> As long as the conclusions are sound, why is it relevant whether AI helped with the writing of the report?

TL;DR: Because of the bullshit asymmetry principle. Maybe the conclusions below are sound, have a read and try to wade through ;-)

Let us address the underlying assumptions and implications in the argument that the provenance of a report, specifically whether it was written with the assistance of AI, should not matter as long as the conclusions are sound.

This position, while intuitively appealing in its focus on the end result, overlooks several important dimensions of communication, trust, and epistemic responsibility. The process by which information is generated is not merely a trivial detail, it is a critical component of how that information is evaluated, contextualized, and ultimately trusted by its audience. The notion that it feels wrong is not simply a matter of subjective discomfort, but often reflects deeper concerns about transparency, accountability, and the potential for subtle biases or errors introduced by automated systems.

In academic, journalistic, and technical contexts, the methodology is often as important as the findings themselves. If a report is generated or heavily assisted by AI, it may inherit certain limitations, such as a lack of domain-specific nuance, the potential for hallucinated facts, or the unintentional propagation of biases present in the training data. Disclosing the use of AI is not about stigmatizing the tool, but about providing the audience with the necessary context to critically assess the reliability and limitations of the information presented. This is especially pertinent in environments where accuracy and trust are paramount, and where the audience may need to know whether to apply additional scrutiny or verification.

Transparency about the use of AI is a matter of intellectual honesty and respect for the audience. When readers are aware of the tools and processes behind a piece of writing, they are better equipped to interpret its strengths and weaknesses. Concealing or omitting this information, even unintentionally, can erode trust if it is later discovered, leading to skepticism not just about the specific report, but about the integrity of the author or institution as a whole.

This is not a hypothetical concern, there are numerous documented cases (eg in legal filings https://www.damiencharlotin.com/hallucinations/) where lack of disclosure about AI involvement has led to public backlash or diminished credibility. Thus, the call for transparency is not a pedantic demand, but a practical safeguard for maintaining trust in an era where the boundaries between human and machine-generated content are increasingly blurred.