Ahh, the classic sensitivity/specificity problem. It's very difficult to get a binary test to detect what you want it to detect. You have to decide. Do you want to affirm 100% human posts as often as possible? Or do you want catch AI as much as possible? Either way you are going to get false positives (or false negatives).
And therein lies the rub, right? That's a math problem. Not an authorship problem.
Sensitivity and specificity tell you how good the test is. They don't tell you what the test is testing for. Pangram is measuring "does this look machine-touched." That's real, useful, measurable. It is not the same question as "did a human mean this" ā and the tradeoff you're describing doesn't change that gap. You can tune the threshold all day. You're still tuning the wrong instrument for the question people actually want answered.
Ahh, the classic sensitivity/specificity problem. It's very difficult to get a binary test to detect what you want it to detect. You have to decide. Do you want to affirm 100% human posts as often as possible? Or do you want catch AI as much as possible? Either way you are going to get false positives (or false negatives).
And therein lies the rub, right? That's a math problem. Not an authorship problem.
Sensitivity and specificity tell you how good the test is. They don't tell you what the test is testing for. Pangram is measuring "does this look machine-touched." That's real, useful, measurable. It is not the same question as "did a human mean this" ā and the tradeoff you're describing doesn't change that gap. You can tune the threshold all day. You're still tuning the wrong instrument for the question people actually want answered.
Good point.