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Chang Ruay-Shiung

The newest arms race in artificial intelligence (AI) is no longer about writing better text. It is about making AI-generated writing appear indistinguishable from human work.
A recently released academic “humaniser” tool promises to remove stylistic clues that reveal AI-generated manuscripts, allowing research papers and grant proposals to read more like they were written by humans. Its developer describes it as an editing aid rather than a deception tool, but many scientists fear it could encourage undisclosed AI use and further blur the boundary between authentic scholarship and machine-assisted writing.
This debate is often framed as a technological contest between AI generators and AI detectors. That is the wrong way to think about it. The real issue is trust.
Modern science depends on an unwritten social contract. Researchers trust that published methods were actually performed. Funding agencies trust that proposals represent applicants’ own intellectual contributions. Peer reviewers trust that manuscripts accurately reflect the author’s reasoning rather than automated fabrication. Without this shared confidence, scientific progress slows because every claim becomes subject to suspicion.
Generative AI is challenging this foundation more fundamentally than plagiarism ever did. Traditional plagiarism copied someone else’s ideas. Today’s large language models can generate entirely new text that appears original while concealing how much intellectual work was actually performed by the researcher. Humaniser tools represent the next stage of this evolution. They do not simply improve grammar or style – their purpose is to erase evidence that AI participated in the writing process.


Ironically, this could render AI detection increasingly futile. Current AI detectors already struggle when text is paraphrased or edited, while false positives continue to affect honest authors, particularly those writing in a second language.
If neither humans nor algorithms can reliably determine whether a document originated from AI, institutions must stop relying on detection as their primary safeguard. The future of scientific integrity will depend less on identifying AI-generated prose than on redesigning research governance itself.
This shift should begin with transparency. The question should no longer be whether researchers use AI. It already assists scientists in literature reviews, statistical programming, grant writing, language editing and experimental design. Particularly for researchers for whom English is a second language, these tools can reduce barriers rather than create them.
However, disclosure remains inconsistent. Instead of attempting to prohibit the use of AI, journals and funding agencies should require structured AI transparency statements similar to conflict-of-interest declarations. Authors should identify where AI contributed, how its output was verified and which scientific judgments remained exclusively human. This would not eliminate misconduct, but it would align incentives towards honesty rather than concealment.


AI also reminds us of an uncomfortable truth. Scientific credibility has never depended solely on elegant writing. A beautifully written paper has never guaranteed sound methodology. At the same time, some transformative discoveries were published in imperfect English by researchers whose first language was not English.
Humanising tools might make prose more polished, but they cannot validate experiments, confirm reproducibility or generate genuine scientific insight. Science advances because evidence survives independent scrutiny. That principle has not changed.
Asia occupies an increasingly influential position in global research. China has become one of the world’s largest producers of scientific publications. Hong Kong remains a major international research hub connecting East and West. Decisions made by institutions across the region will therefore shape international norms regarding responsible AI use in scholarship.
Rather than competing to build more sophisticated AI detectors, Asia has an opportunity to lead in developing governance models that recognise AI as a permanent feature of research while preserving accountability. The objective should be to verify knowledge, not merely authorship.




Imagine grant reviews placing greater emphasis on live interviews with applicants. Imagine doctoral examinations focusing more heavily on oral defence. Imagine journals requiring accessible data sets, reproducible code and independent validation alongside polished manuscripts. These measures evaluate scientific thinking rather than literary style. They are also much harder for AI to counterfeit.
The humaniser controversy should not be remembered simply as another chapter in the race between AI generation and detection. It signals something more profound. AI is gradually making authorship itself less visible. If institutions continue judging scholarship primarily through written documents, they will find themselves trapped in an endless technological contest against increasingly capable machines.
The better path is to strengthen the human dimensions of science that no language model can replace. Integrity is a commitment to truth rather than a writing style. In the age of AI, that commitment has become the most valuable research tool of all.


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Opinion

World Opinion

Tech

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If institutions judge scholarship just through writing, they will be trapped in an endless contest against increasingly capable machines


If institutions judge scholarship just through writing, they will be trapped in an endless contest against increasingly capable machines.


Education

Science

Expresses the views, ideas and conclusions of the author. (Eg. Enough of Crazy Rich Asians, think of crazy poor Hongkongers.)


As AI learns to hide, science must learn to trust differently

.css-1c6uqr6{color:inherit;font-weight:inherit;font-size:inherit;font-family:inherit;line-height:inherit;overflow-wrap:break-word;}If institutions judge scholarship just through writing, they will be trapped in an endless contest against increasingly capable machines

If institutions judge scholarship just through writing, they will be trapped in an endless contest against increasingly capable machines