We got really positive feedback on the TrustCon workshop we ran on writing good content policies for LLMs...so we're doing it again! If you're interested go sign up here, so we can start to figure out timing: forms.gle/tj7vf7ng8n7R...

This response to the Raine tragedy from OpenAI does something remarkable: it has the humility to acknowledge that a *product failure* led to real-world harm. Despite horrific circumstances, it has a rare degree of honesty that I wish tech companies would show more often. openai.com/index/helpin...

We are opening up Zentropi.ai to everyone today so that anyone can build their own content labeler. What started as a crazy academic idea 2 years ago is now a real thing that companies are using in production to safeguard their AI-powered systems. Give it a shot! blog.zentropi.ai/zentropi-bui...

Don't take our word for it! Go kick the tires at zentropi.ai and build your own content labeler (no subscription required!)

@mmasnick.bsky.social I have a bluesky demo for you that you might want to see :)

Just tested this on a few that I know Reddit’s existing Hatred & Harassment automation has blindspots for; it built a focused, accurate labeler in under 10 minutes / a dozen examples, & the human readable criteria it built could be dropped into a training manual / erratum / used to build a regex

So excited for #TrustCon this week! We will be publicly unveiling Zentropi, a platform that helps people instantly build their own content labelers. We'll be opening it up for early access and open sourcing the underlying language model we trained for the task so that it is accessible to everyone.

I expect @dwillner.bsky.social to run around like a maniac again at #Trustcon this year as he shows off Zentropi -- our platform that makes it simple to build your own CoPE-powered content labeler.

A year ago at #TrustCon, I ran around like a maniac showing people something on my laptop. We'd just gotten CoPE - our policy interpretation model - working. It felt like a huge achievement, validating our ideas about LLM-powered labeling. 🧵 1/7

Take back your attention.

The splinternet accelerates. If this stands, look for more countries in 2025 to ban Facebook, Instagram, YouTube, etc. out of fears of American surveillance. www.bloomberg.com/news/article...

If someone on my team at Meta had ever said these kinds of words, I'm pretty sure I'd have had an obligation to notify HR. But maybe times have changed and "masculine energy" is now part of the performance review rubric. www.bloomberg.com/news/article...

Will Meta's retreat from proactive/automated enforcement of its community standards lead to Instagram and Facebook becoming deeper cess pools? Well, it's complicated and the devil will be in the details. Let's look at the data and see what questions we should be asking... 🧵 1/12

Folks are mostly missing the forest for the trees re: the recent Meta announcement. Specifically, the focus on the move from fact checking to community notes. There two different changes that are a much bigger deal that have received less focus. 🧵 1/11

Here's my "fact check" on Meta's announcement that it is terminating its fact checking program. The TL;DR: Pay more attention to product changes than to political pandering. THREAD...

@samidh.bsky.social and I have made a special-purpose language model for content classification that matches GPT-4's performance, but is much smaller/faster. We've got a demo up on HuggingFace and weights are available to partners today - check out the link for more details, including how to help!

To complement the article @dwillner.bsky.social and I wrote on using LLMs for content moderation, we created a Content Policy Compiler GPT to transform your content policy doc into one that's more accurately interpretable by LLMs. Very rough, but give it a shot!

Really excited to get this out the door - I think there's a huge amount of potential in this approach, but it's going to be a commensurately large amount of work to figure out. If you're doing similar experiments, please share what you've learned so we can all move faster together.

As part of our work at the Stanford Cyber Policy Center, @dwillner.bsky.social and I wrote up a practical guide on how to effectively use LLMs for content moderation. It shows both the promise and limitations of the current generation of LLMs for at scale trust and safety work. Hope it is helpful!

In anticipation of the Trust & Safety Research conference this week, here is a provocative paper from Stanford on Embedding Societal Values into Social Media Algorithms. It provides a framework for more systematically shaping these systems for the better. tsjournal.org/index.php/jo...