

Jonathan Stray
25K posts

@jonathanstray
Knowing things is a solved problem. Getting along is not. Doing the science to ensure AI results in less war, not more @CHAI_Berkeley.



Some months ago, I began typing an email to my friend historian Richard John and quickly realized I was writing an essay I had wanted to do for years. I published it this morning and call it, "Two Forms of 'Technology' Criticism: Cultural Pessimism Versus Materialism." In it, I make a distinction between materialist critique and a different tradition rooted in cultures of despair and condescension that frets about "instrumental reason" and something it calls "technology." Materialism is right and good. Cultural pessimism sucks. Link below.


All can be true: 1. Universities have been targeted by a decades-long bad faith campaign 2. Status quo is vastly more normal than craziest stuff on Fox/social media, esp. outside elite schools 3. Parts of academia are in rough shape Despite 1 & 2, we can and should address 3.

















Our new research: to start building towards political superintelligence by using AI to help us govern better, we created an AI that reads contracts and predicts ambiguities that will lead to disputes. We tested it on 10,000 Kalshi and Polymarket resolution rules, and it works quite well! Just by reading the resolution rules, our tool is able to assess which prediction-market contracts are likely to lead to resolution disputes, far better than random chance. So much of governance is writing good rules. But writing good rules is hard. For centuries, we've relied on human experts to try to write rules that anticipate as many confusions, ambiguities, and loopholes as possible. Inevitably, we make mistakes---from the famous contract law example of "the two ships Peerless" that I explore in today's piece, to unclear content moderation policies, ambiguous legislation, or the recent dustup around the US-Iran cease fire agreement wording, this is a perennial challenge. Can AI help us do it better? At Free Systems, a big part of our vision is figuring out how AI can improve how we govern, so we were eager to put this to the test. We collected a sample of 10,000 prediction-market contracts with their stated resolution rules, along with info on which ones ended up disputed. Then, we worked with our buddy Claude to develop a 10-point rubric for contract clarity, covering elements like whether the key question is well defined, whether the entities are identifiable, whether the time window is clearly specified, and so on. We had an LLM grader apply the rubric to the contracts, then built a simple machine-learning model that uses the 10-dimensional rubric score to predict subsequent disputes. The resulting scores allow us to provide overall grades to prediction-market contracts which reflect how clearly written they are and how likely they are to fall into dispute later. The contracts we grade "CCC" are 3.4x more likely to fall into dispute than the ones we grade "A." There's a lot more work to do here---we need to make sure our predictions hold in a truly out-of-sample test where we grade contracts now and see if they get disputed in the future, which we'll be working on next---and we need to expand this beyond prediction market contracts as well. But we're super excited about this direction. Tools like this will help us to identify contract ambiguities before they become disputes, allowing us to write better rules, improve governance, and eventually, get to political superintelligence. There's lots more info in our write-up, here: freesystems.substack.com/p/superintelli… Joint work with @elliotjpaschal





What could it mean for an AI to be "politically neutral”? And can we measure it? New paper + dataset. We propose a defn that applies to any type of conflict: a neutral response should maximize approval on both sides of an issue, while keeping that approval balanced. 1/🧵

To follow up on this — feel free to totally ignore the results for ChatGPT and Anthropic and dismiss them as biased. Grok which is explicitly designed to not have left-wing bias still leans left! Is that because most writers are on the left? Maybe but doesn’t Grok know that?



