Thanks Emily, this is a very valuable post.
I think many people who work in EA orgs do not understand how difficult it is to find a job in an EA org. Once you’re in, perhaps it seems obvious how to get in. Perhaps they lucked out or knew someone or got hired on their first application.
And so they sincerely encourage others, believing that it’s just a matter of time. Because, based on the subset of applicants they know (the people they work with), there is a 100% success rate!
However, most people report applying for jobs and seeing literally hundreds of applicants for each position, and many just give up.
I wrote here previously (I think, it was a while ago) about the amount of effort people put into looking for work in the EA field is actually highly ineffective use of motivated people’s energy and skills.
I also know that EA organisations, more than other companies, put huge effort into giving fair consideration to all applicants, and so many of their hours are spent on recruitment, which is not directly advancing the projects their organisation works on. But I question whether being fair to candidates in this way is consistent with being truly effective in advancing the goals of EA.
And I also wonder if even the candidates themselves would prefer a quicker, less work-intensive process, even if it were less fair.
I would love it if some organisations could do a meta-analysis of the impact of eliminating 90% of the effort of recruiting. How:
1. Very clear criteria in the job description. If you don’t meet these, don’t waste your time and our time applying. (as in: do not put the usual “please apply even if you don’t think you’re a good fit with the criteria”—let people apply to fewer jobs, but with a higher chance of success for each).
2. Very short application forms—like 5-10 minutes maximum for the initial application.
3. Option to submit automatic AI application based on your CV / linked-in, and to be evaluated by AI, which would eliminate most candidates and select only the top few % for in-depth analysis. (Also option to not be evaluated by AI if you think it will unfairly disadvantage you).
4. Hire someone “reasonably good” on a 3-month trial rather than spending 3 months working on recruiting. Take a risk. If they don’t work out, hire someone else. Most applicants who look reasonable good will be reasonably good.
The key points here:
The goal of effective altruism is to do the most good possible.
Being fair to job-applicants (ensuring the best applicant gets the job) is NOT a primary goal of EA. We all have such an inherent sense of fairness, we want to reward the best applicant and find the best candidate—but IMHO for most roles, EA as a whole would be better off with a much less time-consuming process, even if sometimes we didn’t get the best people.
When you need a plumbing job done at home, you look for a competent plumber and usually they work out. You don’t spend months searching for the best plumber and interviewing them. How many EA roles just need someone who is committed and competent?
Ensuring that we get the very best hire for every role should NOT be a critical goal except where there is a demonstrated and strong correlation with achieving the best results. Maybe for a visionary leadership role yes.
I believe people who work in EA orgs are mostly wonderful and brilliant (truly), but so are many of the job-applicants that are rejected.
Having applicants and people new to EA spend huge amounts of effort just to get hired is not effective use of their energy, and it can be very demotivating.
in general (not just in EA) the accuracy of recruitment is greatly overrated. How strong is the evidence that we even get better people after all the work we put in.
I keep reading of EA orgs which are “bottlenecked” not by funding but by lack of people in specific roles. Given the millions of people looking for EA-related work, this suggests a flawed recruitment process.
I love to see this. Based on the Horizon experience in the EU, these kinds of problems are often best tackled by a mixed consortium—maybe a university, a start-up and a multinational—which bring different aspects and questions.
These technical questions are very tractable but very complex. There are universities and industry groups who work on them in other contexts. For example there is a field called tribology which studies the texture of foods in the mouth, and texture is critical to our view of “meatiness”—and very difficult to copy. If it were just taste, we could must mix amino-acids a paste and flavour them, but consumers want not just the taste, but also the mouthfeel, the aroma, the feeling you get when you bite into a steak, and how that changes as you chew it and as it interacts with your saliva. It is hard, but if you get it wrong, people won’t say “well, it was a good effort,” they’ll just say “sorry, but it’s not meat!”
Artificial meat/fish/eggs might be the single greatest opportunity for the world. It can prevent so much animal suffering in factory farms. It can prevent so many emissions. It and reduce the land-use and water-use needed to feed us. And it can ultimately give us a low-cost, plentiful, nutritious food supply with no need for antibiotics, which can feed the world.
It’s great to see the focus on this.