Why Does the Accuracy of AI Detection Depend on Humans? The Reason Human-Likeness is Key

Introduction: Can "Human-Likeness" Be Measured by Algorithms?

As generative AI is widely used in writing, advertising, and reporting, the repeated challenge at the forefront is the "difficulty of judgment regarding detection results." An article published on October 27, 2025, highlights the psychology that readers prefer what "feels human-like," even if they can't distinguish between machine and human. It argues that the final push for detection accuracy comes from human editing and judgment. Although it is sponsored content, it aligns with the practical sense in marketing and education fields. Particularly noteworthy is the point that "emotional fluctuations and voice" can influence evaluations. The Rocky Mountain Collegian

Why the "Limits" of Detection Tools Persist

1) The Ceiling of "Accuracy Rate"

OpenAI discontinued its AI text classifier in July 2023 due to low accuracy, publicly acknowledging the reality that even the world's top research organizations find stable detection challenging. OpenAI

2) Misjudgment and Bias

Various studies and reports have pointed out that detectors are prone to misjudging human writing as "AI," particularly disadvantaging non-native English speakers. For instance, the U.S. nonprofit media The Markup reported experimental results showing non-native writing is easily misjudged as AI. Misjudgments (false accusations) in educational settings are not uncommon. themarkup.org

3) Ease of Evasion

Detectors rely on "features," making them easy to evade through paraphrasing or regeneration. Inside Higher Ed introduces expert opinions stating that they have not reached a reliable level of accuracy in practical use. insidehighered.com

Movements on the Ground: "Do Not Overtrust" as a Slogan

In educational institutions, there has been an early move to stop using detection alone. For example, Vanderbilt University has disabled Turnitin's AI detection and publicly explained concerns about misjudgment and discrimination against non-natives. Recently, Australia's university regulatory authority TEQSA has warned that "reliable detection is difficult," encouraging a return to evaluation forms that ensure "authenticity," such as oral examinations and practical tests. Vanderbilt University

How Social Media is Reacting

On X (formerly Twitter) and Reddit, threads about detector misjudgments regularly go viral. Many posts express dissatisfaction and anxiety, such as "proving innocence is disadvantageous to students" and "individual writing styles are suspected of being 'AI-like.'" Recently, educational communities have raised concerns about misjudgments and legal risks. Furthermore, a 2025 academic survey extracted a tendency in public discussions (SNS/boards) where "students express anger at misjudgment harm, while teachers are caught between AI utilization and misconduct." In other words, social media serves as a platform for voices demanding "fairness in procedures" and "routes for appeals," rather than debating the pros and cons of detection. Reddit

Insights from Sponsored Articles: "AI Tuned by Humans"

The problem is making detection tools "judges." The opening article reinterprets AI detectors as "signals for editing." Machines indicate "which paragraphs are monotonous" and "where the voice is missing," and humans restore "fluctuations" and "narratives" there. Marketing field episodes (where engagement dropped due to AI but recovered with human-led drafts and tone checks) are also introduced. The key point here is designing to confine detectors to one step in the evaluation and editing workflow. The Rocky Mountain Collegian

Applying to Practice: Techniques to Stop at "Reference Value"

1. Two-Pronged Evidence:
   Detection scores are "supplementary evidence." Combine submission logs (creation history, draft differences), citation management, and oral confirmation. In evaluation, assessment, and internal audits, prohibit conclusions based solely on scores. The Australian

2. Standard Procedures for Appeals:
   Clearly define relief procedures for misjudgments (responsible person, deadline, re-evaluation protocol, conditions for oral examination). Refer to precedents in educational institutions (disabling detection functions, introducing oral evaluations). Vanderbilt University

3. "Human-Likeness" Editing Check (Content Operation):
   (α) AI is fine for the first generation.
   (β) In the second, humans add "voice," "space," and "deviation."
   (γ) In the third, use detection tools to identify monotonous parts or rhythm disruptions and re-edit.
   (δ) Finally, confirm in one line "who is being addressed." The Rocky Mountain Collegian

4. Bias and Quality Check:
   Locally verify with a test corpus whether the writing style of non-native speakers is unfairly judged as "AI-like." Pay particular attention in HR, admissions, and peer review. themarkup.org

5. "Undetectable by Design":
   Assume complete detection is impossible and combine source provenance proof (prompt records, draft history, version control) with identity verification evaluations (oral, practical). The Australian

Counterarguments to Common Misunderstandings

• "Is a Paid Detector Safe?"
  There is verification that paid detectors show a certain level of stability, but they are not free from misjudgments. Overconfidence is prohibited. Business Insider

• "Can Detection Eradicate Fraud?"
  Evasion is relatively easy, and the "cat-and-mouse game" of detection and evasion continues. Detection is just one element of deterrence. insidehighered.com

• "Punish the Suspicious?"
  On the contrary. The social and academic costs of misjudgment are high, and ensuring procedural fairness is paramount. themarkup.org

Conclusion: AI as the Compass, Humans at the Helm

AI detection is useful but not omnipotent. Therefore,(1) detection as a reference value, (2) duplication of evidence, (3) institutionalization of appeals, (4) redefining editing as a human roleare crucial. What readers, students, and customers trust is not 100% detection accuracy, but the transparency of the process of "how it was judged and explained." The 2025 discussions indicate not a technological performance race but aredesign of procedures and responsibilities. The Rocky Mountain Collegian