key constraints/bottlenecks (e.g., funding vs. mentorship).
This situation was somewhat predictable and avoidable, in my view. I’ve lamented the early-career problem in the past but did not get many ideas for how to solve it. My impression has been that many mid-career people in relevant organizations put really high premiums on “mentorship,” to the point that they are dismissive of proposals that don’t provide such mentorship.
There are merits to emphasizing mentorship, but the fact has been that there are major bottlenecks on mentorship capacity and this does little good for people who are struggling to get good internships. The result for me personally was at least ~4 internships that were not very relevant to AI governance, were not paid, and did not provide substantial career benefits (E.g., mentorship).
In summary, people should not let the perfect be the enemy of the good: I would have gladly taken an internship working on AI governance topics, even if I had almost no mentorship (and even if I had little or no compensation). I also think there are ways of substituting this with peer feedback/engagement.
I have multiple ideas for AI governance projects that are not so mentorship-dependent, including one pilot idea that, if it worked, could scale to >15 interns and entry-level researchers with <1 FTE experienced researcher in oversight. But I recognize that the ideas may not all be great (or at least their merits are not very legible). Unfortunately, we don’t seem to have a great ecosystem for sharing and discussing project ideas, at least if you aren’t well connected with people to provide feedback through your job or through HAIST/MAIA or other university groups.
Ultimately, I might recommend that someone aggregate a list of past programs and potential proposals, evaluate the importance of various goals and characteristics (e.g., mentorship, skill development, topic education, networks, credentials/CVs), and identify the key constraints/bottlenecks (e.g., funding vs. mentorship).