I appreciate your feedback and comments! To clarify - my vision for this program is to emphasize that it's not a choice between x-risk and diversity; rather both can be pursued simultaneously and effectively. The core of this experiment is to integrate these two objectives. It's not about promoting diversity solely as a means to mitigate x-risk (though my intuition says that more diversity leads to a diverse range of ideas that will help with x-risk solutions). Instead, this program aims to address x-risk while concurrently supporting and helping retain qualified women in a predominantly male field that may otherwise consider leaving. This approach is based primarily on recent interviews I conducted with women in the alignment field and in EA, which revealed specific patterns and issues. 

It's important to note that this program isn't solely based on the research I mentioned here. In hindsight I regret emphasizing those papers, as it may have diverted attention from the program's primary objectives and reasoning for some. The cited studies were meant to provide additional insights rather than serve as definitive proof of the program's value, which I should have stated more clearly. I’m not very surprised that there are small sample sizes or limited studies on this, which should not be misinterpreted as a lack of such gender-based issues. Ultimately whether the program is a success or a learning experience, we'll just have to wait and let the results speak for themselves :)

Instead of listing names of people in the field that I personally wouldn’t say meet my idea of what genius is, I think it’s important to point out that I see and read incredibly intelligent and productive research much of the time. I’m sure others would disagree with me based on what they see as genius. I also think making this judgement is unfair, because of the reasons I listed above pertaining to the subjectivity, bias involved, and unclear metrics of judging genius. Similar to what occurs in scientific research, I see incremental discoveries and ideas in today’s research as incredibly valuable. Many current theories in ai safety may also be wrong, but this doesn’t make them useless. So while I could give examples, it boils down to my subjective take and I am arguing against this in this field because it doesn’t matter.