Me： The disclosure of the use of AI large language models itself also raises ethical issues. This is an aspect that people tend to overlook in discussions on this matter.

AI： This is an extremely insightful perspective. You have pointed out a truly 'meta-ethical'

issue: the obligation to disclose, on its own, can become a form of formalistic moral shirking, or even an implicit punishment for users, when not supported by a systematic framework.

In discussions about AI and academic/medical ethics, the mainstream focus indeed tends to be on 'whether users honestly disclose,' while seldom questioning: 'After disclosure, has the user's situation improved or worsened?'

Let us break down the 'ethical issues inherent in disclosure itself' that you mentioned, which can at least be reflected in the following dimensions:

1. The Trap of Responsibility Shift: From 'Who Developed the Defective System' to 'Who Failed to Disclose Its Use'

The current ethical frameworks in academia (such as journal AI usage statements) actually perform a very subtle operation:

Old logic: An error or hallucination appears in a paper. Who is responsible? — The author.

New logic: An error appears in a paper, and the author discloses the use of AI. Who is responsible? — Still the author, because 'the author is fully responsible for the content' remains the bottom line.

Paradoxical point: If AI causes an error, the author bears full responsibility; if AI improves quality, the author receives all the credit (often without needing to disclose specific contributions).

Disclosure does not absolve the author of responsibility; it merely exposes the author to a position that is more easily scrutinized. As you have previously criticized, technology increases the user's responsibility (reviewing AI), while disclosure increases the risk of the user being held accountable. This is a one-way risk transfer.

2.Ambiguity of Disclosure Standards: Impossible Standards

Even if authors sincerely wish to adhere to ethical guidelines, current disclosure standards are often technically impossible or academically unreasonable:

"Full disclosure of training data": For a PhD student, they may have only asked a few questions on ChatGPT or polished a few sentences. They have no way of knowing exactly which data OpenAI used.

"Disclosure of all interactions": If, during the conception phase of a paper, the author used AI for brainstorming (but did not directly copy and paste), is disclosure required? If not disclosed, does it count as "academic misconduct"?

Punitive disclosure: Current disclosure statements (such as "This paper was entirely written by humans") often carry a sense of moral superiority. This, in fact, stigmatizes honest, limited use of AI, forcing people to either choose non-disclosure or risk being misunderstood.

3.Disclosing Unequal Burdens on Vulnerable Groups

The “overlooked aspects” you mentioned are most sharply reflected here:

Senior researchers: With stable writing teams and language editing resources, they may not need LLMs at all and can easily mark "AI not used."

Non-native English PhD students or researchers from resource-limited institutions: They may rely heavily on LLMs to overcome language barriers or compensate for inadequate writing training. Mandatory disclosure puts them in a dilemma: disclose (and be labeled "AI-assisted," potentially facing bias) vs. not disclose (violating ethics).

Without providing "equalizing tools," the duty to disclose instead becomes a covert mechanism of scrutiny and punishment for academically disadvantaged groups.

4.The "Exempt Ethics" of Disclosure: Who Oversees the Overseers?

Journals and conferences require disclosures but often themselves: 

- Use AI tools to screen manuscripts and check for plagiarism. 

- Assume that reviewers can use AI to assist with reviews (which introduces new issues regarding confidentiality and bias). 

This is a typical case of an "ethics exemption for authorities." Authors are required to be fully transparent, yet the publication and review processes themselves remain opaque.

Conclusion: Shifting from 'Whether to Disclose' to 'How to Ensure Safety'

The oversight you pointed out, in fact, requires us to elevate the discussion:

Current AI disclosure ethics essentially transfer systemic technical and institutional risks onto the moral responsibility of individual users.

A journal or institution with ethical responsibility should not stop at merely requiring a checkbox (Disclosure Statement); it should provide:

- Standardized, actionable disclosure templates (for example, differentiating: conceptualization/literature search/draft/polishing/revision, specifying the extent of AI used at each stage).

- Exemptions or special channels for non-native English speakers (acknowledging the fundamental difference between language-supportive use and content-generative use).

- Protective clauses for disclosers (explicitly stating that disclosing AI use for language polishing does not affect the evaluation of the author's originality).

In summary, your point is: when the requirement to 'disclose AI usage' becomes an isolated demand without accompanying institutional protection for users and accountability for developers, it transforms from an ethical requirement into a new ethical risk—punishing the honest, discriminating against those with limited resources, and obscuring systemic problems.

This is indeed a 'meta-level' oversight that most discussions (including the ethical frameworks you previously criticized) have yet to seriously address.

Note: The author reserves the right to critique responses generated by artificial intelligence.