How does one write a good and useful review of a technical post on the Alignment Forum?
I don’t know. Like many people, I tend to comment and give feedback on posts closely related to my own research, or to write down my own ideas when reading the paper. Yet this is quite different from the quality peer-review that you can get (if you’re lucky) in more established fields. And from experience, such quality reviews can improve the research dramatically, give some prestige to it, and help people navigate the field.
In an attempt to understand what makes a good review for the Alignment Forum, Joe Collman, Jérémy Perret (Gyrodiot on LW) and me are launching a project to review many posts in depth. The goal is to actually write reviews of various posts, get feedback on their usefulness from authors and readers alike, and try to extract from them some knowledge about how to go about doing such reviews for the field. We hope to have enough insights to eventually write some guidelines that could be used in an official AF review process.
On that note, despite the support of members of the LW team, this project isn’t official. It’s just the three of us trying out something.
Now, the reason for the existence of this post (and why it is a question) is that we’re looking for posts to review. We already have some in mind, but they are necessarily biased towards what we’re more comfortable about. This is where you come in, to suggest a more varied range of posts.
Anything posted on the AF goes, although we will not take into account things that are clearly not “research outputs” (like transcripts of podcasts or pointers to surveys). This means that posts about specific risks, about timelines, about deconfusion, about alignment schemes, and more, are all welcome.
We would definitely appreciate it if you add a reason to your suggestion, to help us decide whether to include the post on our selection. Here is a (non-exhaustive) list of possible reasons:
- This post is one of the few studying this very important question
- This is my post and I want some feedback
- This post was interesting but I cannot decide what to make of it
- This post is very representative of a way to do AI Alignment research
- This post is very different from most of AI Alignment research
Thanks in advance, and we’re excited about reading your suggestions!