I would say that's not necessarily the case unless there are zero false positives. In fact, your university situation is exactly where a detection system that works 50-90% of the time would be a nightmare if a meaningful share of the 10-50% errors were false positives.
Im very confused how this is even supposed to work at face value.
1) If the verification can be done by anyone, then anyone can bypass it.
2) If it can only be done by anthropic then the government or whomever has the special privilege (not everyone otherwise this is just #1) has to make a specific request
Is the point of the legislation to accurately classify text in general or to simply detect the true positives?
EDIT: Sorry, I misread your comment above. Yeah, hopefully orders of magnitude fewer kids than the number who are getting falsely accused of cheating with LLMs now.
Increasing the accuracy of these systems - both in terms of false positives and false negatives - seems like a good thing.
If some kids are false positives (detected as using ai but didn’t)then how are they lazy?
A proper watermarking system should be able to have an arbitrarily high accuracy - as many nines as you want. And it should be able to actually report the accuracy of its judgements.
If you're worried about kids being falsely accused of cheating using LLMs, you should be cheering on these developments.
Seems like about 3 collisions per 100 million pictures. If everyone have 1000 pictures that is 3 collisions per 100 000 users.
Raiding 2 people in my city for made up CSAM pictures would be way to high false positive rate.
But you can also make any picture match a CSAM hash by adding picked noise.
Probability of a false positive is 3 x 10^-5 in one example.