Will there be errors in ai writing detection?

Yes, ai writing detection may encounter errors. The current ai writing detection tools have the problem of misjudgment when recognizing AI generated content. Many students have reported that AI detection of the same document at different times or on different platforms may result in different outcomes, and sometimes even contradictory situations. For example, some students perform AI detection on the same document on the same platform, with a detection rate of 10% on the first day and rising to over 20% on the second day; Some students' original content in their papers is determined by the system to be generated by AI; Some students have also found that even if the main content of a paper is formulas or classic articles by famous authors, the system will still show that the AI detection rate exceeds the standard.
In addition, the accuracy of AI detection tools has also been questioned by experts and scholars. A study found that 72 linguists and critics had an accuracy rate of less than 40% in distinguishing between AI writing and manual writing. This indicates that existing AI detection tools still face significant challenges and uncertainties in identifying AI generated content.
Reasons and influencing factors of errors
Data bias: AI detection tools are trained on a large amount of data, and if there is bias in the training data itself, it may lead to misjudgment.
Semantic coherence: AI generated content often has specific semantic coherence features, and the system cannot effectively identify cross paragraph semantic associations during segmentation detection, which may lead to misjudgment.
Theme consistency: Long texts generated by AI may exhibit significant theme deviations or logical inconsistencies, and segmentation detection cannot evaluate these macro level text features.
Writing style: AI generated content has differences in writing style compared to human writing, but these differences may be overlooked in segmentation detection.