I've spent weeks writing a paper on neural networks, but when I run it through AI detectors, it says I'm a bot. Is it because the topic itself is about AI, or is the writing style of data scientists naturally similar to how a machine would structure information?
3 answers
This is a documented phenomenon known as the "non-native speaker bias" or "technical bias" in AI detectors. In Data Science, we use a lot of passive voice and highly structured logical progressions. Machines are trained on this exact type of data. When you write "The results demonstrate a correlation between X and Y," you are using a high-probability word sequence. A detector sees that sequence and assumes it's artificial. To fix this, try incorporating more personal anecdotes or unique structural transitions that a standardized model wouldn't typically generate in a vacuum.
Have you tried running your abstract through a few different tools to see if the "bot" score changes based on the complexity of the math?
I've noticed that using a lot of "transition words" like 'furthermore' and 'consequently' almost always increases the AI score in these tools.
You're right, Sharon; those are "low-entropy" words that these algorithms are specifically trained to flag as potential markers of machine-generated text.
Math-heavy sections are a nightmare for detectors. They often see the precision of mathematical language as a sign of automation. I suggest scanning your text without the formulas first to see if the prose itself is what's causing the high probability score, as this can help you isolate the issue.