Misclassification of text in Ai detection: A serious limitation of Ai detectors and Its threats to human-based scholarly writing

Abstract

Dear Editor,

The rapid integration of AI into academic writing has necessitated tools for detecting AI-generated content such as iThenticate ZeroGPT, Turnitin, Phrasly AI, Open AI text Classifier, Writer, Copy leaks to differentiate between human and machine-generated text.¹ However, current AI detection tools suffer from significant misclassification rates, generating false positives that wrongly accuse human authors and false negatives that allow AI-generated text to evade detection.² This unreliability unduly impacts non-native English speakers, who often utilise AI tools for paraphrasing and grammar correction to ensure their work meets academic standards; however, these legitimate linguistic refinements are frequently misconstrued by detection software as evidence of AI-generated content.³ This problem is heightened by the fact that many AI detection tools, primarily trained on English corpora, struggle to accurately assess texts with diverse linguistic structures and stylistic conventions, thereby increasing the risk of false positives for non-English speaking scholars.² Such false positives, where human-written manuscripts are incorrectly labelled as AI-generated , severely threaten scholarly psychological safety by fostering distrust and creating an environment of anxiety and unfair allegations. This systemic issue fundamentally challenges academic integrity, undermining the credibility of authors and the foundation of human-based scholarly writing.^4,5 This editorial highlights the urgent need for human centric approach to AI detection, advocating for strategies that prioritise human-AI collaboration over sole reliance on fallible automated systems.

A fundamental flaw of current AI detectors lies in their documented inconsistency and unreliability. Studies consistently demonstrate high rates of both false positives, where human-written text is erroneously identified as AI-generated, and false negatives, where AI-generated content evades detection.^6,7 For instance, literature indicated that both free and commercially available AI detection tools can incorrectly classify human-written content as AI-generated with rates ranging from 43.3% to 83.3%.^7,8

The ethical concerns arise directly because of incorrectly labelling human-written manuscripts as AI-generated and vice versa. Such errors lead to unfair allegations, rejection of genuine work, unwarranted accusations of academic misconduct, and reputational damage for authors.^9,10 When authors must modify their writing or use "humaniser" tools to avoid false detection, it paradoxically increases AI involvement and further obscures human-machine authorship boundaries.⁸ Furthermore, the ease with which AI-generated text can be altered allows it to bypass current detection methods, turning the process into a counterproductive 'cat-and-mouse' game.¹ This eventually weakens the very goal of identifying AI misuse while simultaneously unjustly burdening diligent human scholars.⁴

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