As part of the alignment faking paper, I hosted a website with ~250k transcripts from our experiments (including transcripts with alignment-faking reasoning). I didn't include a canary string (which was a mistake).^[1]

The current state is that the website has a canary string, a robots.txt, and a terms of service which prohibits training. The GitHub repo which hosts the website is now private. I'm tentatively planning on putting the content behind Cloudflare Turnstile, but this hasn't happened yet.

The data is also hosted in zips in a publicly accessible Google Drive folder. (Each file has a canary in this.) I'm currently not planning on password protecting this or applying any other mitigation here.

Other than putting things behind Cloudflare Turnstile, I'm not taking ownership for doing anything else at the moment.

It's possible that I actively want this data to be possible to scrape at this point because maybe the data was scraped prior to the canary being added and if it was scraped again then the new version would replace the old version and then hopefully not get trained on due to the canary. Adding a robots.txt might prevent this replacement as would putting it behind Cloudflare Turnstile (as I'm planning to do) or making the repo private (as I have done). If people mostly or always use fully fresh scrapes, then just making it harder to scrape seems better. My current plan is to not overthink this and just make it harder to scrape.

It's certainly possible that I'm making a mistake by not more actively trying to prevent this data from getting into pretraining data.

Does anyone have specific requests that they think it's quite important that I do? I might do these out of general cooperativeness or because they seem like good ideas. Also, if you did all the work yourself and just needed me to (e.g.) host a different website, this would make this an easier call from my perspective.

Also, on a more meta point: If you think this sort of thing is important to prevent in general, I think you should consider writing up (or getting someone to write up) what policy/approach you think people doing research on misaligned AI behavior should follow (e.g., what methods should people use to prevent scraping or inclusion, is this so difficult that it's better to do stuff like have things be password protected with password shared on request, should you only use a small number of examples because quantity is very important etc). Consider making this a guide which is easy to follow so that uptake is more likely. The alignment faking paper isn't the only instance of publishing transcripts exhibiting misaligned AI behavior/reasoning!

(I'm generally open to requests for trades etc and I'm down to unilaterally do things for cooperativeness reasons, e.g. things which seem very helpful from someone's perspective while seeming less useful from my perspective, though this will depend on some details like whether there are people with this alternative perspective who would reciprocate on this sort of cooperativeness.)