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To be honest, my gut reaction to the gender decoder was that it is itself sexist, because it promotes sexist stereotypes.

However, the negative effects are probably pretty distributed across society generally and hard to measure, while the potential benefits from more actual diversity (in EA or generally) could outweigh this. It's plausible that it actually on net reduces gender stereotyping by increasing the visibility of women in stereotypically male roles and men in stereotypically female roles. I really don't know, though.

EDIT: I'm so sorry, I've contributed to turning this into a debate. facepalm

Thanks. :P

Really impressed by your work so far, thanks for sharing this. 

Hey Edo, I'm glad to hear that you find our work useful.

I'm curious about how you are using multiple researchers for this. Most steps can be done in parallel, but I wonder- how much do you rely on multiple views on the same analysis, and how do you go about it? 

We have one lead researcher for each cause, responsible for conducting comprehensive research in their area; this way, they become experts in their respective fields. But we also want to capitalize on the fact that we are one of only a few organizations conducting research in multiple causes. We’re in a unique spot to learn and cross-apply methodologies and practices from other causes, as Neil Buddy Shah illustrates. Animal advocacy can cross-apply from global health research e.g. a comprehensive system to grade the quality of evidence. In turn, global health can learn from animal advocacy e.g. how to answer questions when there is little information, or when the evidence-base is low. For this reason, after the initial draft of a report is completed, it is peer-reviewed by a researcher from a different cause. On top of that, we have a senior staff member whose work is dedicated to thoroughly reviewing the reports. He looks for contradictory research; challenges crucial assumptions; double-checks key inputs in the CEA; verifies that the strength of evidence has been adequately expressed in the report based on its source; etc. At the end, I analyze the conclusions of the report. So for example, I consider whether any crucial considerations have been missed; if the evidence is strong enough to warrant the conclusion; and if equal rigor has been applied across different charity ideas. We also engage external research reviewers and experts in the field.

I'm always looking to improve our systems, so I'm open to suggestions on how we can do things better.

Also, is there anything that the EA community can do to assist the research process? If so, what could be the most valuable?

Thanks for this question and for facilitating this research group! It seems like a fascinating project, and I cannot wait to see updates from it.

Researching marginal ideas on our priority list would be most valuable (ideally using the same process so it is comparable). Ideas that almost made it to our priority list probably have the highest odds of being better than the idea we recommend, so researching them might change what charities will be started. To get more granular, it would be really helpful to conduct crucial consideration research that may determine whether an intervention merits deeper research. As an example, here are the first ideas that didn’t quite make the list for each cause:
1. Mental health and subjective well-being: Addressing mundane, suboptimal happiness through conditional cash transfers for using gratitude journals

2. Animal welfare: Developing and advocating for pre-hatch sexing to reduce the suffering of male chicks
3. Family planning: Informing parents and girls about future economic opportunities

4. Health and Development Policy: Improving health systems through community monitoring of health problems (e.g. through scorecards, planning meetings, etc.; regional comparison/competition for outcomes-focused government)

You can read about each of these possible interventions in more detail in the linked Idea Prioritization reports.

I care a lot about feminism and a lot about diversity in hiring practices, but I will admit that I'm skeptical about (even irritated by) most gender decoders.

EDIT: I'm so sorry, I've contributed to turning this into a debate. facepalm

Thanks Daniela for this article, amazing work!

I wrote two posts exploring suffering, both with plenty of links to more resources thinking about what we mean by "suffering": "Is Feedback Suffering? [LW · GW]" and "Suffering and Intractable Pain [LW · GW]".

My views have evolved since I wrote those posts so I don't necessarily endorse everything in them anymore, but hopefully they are useful starting points. For what it's worth, my view now is more akin to the traditional Buddhist view on suffering as described by the teaching on dependent origination.

I think the science value of exoplanet missions is really underrated. We should build the TPF. We might be able to literally find aliens or at least worlds where alien civilizations had gone extinct in the past. And learning about what had killed them might help us avoid the same fate, at least for a while. Or we might learn something that we cannot foresee.

1.) Do we need to worry about how long it takes to deploy capital? If a patient philanthropist encounters a "hinge", will there be enough pre-existing institutions to be able to take advantage of it?

2.) How much should we value the information gained from acting now and "learning by doing" about what works?

3.) I imagine there also should be value for a patient philanthropist to still donate considerably to research now, so we can better identify "hinges" and know how and where to take advantage of them.

Also, I expect to see small engineered pandemics, but only after effective genetic engineering is widespread. So the fact that we haven't seen any so far is not much evidence.

Yes, that was broadly the response I had in mind as well. Same goes for most of the "unforeseen"/"other" anthropogenic risks; those categories are in the chapter on "Future risks", and are mostly things Ord appears to think either will or may get riskier as certain technologies are developed/advanced.

Sleepy reply to Tobias' "Ord's estimates seem too high to me": An important idea in the book is that "the per-century extinction risks from “natural” causes must be very low, based in part on our long history of surviving such risks" (as I phrase it in this post). The flipside of that is roughly the argument that we haven't got strong evidence of our ability to survive (uncollapsed and sans dystopia) a long period with various technologies that will be developed later, but haven't been yet.

Of course, that doesn't seem sufficient by itself as a reason for a high level of concern, as some version of that could've been said at every point in history when "things were changing". But if you couple that general argument with specific reasons to believe upcoming technologies could be notably risky, you could perhaps reasonably arrive at Ord's estimates. (And there are obviously a lot of specific details and arguments and caveats that I'm omitting here.)

Did this end up happening?