The AI detector doesn’t provide a simple true or false statement regarding AI-detected content. Instead, it operates on thresholds and ratios, aiming to determine whether content is more likely AI-generated or human-written based on various criteria. This approach is akin to how search engines assess content authenticity, considering multiple factors and their thresholds.
Example:
Imagine you have a piece of text. To assess its origin, the detector examines various elements, such as the use of certain words, sentence structures, and transitional phrases. It then calculates ratios and densities of these elements compared to the overall content.
For instance, let’s say the detector finds that the density of words typically associated with human sentiment, like “feel,” “know,” or “understand,” exceeds a certain threshold.
Additionally, it observes a high frequency of transitional words like “however” or “furthermore.” Based on these findings and predetermined thresholds, the detector also concludes that the text is more likely to be AI-generated.
It would then stand to reason that the text, based on statistical evidence is more likely to be AI as a total piece of content.
In summary, the detector’s output isn’t a binary verdict but a nuanced assessment, reflecting the likelihood of AI involvement in the text’s creation based on established thresholds and ratios across various linguistic features.
