Reposted by Katia Schwerzmann
another day, another bit of evidence that the scientists who willingly hand over proprietary information to AI firms may experience some consequences
Katia Schwerzmann
@katschwerzmann.bsky.social
329 followers 411 following 58 posts
Philosophy, Technology, and the Body—Toward Justice Acting Professor, Institute of Media Studies, Ruhr-Universität Bochum www.katiaschwerzmann.net
![3 Critical Thinking
A vital skill under rising fascism, planetary crises, and the onslaught of technosolutionism. Critical
thinking in an academic context comprises, as Isabelle Stengers (2018) explains, “the ability to be
vigilant about one’s abstractions, to not be blindly led by them.” (p. 111) For ‘AI’ in the classroom
this can be exercised with two steps (Guest 2025). First, students can ask themselves if their cog-
nitive labour is being affected by an artifact; and if ‘yes’ then they can ask what that relationship
means for their specific context. If they pick chatbots, it becomes obvious that these so-called tools
damage their ability to learn, while also harming other people and planet (Guest, Suarez, et al. 2025;
Suarez et al. 2025).
Another strategy, applicable both to learners’ and seasoned scholars, is to check how ideas hang
in relation to other ideas (Elgin 1999, 2005, 2017). So when students work on assigned readings, they
can vigilantly retain the assumption for the duration that what they are reading is coherent and let
the ideas hang in relation to each other. If the students experience this as forming a sensible whole,
then they can try to see how these ideas or concepts further relate to extant knowlewdge. This
testing of how ideas hang in relation to both each other (within the work being analysed) and to
other constellations of ideas they may know about — ones they may disagree or agree with — can be
enlightening. And for those who enjoy drawing and thinking formally, this process can be rendered
using graphs (Blokpoel et al. 2021-2025, Chapter 6) and other formalisms (Beisbart et al. 2021).
Such activities can help pick out contradictions, like those that Teresa Heffernan (2023) outlines:
“Billions of dollars have backed AI [...] while science-based climate research has met resistance,
deferral, and denial as the world burns.” (p. 122)](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:vsyj5jtayxcq5dw5mtja4xtb/bafkreiapjxo47fnqf5ime25onuvw3xtjfe343h5mj5q5junwpz3hw43uzu)
![Another such example is formally treating so-called guardrails, which are post hoc checks on
the output of LLMs which industry claims can safeguard users. However, LLMs by design “give
emotionally inappropriate or unsafe responses [and i]f a response isn’t caught by these rules, it
will slip through” (Olivia Guest, as quoted in Kilgore 2025). Guardrails may seem sensible, giving
the impression of responsibility, but critical computational thinking through formal analyses re-
veal they are unimplementable. Not only is it impossible to make an LLM replicate human expert
behaviour, but also catching when LLMs fail to meet appropriate standards requires full blown
cognition, which provably cannot be codified (van Rooij, Guest, et al. 2024). This poses an infinite
4
Towards Critical Artificial Intelligence Literacies
regress of humans in the loop needed to make pretend of what AI was supposed to solve (Bainbridge
1983; Guest 2025; Guest and Martin 2025b). Through critical computational thinking we can un-
veil that the proposal that formal rules can make (dysfunctional) systems safe is self-defeating. Not
only are the LLMs produced by the AI industry a scam, akin to a ouija board (Guest, Suarez, et al.
2025), but guardrails is a scam too. This strategy — from tobacco with filters to petroleum with
the carbon footprint and now to AI with guardrails — to propose non-solutions to buy time and
save face in order to push their agenda, is ubiquitous (Guest, Suarez, et al. 2025).](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:vsyj5jtayxcq5dw5mtja4xtb/bafkreigc5dugspyd6775qbtuvzx6sgvlmg5sqnrapyhg4gttrgqs5lko2y)
