When did we stop worrying about the efficiency of the tools we use? // I have read something like this in a post by @irisvanrooij.bsky.social
Iris van Rooij 💭
@irisvanrooij.bsky.social
Professor of Computational Cognitive Science | Dept. of Cognitive Science & Artificial Intelligence | @Iris@scholar.social on 🦣 | http://irisvanrooijcogsci.com | she/they 🏳️🌈
So happy it is helpful!! 💗 If you click through to Chapter 2 in the open textbook linked below, and then scroll to the section on Explaining capacities (see screenshot for first paragraph), you'll see an accessible explanation of different types of explanations that are relevant in this context.
Iris van Rooij 💭@irisvanrooij.bsky.social · 6h agoIf you would like to teach theoretical modeling skills to your psychology or cognitive science students, or would like to learn these skills yourself, check out our open online textbook: computationalcognitivescience.github.io/lovelace/
Do computer scientists ignore these real-world harms?
Psychological scientists predominantly prefer to ignore such real-world harms and instead ask: ‘How can AI benefit us?’" journals.sagepub.com/doi/10.1177/...
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"This AI hype cycle’s impact seems even worse than the ones that came before. With a devastating ecological footprint and the exploitation of hidden labour, hyped-AI serves to amplify discrimination and other social, economic and environmental injustices. ++ journals.sagepub.com/doi/10.1177/...
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Yes, you're the competition from the viewpoint of AI companies, so they'll try and close you down. And we - academics generally - also. The war on expertise is very much in progress.
If you would like to teach theoretical modeling skills to your psychology or cognitive science students, or would like to learn these skills yourself, check out our open online textbook: computationalcognitivescience.github.io/lovelace/
Thanks. In my early days, all that was documented so you could know. NAG (numerical algorithms group) took it over and it will still be documented. normpdf(x) works for |x|>10⁻³⁰⁰ or something. One of the AI issues is that it's not documentable. We remain in the dark.
This also links back to why AI as produced in 2020s is that different and why it's that different...
Olivia Guest · Ολίβια Γκεστ@olivia.science · 11mo ago7. the kinda appealing, but substantively indefensible, idea that somehow AI is different to other technology, like calculators, in a pedagogical context — but we totally ban a great deal of technology in the classroom. (Section 3.7 here doi.org/10.5281/zeno...) 9/n
I also think this one, and the one right below it. All these claims seem sensible when everyone around repeats them, I suspect.
Olivia Guest · Ολίβια Γκεστ@olivia.science · 11mo ago6. the extremely unhinged series of claims that without training them on how to be users of such systems that we somehow fail as teachers — truly ludicrous, utterly bizarre, and in fact directly contradicts other industry selling points. (Section 3.6 here doi.org/10.5281/zeno...) 8/n
Also I think you may benefit from thinking about some of these. Maybe because I see them a lot and they are very difficult to escape without help. Let me know if they are helpful.
Olivia Guest · Ολίβια Γκεστ@olivia.science · 11mo ago2. the strange but often repeated cultish mantra that we need to "embrace the future" — this is so bizarre given, e.g. how destructive industry forces have proven to be in science, from petroleum to tobacco to pharmaceutical companies. (Section 3.2 here doi.org/10.5281/zeno...) 4/n
![3.2 We do not have to ‘embrace the future’ & we can turn back the tide
It must be the sheer magnitude of [artificial neural networks’] incompetence that makes
them so popular.
Jerry A. Fodor (2000, p. 47)
Related to the rejection of expertise is the rejection of imagining a better future and the rejection
of self-determination free from industry forces (Hajer and Oomen 2025; Stengers 2018; van Rossum
2025). Not only AI enthusiasts, but even some scholars whose expertise concentrates on identifying
and critically interrogating ideologies and sociotechnical relationships — such as historians and gender scholars — unfortunately fall prey to the teleological belief that AI is an unstoppable force. They
embrace it because alternative responses seem too difficult, incompatible with industry developments,
or non-existent. Instead of falling for this, we should “refuse [AI] adoption in schools and colleges,
and reject the narrative of its inevitability.” (Reynoldson et al. 2025, n.p., also Benjamin 2016; Campolo and Crawford 2020; CDH Team and Ruddick 2025; Garcia et al. 2022; Kelly et al. 2025; Lysen
and Wyatt 2024; Sano-Franchini et al. 2024; Stengers 2018). Such rejection is possible and has historical precedent, to name just a few successful examples: Amsterdammers kicked out cars, rejecting
that cycling through the Dutch capital should be deadly. Organised workers died for the eight-hour
workday, the weekend and other workers’ rights, and governments banned chlorofluorocarbons from
fridges to mitigate ozone depletion in the atmosphere. And we know that even the tide itself famously
turns back. People can undo things; and we will (cf. Albanese 2025; Boztas 2025; Kohnstamm Instituut 2025; van Laarhoven and van Vugt 2025). Besides, there will be no future to embrace if we deskill
our students and selves, and allow the technology industry’s immense contributions to climate crisis](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:vsyj5jtayxcq5dw5mtja4xtb/bafkreidzio5vcvzus3xjqxcpav64pwk2gwslwslogabl7b5dzhn6egf37i)
Who rejects all AI?
Olivia Guest · Ολίβια Γκεστ@olivia.science · last yr.I split AI into 3 non-mutually exclusive types (see Table 1 above): displacement (harmful), enhancement (beneficial), and/or replacement (neutral) of human cognitive labour. More later possibly, but see Tables 2 to 4 (attached or here: arxiv.org/pdf/2507.19960) for the worked through examples. 2/n
One upshot is that it *really* matters what kind of approximation, with what kinds of guarantees (e.g., value, structure, how close), one is making claims about. One cannot just claim “this intractable f is approximable with this A_approx", without precise definitions and formal proof. 21/🧵
"As the AI summer rolls on with heatwave upon heatwave, we directly experience its damage. We witness severe deskilling to academic reading, to essay writing, to deep thinking, even to scholarly discussions between students, which are all now seen as acceptably outsourced to AI products ..."
Olivia Guest · Ολίβια Γκεστ@olivia.science · 11mo ago💛🚫🤖 No AI Gods, No AI Masters 🤖🚫💛 I am massively excited to share the backstory ACADEMIC SHENANIGANS behind our Open Letter (& so this paper below too) — and as always big thanks to my co-authors {@irisvanrooij.bsky.social & @marentierra.bsky.social}: www.civicsoftechnology.org/blog/no-ai-g... 1/n
Deskilling is a huge concern. It's terrifying that students and academics convince themselves that AI can substitute for their own critical faculties or skill development.
Olivia Guest · Ολίβια Γκεστ@olivia.science · 11mo ago💛🚫🤖 No AI Gods, No AI Masters 🤖🚫💛 I am massively excited to share the backstory ACADEMIC SHENANIGANS behind our Open Letter (& so this paper below too) — and as always big thanks to my co-authors {@irisvanrooij.bsky.social & @marentierra.bsky.social}: www.civicsoftechnology.org/blog/no-ai-g... 1/n
💛🚫🤖 No AI Gods, No AI Masters 🤖🚫💛 I am massively excited to share the backstory ACADEMIC SHENANIGANS behind our Open Letter (& so this paper below too) — and as always big thanks to my co-authors {@irisvanrooij.bsky.social & @marentierra.bsky.social}: www.civicsoftechnology.org/blog/no-ai-g... 1/n
No AI Gods, No AI Masters — Civics of Technology
More on their open letter, Stop the Uncritical Adoption of AI Technologies in Academia , and position piece, Against the Uncritical Adoption of 'AI' Technologies in Academia , by Guest et al.
civicsoftechnology.org
Olivia Guest · Ολίβια Γκεστ@olivia.science · 11mo agoFinally! 🤩 Our position piece: Against the Uncritical Adoption of 'AI' Technologies in Academia: doi.org/10.5281/zeno... We unpick the tech industry’s marketing, hype, & harm; and we argue for safeguarding higher education, critical thinking, expertise, academic freedom, & scientific integrity. 1/n
Published with some minor changes! Please share widely. 😊 Guest, O., Suarez, M., ... & van Rooij, I. (2026). Against the Uncritical Adoption of 'AI' Technologies in Academia. Digital Culture & Education, 16(2), 85–118. doi.org/10.5281/zeno... Journal: www.digitalcultureandeducation.com/volume-162
Volume 16.2 — Digital Culture & Education (ISSN: 1836-8301)
digitalcultureandeducation.com
Olivia Guest · Ολίβια Γκεστ@olivia.science · 11mo agoFinally! 🤩 Our position piece: Against the Uncritical Adoption of 'AI' Technologies in Academia: doi.org/10.5281/zeno... We unpick the tech industry’s marketing, hype, & harm; and we argue for safeguarding higher education, critical thinking, expertise, academic freedom, & scientific integrity. 1/n
The authors formalize the problem of hallucinations in Large Language Models (LLMs) and show that their complete elimination is fundamentally impossible. arxiv.org/abs/2401.11817
Hallucination is Inevitable: An Innate Limitation of Large Language Models
Hallucination has been widely recognized to be a significant drawback for large language models (LLMs). There have been many works that attempt to reduce the extent of hallucination. These efforts hav...
arxiv.org
More likely they were indeed heuristics, but what the people did not tell you is this: bsky.app/profile/iris...
Iris van Rooij 💭@irisvanrooij.bsky.social · 21h agoSo heuristics make no promises. They may succeed on some inputs, they may fail on many inputs. There are no guarantees, they may fail massively, terribly, and often. Using heuristics is more like faith or hope than computer science or engineering. So what about approximation algorithms? 17/🧵
Value- vs structure-approximability are *fully dissociable*. IOW, an output may be close to the optimal value, yet arbitrarily off in terms of structure, and vice versa, an output may be close to the required structure, yet arbitrarily off in terms of value. Illustrations in the figures. 20/🧵
Also to underline, I assume since you work at a university you don't have issues with this one. But many really do these days!
Olivia Guest · Ολίβια Γκεστ@olivia.science · 11mo agoWe also go through many arguments that can be used as counters to typical false frames forced upon us, such as: 1. the powerful nonsense that we as experts know nothing (Section 3.1 here doi.org/10.5281/zeno...) 3/n
![3.1 Rejection of expertise, ironically including our own
Being in a colonizing discipline first demands and then encourages an attitude that might
be called intellectual hubris. Furthermore, since you cannot master all the disciplines that
you have designs on, you need confidence that your knowledge makes the ‘traditional
wisdom’ of these fields unworthy of serious consideration. Here too, the AI scientist
feels that seeing things through a computational prism so fundamentally changes the
rules of the game in the social and behavioural sciences that everything that came before
is relegated to a period of intellectual immaturity.
Sherry Turkle (1984, p. 230)
Every field that comes into contact with AI discourse becomes infected even within AI as a field of
study (recall Table 1). Our colleagues have embraced these systems, uncritically incorporating them
into their workflows and their classrooms, without input from experts on automation, cognitive science, computer science, gender and diversity studies, human-computer interaction, pedagogy, psychology, and law to name but a few fields with direct relevant expertise (Sloane et al. 2024). Meanwhile, technology companies have rushed to invest in ‘AI ethics’ or ‘AI safety’ to ethics wash their
claims, thereby “laundering accountability” (as Abeba Birhane explains in Arseni 2025) and “distracti[ng] from real AI ethics” (Crane 2021), while censoring academics and thus, violating academic
freedom (Gebru and Torres 2024; Gerdes 2022; Goudarzi 2025; Munn 2023; Ochigame 2019; Suarez
et al. 2025; Tafani 2023).](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:vsyj5jtayxcq5dw5mtja4xtb/bafkreihavj4y4hvuqmervolkakodhui7qozn2jmnsrav5lilv37cecg6bq)
Poll! 📊 Even though Bluesky does not have the nice automatic poll option, maybe people still want to share their intutions 😊
Iris van Rooij 💭@irisvanrooij.bsky.social · 17h agoI am also curious about your intuitions about how often an intractable function may be tractably approximable. What do you think? Pick one of the below options: a) always b) often c) sometimes d) seldom e) never 22/🧵
Let me know if this is helpful and/or if you have any clarification questions, feel free to ask. I can also recommend more resources if needed/useful.
Iris van Rooij 💭@irisvanrooij.bsky.social · 4d agoGoing to do a thread, using some of my own (incl. older) work, to clear up some of these relevant concepts. Hope it helps builds conceptual hygiene and immunity to hyped claims about “AI” capabilities. 1/🧵
Let me know if this is helpful and/or if you have any clarification questions, feel free to ask. I can also recommend more resources if needed/useful.
Iris van Rooij 💭@irisvanrooij.bsky.social · 4d agoGoing to do a thread, using some of my own (incl. older) work, to clear up some of these relevant concepts. Hope it helps builds conceptual hygiene and immunity to hyped claims about “AI” capabilities. 1/🧵
It's so much worse because schools are handing them their first cigarette and saying you need to use this.
Poll! 📊 Even though Bluesky does not have the nice automatic poll option, maybe people still want to share their intutions 😊
Iris van Rooij 💭@irisvanrooij.bsky.social · 17h agoI am also curious about your intuitions about how often an intractable function may be tractably approximable. What do you think? Pick one of the below options: a) always b) often c) sometimes d) seldom e) never 22/🧵
They: “We cannot ban AI from the classroom!” Us: “Would you allow students to smoke in class?” -- It is still possible to do the right thing. You can sign our Open Letter here: openletter.earth/open-letter-... 🖋️
