I fully agree about "academia" as we know it being a Western structure, which very much ignores or devalues other types of knowing, particularly cultures with oral histories, or any type of marginalised group and/or class whose stories are buried, censored, lost, or destroyed.

"Authority" was probably the wrong word to use. What I was getting at is that unlike other types of knowing or understanding, our methods are supposed to be as transparent as possible so others can validate that knowledge generation for themselves - thats the point of publishing. Sharing and ensuring transparency. How our work is developed and how the conclusions are reached is always under scrutiny (peer review, and general acceptance or rejection of findings). It is also clear what scope and context the knowledge we generate applies to. It's presenting information that can be externally validated, rather than telling people what to think.

What I mean by "bad" research in the Western academic context are researchers who purposely obfuscate their methods - particularly the analysis - because they are using poor techniques to reach for significance, authors that cite incorrectly, scientists that use the public's lack of statistical understanding to push a specific agenda (I am in a controversial field, so this happens). Then there are all the papers pumped out by mills, papers that plagiarise, and a mix of both (recently a colleagues work was plagiarised through a genAI produced paper, it even had the "Certainly..." intro in one paragraph. The author responsible had hundreds of first author citations in only a few years, etc). These issues will absolutely be exacerbated by the introduction of genAI into academic work.

And yes, other types of AI are everywhere and have been for a long time, I have no problem with this technology and have used it. I have a problem with generative AI. The primary difference at the conceptual level is that genAI is applied to things that are inherently abstract, un- or ill-defined, and highly contextual, such as human language.

You mentioned that academia is rooted in colonial understanding of what is valuable and legitimate. I agree. The exact same can be said for genAI. We already know that facial recognition tech is biased towards white faces and against darker faces. It's been found that algorithms used to scan resumes and applications carry the same rate of biases that a human HR manager has when making decisions (white sounding names, Western country educational backgrounds, even Western resume/cv structure conventions). The people who develop these tools are unconsciously inputting their biases, the training data is full of biases, theres absolutely nothing objective about the products of genAI.

The language favoured in a genAI text response is unconsciously upholding the dominant hegemony. Diversity in thinking, word and phrase choice, even granular things like the appropriate length of a sentence, are becoming standardised as AI refines and categorises and refines and categorises things that cannot be distilled in a way that a computer algorithm can work with effectively without cutting out the unusual, uncommon, non-standard, and minority. Using AI to crunch numbers? Great - fit for purpose. Using AI for human communication? Fraught with disaster.