GoKawiilWhen I trained the previous ai comment classifier, I used partially personal private data to do it, and built it on a somewhat shaky foundation, so I couldn’t share the code or data. I rebuilt it on public data and a better foundation!
First off, you might want to try it out. Nothing you paste into that web page leaves your browser, so you can safely try it with whatever you like. I have invited some testers to try out an earlier version of it, and they had mainly positive feedback to give.
We won’t break down robot-isms the way we broke down Claude-isms in the previous article, because in the ui of the new classifier you can just click any part of the text being classified to see which features activate on that portion of the text, and how they contribute to the overall judgment. Here’s an example of the expanded feature activation view.
In terms of performance, the headline number is the balanced accuracy of 77 %. This is how often the classifier gets the human vs. robot verdict right, assuming human-written and robot-generated comments are equally likely.
The classifier also prints a predicted percentage which is calibrated, meaning it can be read as the probability that any specific verdict is correct. We test this through the calibration curve, which shows what probability the classifier assigns to an event with a known probability.
Since all dots lie very close to the reference diagonal, we know they are approximately correct. This holds true across comments of multiple lengths, where a fitted temperature parameter adjusts for increased confidence as the amount of data increases.
We can get more details about the classifier’s failure modes by looking at its confusion matrix. In this table, “robot” is considered the positive class, i.e. the thing we want to detect. The abbreviations stand for true/false positive/negative rate.
verdict: human verdict: robot input: human tnr = 0.73 fpr = 0.27 input: robot fnr = 0.20 tpr = 0.80
When presented with a known-human input, the classifier correctly judges it as human 73 % of the time. With a known-robot input, it is correctly judged 80 % of the time. This means in both cases (known-human and known-robot) the mistake rate is around 25 %. That might sound high!
But remember that this mistake rate is the aggregate over all possible inputs. We don’t need to pay too much attention to it, because the classifier outputs a calibrated predictive percentage every time it classifies something. Thus, for individual judgments, we know when the risk of false positives is lower or higher. When the classifier is very confident – e.g. when the confidence is 80 % or more – the risk of a false positive drops to 5 %. When the classifier is uncertain – when confidence is around 50 % – then by calibration it will issue the wrong verdict around half the time.
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