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āBecause of that, it’s really hard to do this detection problem, to be able to say confidently that āThis text was generated by a large language modelā versus āThis text was generated by a non-native English speaker,ā for example,ā Guzdial says.
āOr āThis text was generated by somebody who just made some mistakes in their wording,ā or things like that. So, it’s very difficult to detect these things accurately, and so you can get actually in quite a bit of trouble.ā
Tactics to decrease AI-generated job applications
Instead of eliminating AI use, the focus instead should be reducing the amount of time wasted on applications that will never make it to the interview stage, says Guzdial.
āYou want to limit the amount of flak, the amount of people that you’re just never going to hire,ā he says. For example, many applicants use AI to scan company websites and job postings to generate resumes and cover letters.
By inserting āinvisibleā text (that is the same colour as the background) on websites and job postings, and instructing the applicantās AI tool to create something else ā āāMary Had a Little Lambā or whatever, just something arbitraryā ā employers can dissuade applicants from continuing their application.