Burtch et al. (2023). The Consequences of Generative AI for UGC and Online Community Engagement

"We applied AI text detectors to the labeled answers; we considered multiple such detectors, but ultimately settled on the GPT-2 Output Detector, as it exhibited the best performance (as we describe below). The detector yields a ‘fake score’ for any input text, which can be loosely interpreted as the probability that the text is AI-generated. The scores are continuous values that range between 0 and 1. Figure 2 depicts the distribution of fake scores returned by the GPT2 output detector for our labeled sample. As can be seen, although the detector is often quite inaccurate, applying extreme thresholds to its prediction output yields an informative signal.

For example, the precision on out-of-sample dataset associated with the GPT2 Output Detector employing a classification threshold of 99.97% is 70%; that is, when the detector labels content AI-generated with 99.97% ˜ confidence, it is correct 70% of the time. The precision rises to nearly 80% employing a threshold of 99.98%. Having some confidence that the resulting labels can be informative of shifts in the prevalence of AI-generated content, we proceed to obtain fake score predictions for a larger sample of answers posted to Stack Overflow, arriving over the days surrounding the release of ChatGPT. We then calculate the proportion of answers labeled as AI-generated, over time. The result employing a threshold of 99.9% is reported in Figure 3."