Drew Dimmery

@ddimmery.com

social science methods: experiments, stats, ML. has read over a dozen books

Last monday we published our paper on AI Labels and a potential spillover effect on unlabeled posts on platforms such as Instagram in the ICWSM proceedings. If you are interested in our work you can now find it here: ojs.aaai.org/index.php/IC... Huge thanks to my co-authors @ddimmery.com and Pu Yan!

Implied Authenticity Effect? The Impact of Explicit Labels on AI-Generated Content | Proceedings of the International AAAI Conference on Web and Social Media

ojs.aaai.org

Fabian Pawelczyk@fpawel.bsky.social · 6mo ago

🚨My first preprint is out on @socarxiv.bsky.social! How do AI-generated content labels shape what people see as authentic on social media — and do labels have unintended side effects? osf.io/preprints/so... A thread 🧵

The end of 538 is a huge shame - both for the incredible people who worked there, and for political and data journalism as a whole. I was lucky enough to work beside them for a few years and want to say a bit about what I think was so valuable that I hope doesn't vanish from the media landscape: 🧵

Nathaniel Rakich@baseballot.bsky.social · last yr.

Incredibly sad to report that ABC News is indeed eliminating 538. I count myself incredibly lucky to have worked with such incredibly smart, kind people for 7 years. Thank you all for coming along for the ride.

Does anyone know of a good intellectual history of ML/AI? Something that doesn't just survey the development of methods, but includes things like "Papert and Minsky think solving computer vision will be an undergrad summer project in the 60s"