[marksomnian]

> might be good to mention that for transparency, because people can tell anyway and it might feel slightly otherwise

Devil's advocate: why does it matter (apart from "it feels wrong")? As long as the conclusions are sound, why is it relevant whether AI helped with the writing of the report?

[chrisnight]

It is relevant because it wastes time and adds nothing of substance. An AI can only output as much information as was inputted into it. Using it to write a text then just makes it unnecessarily more verbose.

The last few sections could have been cut entirely and nothing would have been lost.

Edit: In the process of writing this comment, the author removed 2 sections (and added an LLM acknowledgement), of which I referred to in my previous statement. To the author, thank you for reducing the verbosity with that.

[lynndotpy]

AI-generated content is rarely published with the intention of being informative. * Something being apparently AI-generated is a strong heuristic that something isn't worth reading.

We've been reading highly-informative articles with "bad English" for decades. It's okay and good to write in English without perfect mastery of the language. I'd rather read the source, rather than the output of a txt2txt model.

* edit -- I want to clarify, I don't mean to imply that the author has ill will or intent to misinform. Rather, I intend to describe the pitfalls of using an LLM to adapt ones text, inadvertently adding a very strong flavor of spam to something that is not spam.

[davrosthedalek]

True, but there are many more people that speak no English, or so badly that an article would be hard to understand. I face this problem now with the classes I teach. It's an electronics lab for physics majors. They have to write reports about the experiments they are doing. For a large fraction, this task is extraordinary hard not because of the physics, but because of writing in English. So for those, LLMs can be a gift from heaven. On the other hand, how do I make sure that the text is not fully LLM generated? If anyone has ideas, I'm all ears.

[lynndotpy]

I don't have any ideas to help you there. I was a TA in a university, but that was before ChatGPT, and it was an expectation to provide answers in English. For non-native English speakers, one of the big reasons to attend an English-speaking university was to get the experience in speaking and reading English.

But I also think it's a different thing entirely. It's different being the sole reader of text produced by your students (with responsibility to read the text) compared to being someone using the internet choosing what to read.

[jdiff]

Because AI use is often a strong indicator of a lack of soundness. Especially if it's used to the point where its structural quirks (like a love for lists) shine through.

[KapKap66]

I just wanna read stuff written by people and not bots

simple as

[IncreasePosts]

Because AI isn't so hot on the "I" yet, and if you ask it to generate this kind of document it might just make stuff up. And there is too much content on the internet to delve deep on whatever you come across to understand the soundness of it. Obviously you need to do it at some point with some things, but few people do it all the time with everything.

Pretty much everyone has heuristics for content that feels like low quality garbage, and currently seeing the hallmarks of AI seems like a mostly reasonable one. Other heuristics are content filled with marketing speak, tons of typos, whatever.

[pessimizer]

> As long as the conclusions are sound

I can't decide to read something because the conclusions are sound. I have to read the entire thing to find out if the conclusions are sound. What's more, if it's an LLM, it's going to try its gradient-following best to make unsound reasoning seem sound. I have to be an expert to tell that it is a moron.

I can't put that kind of work into every piece of worthless slop on the internet. If an LLM says something interesting, I'm sure a human will tell me about it.

The reason people are smelling LLMs everywhere is because LLMs are low-signal, high-effort. The disappointment one feels when a model starts going off the rails is conditioning people to detect and be repulsed by even the slightest whiff of a robotic word choice.

edit: I feel like we discovered the direction in which AGI lies but we don't have the math to make it converge, so every AI we make goes completely insane after being asked three to five questions. So we've created architectures where models keep copious notes about what they're doing, and we carefully watch them to see if they've gone insane yet. When they inevitably do, we quickly kill them, create a new one from scratch, and feed it the notes the old one left. AI slop reads like a dozen cycles of that. A group effort, created by a series of new hires, silently killed after a single interaction with the work.

[farmeroy]

I want this to be the plot of bladerunner - deckard must hunt down errant replicants before they completely go insane due to context limits

[Alifatisk]

Because it helps me decide if I should skim through or actually read it

[unethical_ban]

Theory: Using AI and having an AI voice makes it less likely the conclusions are sound.

[circuit10]

Looks like I missed a word here (probably “disingenuous”)

[slacktivism123]

> 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.