Hide table of contents

Retrospective Employment Bounties

Funding socially beneficial work in retrospect is an idea that has been frequently discussed (e.g. here) but has had a variety of problems in practice, including the difficulty in concretely identifying ahead of time what will be in retrospect eligible.

A possible solution to this would be to exploit an existing area where such organisations already make such deliberations: hiring. At present orgs evaluate researchers, estimating their expected research contributions to that organisation’s agenda. If this estimate is high, the researcher is hired. If it is low, the researcher is rejected.

Sometimes these estimates are wrong, however, and sometimes the candidate might have good reason to believe this. Perhaps he knows he underperformed his true potential at the interview, or struggled to explain his ideas, or simply thinks the interviewers were mistaken about some crucial consideration. In these cases it might be good if the researcher was incentivised to work on the research anyway, as if he had been hired.

This could be achieved if the organisation credibly promised to pay rejected applicants in retrospect if their work turned out to be sufficiently good that they should have been hired. In order to account for risk, the organisation might offer a multiple of the salary + benefits they would have offered. For example, if the organisation thinks their marginal hire (that they actually hired) has a 50% chance of producing work of quality X, they should be willing to pay 2x compensation for any rejected candidate who nonetheless produces quality X work.

The organisation could additionally hire the researcher at this point, but this is not a necessary component. Additionally, this system could be combined into a broader Impact Certificates program - it would essentially be a commitment to purchase certificates at a certain price - but can stand on its own. Some mechanism may be required to ensure that multiple organisations do not fund the same work.

Advantages of this approach:

  • Incentivise additional work
    • The prospect of compensation (and vindication!) may induce additional research effort on useful topics.
  • Guide research
    • Independent researchers will have some sense of what to work on: instead of ‘produce valuable things’, they have the slightly more concrete ‘things this organisations thinks are valuable’.
  • Pay for Performance
    • Only research successes require payment.
  • Existing Evaluation Criteria
    • Organisations already make hiring decisions, so there is relatively little novel evaluative work required.
  • Save overhead
    • If rejected candidates can produce good work independently, you are relieved of the costs of managing them.
  • Fail gracefully
    • If no organisations adopt this policy, or no spurned applicants pursue it, little is lost.

Disadvantages of this approach:

  • Waste Time
    • Low quality, correctly rejected candidates might waste their time on a quixotic quest for vindication.
  • Limited Scope
    • This can only induce counterfactual effort from rejected candidates, a relatively small pool.
    • If organisations are good at hiring, this may be a weak pool.
  • Dissatisfaction
    • Being rejected from such a prize could add insult to the injury of the initial rejection.
    • Some people might disagree with the retrospective evaluation and feel cheated.
  • Risk aversion / diminishing marginal utility
    • A guarantee of Y dollars is more valuable than a 50% chance of 2*Y, so this produces less utility for researchers than if they had been hired (though this was not on the table as they were rejected).
  • Duplication of effort
    • Multiple researchers may require compensation for the same insights in a way that could be avoided if the organisation was directly allocating their labour.
  • Financial planning
    • Uncertainty about how many such prizes will be earned could make it harder for an organisation to predict their future expenditure. But I would expect that, if this is a good idea, donors could be found to underwrite the expense.

What next?

Having put this idea out there I have little intention of promoting it further, but would be happy to discuss or help any organisations that found it interesting.

38

0
1

Reactions

0
1

More posts like this

Comments4


Sorted by Click to highlight new comments since:

This is an interesting idea, thanks for raising it!

I think intuitively, it worries me. As someone around hiring in these sorts of areas, I'm fairly nervous around the liabilities that come from hiring, and this seems like it could increase these. (Legal, and just upsetting people).

I'm imagining:

  • There's a person who thinks they're great, but the hiring manager really doesn't see it. They get rejected.
  • They decide to work on it anyway, saying they'll get the money later.
  • They continue to email the org about their recent results, hoping to get feedback, sort of similar to as an employee.
  • 6-20 months later, they have some work, and are sure that it deserves funding.
  • The work isn't that great, and the prize is denied.
  • They get really upset that their work has been denied.

This system could create "pseudo-employees" who are trying to act as employees, but aren't really employees. This just seems pretty messy. 

In addition, funding seems tricky. Like, a lot of research nonprofits don't have that much extra funding allocated in their budgets for this. I imagine it would have to be coordinated with funders, on-demand. ("Hey, funder X... person Y, who we rejected, just did good work, and now we need $160k to fund them. Can you donate that money to us, so we can retrospectively pay them?")

I could also see the tax/legal implications as messy, though that could be resolved with time.

Generally, if someone seems pretty strong and capable of doing independent work, I suggest they apply to the LTFF, and say that I could help discuss their application. The LTFF funds a lot of people  at this point. Small funders like the LTFF seem like great escape hatches for these situations. So this technique would really make sense, I assume, if both the LTFF rejects them, and I'm pretty confident they have a solid chance of doing good research. This is pretty unusual. 

It's quite possible the benefits overcome these negatives. I'm not sure, I just wanted to share my quick feelings on this.

Oh wow,  literally just minutes I independently made a similar suggestion in a shortform here. My idea is a bit different because I propose that people would apply to EA funds (or other funds) to do research and would be paid after their research is done, depending on how good/impactful their research was.  I thought I should mention it here because pursuing any one of these two ideas would probably be enough.

I think you missed a disadvantage: I think there's a free rider problem where everyone reaps the benefits of the research and it's too easy for a given org to decline funding it. 

Overall I like the idea a lot and 

Some mechanism may be required to ensure that multiple organisations do not fund the same work.

I hope to find time for this exercise later today. 

One big change that a lot of employers can make is changing their interviews and written tests.

I’ve been required to create a new policy from scratch in interview settings. “Okay now you should come up with an idea on the spot, and you will need to say why this policy should now be a legal requirement of every person in the country.” It’s exactly that type of surface-level thinking that policymakers should avoid.

You should be allowed to bring in work that you’ve already made into the interview and for the written application. It’s far more reflective of the work you will do, because it is the work that you’ve done. Plans for future policy writings mean nothing because there very well could be some technical reason why your nascent policy idea is fundamentally flawed.

Curated and popular this week
Paul Present
 ·  · 28m read
 · 
Note: I am not a malaria expert. This is my best-faith attempt at answering a question that was bothering me, but this field is a large and complex field, and I’ve almost certainly misunderstood something somewhere along the way. Summary While the world made incredible progress in reducing malaria cases from 2000 to 2015, the past 10 years have seen malaria cases stop declining and start rising. I investigated potential reasons behind this increase through reading the existing literature and looking at publicly available data, and I identified three key factors explaining the rise: 1. Population Growth: Africa's population has increased by approximately 75% since 2000. This alone explains most of the increase in absolute case numbers, while cases per capita have remained relatively flat since 2015. 2. Stagnant Funding: After rapid growth starting in 2000, funding for malaria prevention plateaued around 2010. 3. Insecticide Resistance: Mosquitoes have become increasingly resistant to the insecticides used in bednets over the past 20 years. This has made older models of bednets less effective, although they still have some effect. Newer models of bednets developed in response to insecticide resistance are more effective but still not widely deployed.  I very crudely estimate that without any of these factors, there would be 55% fewer malaria cases in the world than what we see today. I think all three of these factors are roughly equally important in explaining the difference.  Alternative explanations like removal of PFAS, climate change, or invasive mosquito species don't appear to be major contributors.  Overall this investigation made me more convinced that bednets are an effective global health intervention.  Introduction In 2015, malaria rates were down, and EAs were celebrating. Giving What We Can posted this incredible gif showing the decrease in malaria cases across Africa since 2000: Giving What We Can said that > The reduction in malaria has be
Neel Nanda
 ·  · 1m read
 · 
TL;DR Having a good research track record is some evidence of good big-picture takes, but it's weak evidence. Strategic thinking is hard, and requires different skills. But people often conflate these skills, leading to excessive deference to researchers in the field, without evidence that that person is good at strategic thinking specifically. I certainly try to have good strategic takes, but it's hard, and you shouldn't assume I succeed! Introduction I often find myself giving talks or Q&As about mechanistic interpretability research. But inevitably, I'll get questions about the big picture: "What's the theory of change for interpretability?", "Is this really going to help with alignment?", "Does any of this matter if we can’t ensure all labs take alignment seriously?". And I think people take my answers to these way too seriously. These are great questions, and I'm happy to try answering them. But I've noticed a bit of a pathology: people seem to assume that because I'm (hopefully!) good at the research, I'm automatically well-qualified to answer these broader strategic questions. I think this is a mistake, a form of undue deference that is both incorrect and unhelpful. I certainly try to have good strategic takes, and I think this makes me better at my job, but this is far from sufficient. Being good at research and being good at high level strategic thinking are just fairly different skillsets! But isn’t someone being good at research strong evidence they’re also good at strategic thinking? I personally think it’s moderate evidence, but far from sufficient. One key factor is that a very hard part of strategic thinking is the lack of feedback. Your reasoning about confusing long-term factors need to extrapolate from past trends and make analogies from things you do understand better, and it can be quite hard to tell if what you're saying is complete bullshit or not. In an empirical science like mechanistic interpretability, however, you can get a lot more fe
Ronen Bar
 ·  · 10m read
 · 
"Part one of our challenge is to solve the technical alignment problem, and that’s what everybody focuses on, but part two is: to whose values do you align the system once you’re capable of doing that, and that may turn out to be an even harder problem", Sam Altman, OpenAI CEO (Link).  In this post, I argue that: 1. "To whose values do you align the system" is a critically neglected space I termed “Moral Alignment.” Only a few organizations work for non-humans in this field, with a total budget of 4-5 million USD (not accounting for academic work). The scale of this space couldn’t be any bigger - the intersection between the most revolutionary technology ever and all sentient beings. While tractability remains uncertain, there is some promising positive evidence (See “The Tractability Open Question” section). 2. Given the first point, our movement must attract more resources, talent, and funding to address it. The goal is to value align AI with caring about all sentient beings: humans, animals, and potential future digital minds. In other words, I argue we should invest much more in promoting a sentient-centric AI. The problem What is Moral Alignment? AI alignment focuses on ensuring AI systems act according to human intentions, emphasizing controllability and corrigibility (adaptability to changing human preferences). However, traditional alignment often ignores the ethical implications for all sentient beings. Moral Alignment, as part of the broader AI alignment and AI safety spaces, is a field focused on the values we aim to instill in AI. I argue that our goal should be to ensure AI is a positive force for all sentient beings. Currently, as far as I know, no overarching organization, terms, or community unifies Moral Alignment (MA) as a field with a clear umbrella identity. While specific groups focus individually on animals, humans, or digital minds, such as AI for Animals, which does excellent community-building work around AI and animal welfare while