What are EA's biggest legible achievements in x-risk?

I might've slightly decreased nuclear risk. I worked on an Air Force contract where I trained neural networks to distinguish between earthquakes and clandestine nuclear tests given readings from seismometers.

The point of this contract was to aid in the detection (by the Air Force and the UN) of secret nuclear weapon development by signatories to the UN's Comprehensive Test Ban Treaty and the Nuclear Non-Proliferation Treaty. (So basically, Iran.) The existence of such monitoring was intended to discourage "rogue nations" (Iran) from developing nukes.

That being said, I don't think an Iran-Israel exchange would constitute an existential risk, unless it then triggered a global nuclear war. Also, it's not clear that my performance on my contribution to the contract actually increased the strength of the deterrent to Iran. However, if (a descendant of) my model ends up being used by NATO, perhaps I helped out by decreasing the chance of a false positive.

Disclaimer: This was before I had ever heard of EA. Still, I've always been somewhat EA-minded, so maybe you can attribute this to proto-EA reasoning. When I was working on the project, I remember telling myself that even a very small reduction in the odds of a nuclear war happening meant a lot for the future of mankind.

EAs have legible achievements in x-risk-adjacent domains (e.g. highly cited covid paper in Science, Reinforcement Learning from Human Feedback which was used to power stuff like InstructGPT), and illegible achievements in stuff like field-building and disentanglement research.

However, the former doesn't have a clean connection to actually reducing x-risk, and the latter isn't very legible.

So I think it is basically correct that we have not done legible things to reduce object-level x-risk like cause important treaties to be signed, ban gain-of-function research in some countries, engineer the relevant technical defenses, etc.

See this post by Owen Cotton-Barrat as well.

I'm not sure how legible they are, but there are indications that work so far on resilience to global catastrophic agricultural risks and resilience to loss of electricity/infrastructure catastrophes has reduced X risk.

Not an answer, just wanting to say thank you for asking this question! The same question had been percolating in my mind for some time but couldn't quite put it into words, and you did so perfectly. Thank you!

The AI safety community has gotten people to do reinforcement learning from human feedback (rather than automated reward functions) sooner than it would otherwise have happened.

There's lots of subtleties about whether this reduced x-risk or not but I think it did.

Not sure how relevant to the criteria of legibility, but FHI and FLI have in short time become more influential than the likes of Chatham House and RAND Corporation (world's 6th and 7th most influential think tanks overall, founded in 1920 and 1948) within the AI domain. I previously thought naively and pessimistically that our EA organisations did not have much traction in the policy influence sphere but this report from University of Pennsylvania ranking think tanks in terms of influence went some way in making me change my mind about the achievements of EA organizations.

There's a whole AI ethics and safety field that would have been much smaller and less influential.

From my paper Activism by the AI Community: Analysing Recent Achievements and Future Prospects.

"2.2 Ethics and safety 

There has been sustained activism from the AI community to emphasise that AI should be developed and deployed in a safe and beneficial manner. This has involved Open Letters, AI principles, the establishment of new centres, and influencing governments. 

The Puerto Rico Conference in January 2015 was a landmark event to promote the beneficial and safe development of AI. It led to an Open Letter signed by over 8,000 people calling for the safe and beneficial development of AI, and a research agenda to that end [21]. The Asilomar Conference in January 2017 led to the Asilomar AI Principles, signed by several thousand AI researchers [23]. Over a dozen sets of principles from a range of groups followed [61]. 

The AI community has established several research groups to understand and shape the societal impact of AI. AI conferences have also expanded their work to consider the impact of AI. New groups include: 

• OpenAI (December 2015)
• Centre for Human-Compatible AI (August 2016) 
• Leverhulme Centre for the Future of Intelligence (October 2016)3 
•  DeepMind Ethics and Society (October 2017)
• UK Government’s Centre for Data Ethics and Innovation (November 2017)"