When a human is miscategorized as a bot, they could find themselves in front of academic fraud boards, skipped over by recruiters, placed in the spam folder, etc.
> When a human is miscategorized as a bot, they could find themselves in front of academic fraud boards, skipped over by recruiters, placed in the spam folder, etc.
Is the problem here the algorithms or how people choose to use them?
There’s a big difference between treating the results of an AI algorithm as infallible, and treating it as just one piece of probabilistic evidence, to be combined with others, to produce a probabilistic conclusion.
“AI detector says AI wrote student’s essay, therefore it must be true, so let’s fail/expel/etc them” vs “AI detector says AI wrote student’s essay, plus I have other independent reasons to suspect that, so I’m going to investigate the matter further”
Two people can buy the same product yet use it in very different ways: some educators take the output of anti-cheating software with a grain of salt, others treat it as infallible gospel.
Neither approach is determined by the product design in itself, rather by the broader business context (sales, marketing, education, training, implementation), and even factors entirely external to the vendor (differences in professional culture among educational institutions/systems).
To make it more concrete on work I am very familiar with: breast cancer screening. If you had a model that outperformed human radiologists at predicting whether there is pathology confirmed cancer within 1 year, but the accuracy was not 100%, would you want to use that model or not?
I agree that there are places where we shouldn't put AI and that checking whether something is an LLM or not is one of them. However I think the sentence above takes it way too far and breast cancer screening is a pretty clear example of somewhere we should accept AI even if it can sometimes make mistakes.