[WhitneyLand]

That’s a different point.

I’d want detectors to be as accurate as possible, false positives of 1 in 10000 seems like a good starting point. I believe their results have been independently tested.

And as a separate matter, any tool for evaluating students should be applied fairly, safely, and with adequate human review and due process.

You need good tools and good oversight.

[cwmoore]

Due process should never just become a checkbox item. To deal with lives and livelihoods justly, you need appeal pathways and meaningful liability exposure for the processors.

Plagiarism and cheating sucks for everyone. Worth solving.

[hgoel]

>And as a separate matter, any tool for evaluating students should be applied fairly, safely, and with adequate human review and due process.

Agreed, that's a fair and reasonable stance.

The reason I asked is that I have a hard time understanding the point of these tools. When it comes to education, it can be a matter of learning objectives. But outside that, what's the point?

The prediction from the tool is pointless for deciding on copyright or contract issues, and other text should be judged on its correctness or applicability to the task.

If all the tool is good for is "maybe this student cheated, but only an in-depth investigation would maybe prove it", it isn't a very useful tool, because it's more straightforward to just mandate that evidence is submitted regardless of what the tool says. On top of that, even the lack of evidence of manual work isn't good proof of using LLMs.

[WhitneyLand]

I’m personally interested in it as part of the research to improve LLM writing. Detecting “AI voice” is part of understanding what’s wrong with it in the first place and how to improve it.

But yeah, in general I think you’re right, the actual utility is pretty niche.