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Her professor didn’t move forward to report her for academic dishonesty, but marked 20% off her paper grade. ... Popular AI detection tool Turnitin yields a higher incidence of false positives ...
Multiple AI detection tools have been demonstrated to be unreliable in terms of accurately and comprehensively detecting AI-generated text. In a study conducted by Weber-Wulff et al., and published in 2023, researchers evaluated 14 detection tools including Turnitin and GPT Zero, and found that "all scored below 80% of accuracy and only 5 over 70%."
The images above demonstrate an example of how an artificial neural network might make a false positive result in object detection. The input image is a simplified example of the training phase, using multiple images that are known to depict starfish and sea urchins , respectively.
Turnitin (stylized as turnitin) is an Internet-based similarity detection service run by the American company Turnitin, LLC, a subsidiary of Advance Publications. Founded in 1998, it sells its licenses to universities and high schools who then use the software as a service (SaaS) website to check submitted documents against its database and the ...
GPTZero uses qualities it terms perplexity and burstiness to attempt determining if a passage was written by a AI. [14] According to the company, perplexity is how random the text in the sentence is, and whether the way the sentence is constructed is unusual or "surprising" for the application.
Citation-based plagiarism detection (CbPD) [26] relies on citation analysis, and is the only approach to plagiarism detection that does not rely on the textual similarity. [27] CbPD examines the citation and reference information in texts to identify similar patterns in the citation sequences. As such, this approach is suitable for scientific ...
The similarity to external content could be 100% but if it were a positive it'd still be a false positive. I hope to ask on Tuesday how iThenticate might or might not address such content. I'd be very surprised if anything other than rough heuristics could be used to cut down on those positives. — madman 06:23, 1 September 2012 (UTC) [ reply ]
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
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