It would be foolish to try to forbid Wikipedia contributors from using chatbots to help write articles: people would use them anyway, but would try to hide the fact. A ban would also be counterproductive. LLMs are simply tools, just like computers, and the real issue is not whether to use them, but how to use them properly.

While open access is a cornerstone of Wikipedia’s design principles, some worry the unrestricted scraping of internet data allows AI companies like OpenAI to exploit the open web to create closed commercial datasets for their models. This is especially a problem if the Wikipedia content itself is AI-generated, creating a feedback loop of potentially biased information, if left unchecked.

Encyclopedias are a compendium and summary of accepted human knowledge. Their purpose is not to provide compelling and interesting articles, but to provide accurate and verifiable information. To this end, encyclopedias strive to always represent each point-of-view in a controversy with an amount of weight and credulity equal to the weight and credulity afforded to it by the best sources of information on the subject. This means that the consensus of experts in a subject will be treated as a fact, whereas theories with much less acceptance among experts, or with acceptance only among non-experts will be presented as inaccurate and untrue.

Before you even begin to try to raise the issue at a talk page, you should ask yourself "Is this article really biased, or does it accurately reflect the views of authoritative sources about this subject?" Do some research. Read the sources used by the article and find other reliable sources on the subject. Do they present the subject as controversial, or do they tend to take a side? If there's a clear controversy, what field of study would impart expertise on this, and what side do people who work in that field tend to take? Do the claims made by the article match the claims made by the sources? Depending on the answers to these questions, the article may not be biased at all.

The Learn In Public movement has encouraged thousands of people to write, speak, draw, or otherwise pick up what mentors put down, with the end goal of lifelong L(N*P) growth in personal knowledge and network. A key part of this strategy is maintaining your own Digital Garden. A Digital Garden is your very own place (often a blog, or twitter account) to plant incomplete thoughts and disorganized notes in public - the idea being that these are evergreen things that grow as your learning does, warmed by constant attention and fueled by the unambiguous daylight of peer review. It is in part a trick for creators to play on themselves: For perfectionists who stress over shipping anything less-than-polished and therefore never ship anything, it is a license to trade off self review for peer review and increased velocity. Many report both improved quality and quantity of output after giving themselves the permission to do this.

People with audiences do of course have some obligation to not do them a disservice, else they don’t deserve that audience. However this doesn’t mean that they must do exhaustive due diligence and be authoritative in every context - there needs to be space to experiment, grow, and quite frankly, be ignorant and wrong.

I will “steelman” arguments - the opposite of “strawman arguments” - instead of picking on the weakest piece of their argument, I will confront head on their best argument by seeking first to understand before trying to be understood.

While open access is a cornerstone of Wikipedia’s design principles, some worry the unrestricted scraping of internet data allows AI companies like OpenAI to exploit the open web to create closed commercial datasets for their models. This is especially a problem if the Wikipedia content itself is AI-generated, creating a feedback loop of potentially biased information, if left unchecked.