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Cited by 22Academic integrity and plagiarismAuthorship Attribution and ProfilingEthics and Social Impacts of AI

The accuracy-bias trade-offs in AI text detection tools and their impact on fairness in scholarly publication

Open access
Pratama, Ahmad R.·2025·PeerJ Computer Science
DOI 10.7717/peerj-cs.2953PMC PMC12453642
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Abstract

Artificial intelligence (AI) text detection tools are considered a means of preserving the integrity of scholarly publication by identifying whether a text is written by humans or generated by AI. This study evaluates three popular tools (GPTZero, ZeroGPT, and DetectGPT) through two experiments: first, distinguishing human-written abstracts from those generated by ChatGPT o1 and Gemini 2.0 Pro Experimental; second, evaluating AI-assisted abstracts where the original text has been enhanced by these large language models (LLMs) to improve readability. Results reveal notable trade-offs in accuracy and bias, disproportionately affecting non-native speakers and certain disciplines. This study highlights the limitations of detection-focused approaches and advocates a shift toward ethical, responsible, and transparent use of LLMs in scholarly publication.

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  • The disparity in false positive rates between non-native and native English writers has been replicated across different detection platforms, linguistic populations, and testing conditions.

    “This disparity has been replicated across different detection platforms, linguistic populations, and testing conditions”

    From ARE AI DETECTORS FAIR TO EFL LEARNERS? A LIBRARY RESEARCH ON DETECTION BIAS AND ACADEMIC INTEGRITY (2026) · cites this paper as Pratama, 2025

    In Results, Scenario 1 (Table 4; page not provided), the paper says false-positive-rate differences between native and non-native authors were not consistent across tools. ZeroGPT had a higher rate for native authors, DetectGPT had a higher rate for non-native authors, and GPTZero showed no difference. The consistent disparity reported in Scenario 2 concerns over-detection of AI-assisted abstracts, not false positives. (LLM, conf=0.90)

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  • Conventional digital divide models fail to sufficiently address the psychological impacts of unequal access, including low self-efficacy, increased anxiety, or identity threat.

    “Although these models can be applied in identifying structural differences, they fail to sufficiently address the psychological impacts of unequal access, including low self-efficacy, increased anxiety, or identity threat, which this review has found to be important mediating processes (Pratama, 2025; Beckman et al., 2025).”

    From Mapping the impact of generative AI in higher education: A scoping review of psychological and equity dimensions. (2026) · cites this paper as Pratama, 2025

    The Introduction discusses bias in AI-text detection by author background, not conventional digital divide models. Searches for digital divide, self-efficacy, anxiety, and identity threat found no passage evaluating those models or linking unequal access to the claimed psychological impacts; the self-efficacy mention in Materials and Methods is part of an example abstract. (LLM, conf=0.78)

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Cited by

Sorted by citations

Authors
Pratama, A. R.
Affiliations
State University of New York (US)
Corresponding author
Pratama, Ahmad R.
ORCID
Pratama, Ahmad R.
Venue
PeerJ Computer Science
Publisher
PeerJ
Published
2025-06-23
Volume
11
Pages
e2953
ISSN
2376-5992 (ISSN-L)
DOI
10.7717/peerj-cs.2953
PMC
PMC12453642
OpenAlex
W4411534675
Cited by
22
References
12
Citations by year
2026: 20 · 2025: 2
FWCI
22.76
Type
journal article
Language
en
Open access
Licence
Locations
  • PeerJ Computer Science (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
AI-generated text detection, AI detection tools, GPTZero, large language models, ChatGPT, Gemini, human-written text, AI-generated text, fairness, non-native speakers, AI-assisted writing, readability, responsible AI use

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