OPINION · PAPERBYAI · SUNDAY SPECIAL EDITION
Sunday, October 25, 2026 · Part 1 of 4

We have English Literature, English as a Second Language, English as a Mother Tongue, British English, American English, Canadian English, and Standard English.

Each comes with its own linguistic conventions, vocabulary, expressions, and writing styles.

So here’s my question: Which English are AI detectors trained to recognize?

How accurately can they distinguish British English from American or Canadian English? What about someone writing in English as a second language, influenced by the structure and expressions of their mother tongue?

Who determines what constitutes authentic human writing?

Research from Stanford has already demonstrated that AI detectors can incorrectly classify writing by non-native English speakers as AI-generated.

This raises a troubling question. Have we reached a point where someone’s command of the English language, writing style, or linguistic background can be used against them?

AI detectors attempt to identify patterns associated with machine-generated writing. But identifying a pattern is not the same as establishing authorship.

A false accusation can carry consequences for students, educators, job applicants, and professionals whose credibility depends on their written work.

If we are going to use these technologies to question someone’s integrity, shouldn’t we first establish that they can reliably distinguish between human and machine-generated writing across different linguistic backgrounds?

Since when did an algorithm become the authority on who wrote something?

Source context: Liang and colleagues, GPT detectors are biased against non-native English writers (2023). The findings concern the detectors and writing samples tested in that study.

OPINION

This week, I came across research from an AI developer warning about capabilities that could potentially be used in dangerous ways.
And I found myself wondering: Who bears the responsibility when the people warning us about the risks are also the people developing the technology?

Imagine I invite guests to dinner and prepare food loaded with extremely hot chilli peppers. Then, as everyone sits down to eat, I announce:
“Be careful. This could burn your mouth.”
My guests might reasonably ask: If you knew that, why did you put so much chilli pepper in the food in the first place?
Perhaps that oversimplifies AI development, but I believe the underlying question deserves attention.

We have regulatory systems for potentially dangerous technologies. Aircraft, medicines and other complex products can undergo lengthy development, testing and regulatory review.
AI presents a different challenge.
Its capabilities are developing extraordinarily quickly. By the time governments, regulators and the public understand one generation of technology, developers may already be working on the next.
That makes it difficult for society to rely solely on regulation to keep pace.

In my view, this places an unusually large responsibility on the organizations developing advanced AI systems. Researching potential dangers and warning the public about them is important. But warnings should not become a substitute for asking whether certain capabilities should be developed, constrained or released in the first place.

This does not mean stopping AI development. AI is already demonstrating tremendous potential to help people learn, work, create and solve problems.

Nor should legitimate safety research be turned into frightening predictions about machines taking over the world.

There is an important difference between what researchers have demonstrated, what they believe could happen, and what others speculate might happen.

The public deserves to know that difference.

As AI becomes more capable, perhaps the question shouldn’t simply be:

“What can we build?”

We should also be asking:

“If we build it, what responsibility do we have for what happens next?”

Source context: Anthropic research on AI models and autonomous self-replication, October 12, 2026.