I don't think we have a good answer to what happens after we do auditing of an AI model and find something wrong.

Given that our current understanding of AI's internal workings is at least a generation behind, it's not exactly like we can isolate what mechanism is causing certain behaviours. (Would really appreciate any input here- I see very little to no discussion on this in governance papers; it's almost as if policy folks are oblivious to the technical hurdles which await working groups)

I see way too many people confusing movement with progress in the policy space. 

There can be a lot of drafts becoming bills with still significant room for regulatory capture in the specifics, which will be decided later on. Take risk levels, for instance, which are subjective - lots of legal leeway for companies to exploit. 

Communicating by keeping human rights at the centre of AI Policy discussion is extremely underappreciated.
For e.g., the UN Human Rights chief in 2021 called for a moratorium on the sale and use of artificial intelligence (AI) systems until adequate safeguards are put in place.

Respect for human rights is a well-established central norm; leverage it  

Great post!
Do note that given the context and background, a lot of your peers are probably going to be nudged towards charitable ideas. I would want to be generally mindful that you are doing things that have counterfactual impacts while also taking into account the value of your own time and potential to do good.

I encourage you to also be cognizant of not epistemically taking over other people's world models with something like "AI is going to kill us all" - I think an uncomfortable amount of space inadvertently and unknowingly does this and is one of the key reasons why I never started an EA group at my university.

Also, here is a link if anyone wants to read more on the China AI registry which seems to be based on the model cards paper

Nice summarization! I generally see model registries as a good tool to ensure deployment safety by logging versions of algorithms and tracking spikes in capabilities. I think a feasible way to push this into the current discourse is by setting it in the current algorithmic transparency agenda. 

Potential risks here include who decides what is a new version of a given model. If the nomenclature is left in the hands of companies, it is prone to be misused. Also, the EU AI Act seems to take a risk-based approach, with the different kinds of risks being more or less lines in the sand.

Another important point is what we do with the information we gather from these sources - I think there are "softer"(safety assessments, incident reporting) and "harder"(bans, disabling) ways to go about this. It seems likely to me that governments are going to want to lean into the softer bucket to enable innovation and have some due process kick in. This is probably more true with the US which has always favoured sector-specific regulation.

It's frankly quite concerning that usually technical specifications are only worked on by Working Groups after high-level qualitative goals are set by policymakers- seems to open a can of worms for different interpretations and safety washing.

After talking and working for some time with non-EA organisations in the AI Policy space, I believe that we need to give more credence to the here-and-now of AI safety policy as well to get the attention of policymakers and get our foot in the door. That also gives us space to collaborate with other think tanks and organisations outside of the x-risk space that are proactive and committed to AI policy. Right now, a lot of those people also see X-risks as being fringe and radical(and these are people who are supposed to be on our side).

Governments tend to move slowly, with due process, and in small increments(think, "We are going to first maybe do some risk monitoring, only then auditing"). Policymakers are only visionaries with horizons until the end of their terms(hmm, no surprise). Usually, broad strokes in policy require precedents of a similar size for it to be feasible within a policymakers' agenda and the Overton window. 

Every group that comes to a policy meeting thinks that their agenda item is the most pressing because, by definition, most of the time, contacting and getting meetings with policymakers means that you are proactive and have done your homework.

I want to see more EAs respond to Public Voice Opportunities, for instance- something I rarely hear on the EA forum or via EA channels/material. 

So, some high-level suggestions based on my interactions with other people I have are:

1. Being more explicit about this in 80K hours calls or talking about the funding bar (potentially somehow with grantmakers/ intro'ing to successful candidates who do independent stuff). Maybe organisations could explicitly state this in their fellowship/intern/job applications: "Only 10 out of 300 last year got selected" so that people don't over-rely on some applications. 
2. There is a very obvious point that Community Builders can only do so much because their general job is to point resources out and set initial things rolling. I think that as community builders, being vocal about this from an early point is important. This could look like, "Hey, I only know as much as you do now that you have read AGI SF and Superintelligence."  Community builders could also try connecting with slightly more senior people and doing intros on a selective basis(e.g., I know a few good community builders who try to go out of their way to an EAGx to score convos with such people).
3. I think metrics for 80K, and CBs need to be more heavily weighted towards(if not already) "X went on to do an internship and publish a paper" and away from "this guy read superintelligence and did a fun hackathon". The latter also creates weird sub-incentives for community members to score brownie points with CBs and make a lot of movement with little impactful progress.
4. Talking about creating your own opportunities seems really untalked about in EA circles- there is a lot of talk about finding opportunities and overwhelming newcomers with EA org webpages, which, coupled with neglectedness, causes them to overestimate the opportunities. Maybe there could be a guide for this, some sort of a group/support for this?
5. For early career folks, maybe there could be some sort of a peer buddy system where people who are a little bit further down the road can get matched and collaborate/talk. A lot of these conversations involve safe spaces, building trust and talking about really sensitive issues(like finances, runway planning and critical feedback on applications). I have been lucky to build such a circle within EA, but I recognize that's only because of certain opportunities I got early on, along with being comfortable with reaching out to people, something which not necessarily everyone is.
6. We need to identify more proactive people who already have a track record of social impact/being driven by certain kinds of research instead of just high-potential people- these are probably the only people who will actually convert to returns for the movement(very crudely speaking). This is even more true in non-EA hubs where good connections aren't just one local meetup away as with NYC or Oxford. I think there is a higher attrition rate of high-potential people in LMICs, at least partly due to this.

I find the Biden chip export controls a step in the right direction, and it also made me update my world model of compute governance being an impactful lever. However, I am concerned that our goals aren't aligned with theirs; US policymakers' incentive right now is to curb China's tech growth and fun trade war reasons, not pause AI.

This optimization for different incentives is probably going to create some split between US policymakers and AI safety folks as time goes on.

It also makes China more likely to treat this as a tech race which sets up interesting competitive race dynamics between the US and China which I don't see talked about enough.