Cause prioritization is likely quite important. EAs know that some causes can be immensely more effective than others with the best cause being many times more effective than the average cause. If you believe Michael Dickens’s calculations, AI safety work could be 10^51 times more important than work on global poverty[1] work if the estimate provided is literally correct and 15000 times more important once you adjust for the prior belief and the strength of the evidence for the estimate. This would suggest that $1K put toward AI safety could potentially accomplish more than the $9.8M that Good Ventures gave GiveDirectly in December 2015. Similarly, using Dickens’s numbers, the OpenPhil grants of $3M to promoting cage free egg campaigns would be worth an equivalent of $3B to GiveDirectly (not taking into account room for more funding or diminishing marginal returns).
If this true[1], that would mean establishing and promoting AI safety or cage free egg campaigns over global poverty donations would be of immense value. And if this isn’t true, then it would be good to figure out which cause is the best and what relative returns we can expect or if there is a currently undiscovered cause that would be higher leverage than all the known causes. Figuring out which cause is the best (and by how much) is key to the work of cause prioritization.
Cause prioritization work can come in a bunch of different forms[2]:
For example, consider the question “How valuable is marginal investment in campaigns for cage-free eggs?”
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Philosophical: Think about whether nonhuman animals are morally relevant and what kinds of ways nonhuman animals can be relevantly harmed. Think about what it means for animals to have welfare relative to humans. Consider whether cage-free campaigns are important, neglected, and tractable.
Of these methods, I’d argue that the biggest opportunities lie in working more on empirical cause prioritization. Why?
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Without empirical work, we have very little concrete evidence to use to make the other methods work. Theoretical and synthesis work both go off of empirical work, aggregating them together. Philosophical work is best when telling us how to judge empirical work or what views empirical work may have missed. And forecasting work is best benchmarked against how accurately it predicts the future empirical results.
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Empirical cause prioritization is relatively neglected to its importance. I’d expect all five methods to be useful for cause prioritization, but there has lately only been significant investment in philosophical, theoretical, and synthesis-related work. No one appears to do any empirical work[4], which suggests it could be a neglected opportunity worth taking[5].
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Empirical work is more tractable. Working out tough problems like quantifying the amount of existential risk that currently exists are difficult problems that not only are unlikely to be resolved soon, but are unlikely to even find frameworks for resolution that are satisfying. While empirical work is challenging, we at least already have a framework for how progress can be made[6].
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There are studies worth doing now that can be done now but aren’t being done, such as a high-quality studies to determine whether certain interventions work to improve animal welfare (potentially working with philosophical work to establish a relative value to global poverty work) and higher-quality work to establish the impact of EA movement building.
Endnotes
[1]: Of course, I’m pretty skeptical this is the case. I don’t think anyone claims AI risk is literally 10^51 times more important, but I’m even skeptical of the 15000x number.
[2]: This was adapted from Paul Christiano’s Untitled Prezi on cause prioritization.
[3]: One aspect I did not mention that OpenPhil does a lot of (and ACE does some of) is learning by giving. This could be considered empirical cause prioritization work that is a lot less structured and rigorous than an RCT but potentially allows one to learn the effects of particular interventions with high external validity.
[4]: Looking through the five outlined approaches to cause prioritization, here are some organizations that I think best fit each (certainly I am missing some organizations):
Empirical -- the Institute for Health Metrics and Evaluation, IDinsight,the Center for Global Development, the World Health Organization,the Center for Disease Control, Cochrane,the Campbell Collaboration,the Abdul Latif Jameel Poverty Action Lab,Innovations for Poverty Action, and the International Initiative for Impact Evaluation, AI Impacts.
Theoretical/Philosophical -- Machine Intelligence Research Institute,Future of Humanity Institute, Future of Life Institute,Global Catastrophic Risks Institute, Foundational Research Institute, Global Priorities Project
Synthesis -- GiveWell, Open Philanthropy Project,Copenhagen Consensus,the Disease Control Priorities Project, Our World in Data, Animal Charity Evaluators
Forecasting -- Centre for Applied Rationality, Philip Tetlock
While there are a good deal of organizations working on producing empirical studies, with the exception of AI Impacts these organizations are entirely within global poverty as a cause and none of the organizations work to compare between causes.
Likewise, for the question of cage-free campaigns, there exists marginal investment in synthesis, theoretical approaches, and philosophical approaches, but no investment in empirical or forecasting approaches.
[5]: Similarly, forecasting work is also neglected, though I expect it to be somewhat less useful and tractable than empirical work. When thinking through how neglected something is we must always wonder if it is neglected for good reason. I don’t think this is true for empirical work -- empirical work seems to have important value for establishing initial numbers to work from. Instead, I think empirical work is neglected because it is very difficult and relatively expensive.
[6]: Now excuse me while I try to figure out what kind of methodology best fits an empirical study to figure out whether empirical methods to cause prioritization outperform other methods of cause prioritization.
A common response here is that this looks at the long-term effects of AI safety but only the direct effects of global poverty work, when really you should look at the long-term effects of global poverty alleviation as well. Although this may make global poverty look worse because I believe the long-term effects look slightly more likely to be bad than good.