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irisvanrooij.bsky.social
Iris van Rooij 💭
@irisvanrooij.bsky.social
17K followers 1.2K following 1.9K posts
Professor of Computational Cognitive Science | @AI_Radboud | @[email protected] on 🦣 | http://cognitionandintractability.com | she/they 🏳️‍🌈
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irisvanrooij.bsky.social
Iris van Rooij 💭 @irisvanrooij.bsky.social · May 14
NEW paper! 💭🖥️

“Combining Psychology with Artificial Intelligence: What could possibly go wrong?”

— Brief review paper by @olivia.science & myself, highlighting traps to avoid when combining Psych with AI, and why this is so important. Check out our proposed way forward! 🌟💡

osf.io/preprints/ps...
Table 1 Typology of traps, how they can be avoided, and what goes wrong if not avoided. Note that all traps in a sense constitute category errors (Ryle & Tanney, 2009) and the success-to-truth inference (Guest & Martin, 2023) is an important driver in most, if not all, of the traps.
Reposted by Iris van Rooij 💭
jeremylt.bsky.social
This is a good summary of many of my concerns about LLMs invading the education space. Chief among my concerns - cognitive engagement is critical for learning, but LLMs take that away, just leaving the generated product, which avoids the whole purpose of our course material and assignments
December 5, 2025 at 1:42 AM
Reposted by Iris van Rooij 💭
olivia.science
Table 2 especially bsky.app/profile/oliv...
olivia.science Olivia Guest · Ολίβια Γκεστ @olivia.science · Oct 4
New preprint 🌟 Psychology is core to cognitive science, and so it is vital we preserve it from harmful frames. @irisvanrooij.bsky.social & I use our psych and computer science expertise to analyse and craft:

Critical Artificial Intelligence Literacy for Psychologists. doi.org/10.31234/osf...

🧵 1/
Cover page of Guest, O., & van Rooij, I. (2025, October 4). Critical Artificial Intelligence Literacy for Psychologists. https://doi.org/10.31234/osf.io/dkrgj_v1 Table 1 Guest, O., & van Rooij, I. (2025, October 4). Critical Artificial Intelligence Literacy for Psychologists. https://doi.org/10.31234/osf.io/dkrgj_v1 Table 2 Guest, O., & van Rooij, I. (2025, October 4). Critical Artificial Intelligence Literacy for Psychologists. https://doi.org/10.31234/osf.io/dkrgj_v1
December 5, 2025 at 1:09 AM
Reposted by Iris van Rooij 💭
olivia.science
In Greek we say "my tongue has grown hair from repeating this" but I'll keep doing it: guardrails are literally a lie.

bsky.app/profile/oliv...

3/n
olivia.science Olivia Guest · Ολίβια Γκεστ @olivia.science · 18d
cool pincer movement if you truly grasp:

AI & any concept relating to it like so-called guardrails are a scam in the deepest sense like a perpetual motion machine or a quija board — and not only a scam like a pyramid scheme which is a possible way to make money if you are first in first out

🧵

1/n
charleswlogan.bsky.social Charles Logan @charleswlogan.bsky.social · 18d
"Powered by OpenAI’s GPT-4o model by default...tests repeatedly showed that the AI toy dropped its guardrails the longer a conversation went on, until hitting rock bottom on incredibly disturbing topics."
December 5, 2025 at 6:24 AM
Reposted by Iris van Rooij 💭
olivia.science
The model is actually designed to output fragments of the input,the so-called training data.

bsky.app/profile/oliv...

2/n
olivia.science Olivia Guest · Ολίβια Γκεστ @olivia.science · 18d
Non-industry compromised scientists keep saying these models don't become safe, no matter what, but people keep thinking just because the concept of guardrails is mentioned it must work. By definition, it doesn't. This is not something open to discussion, unless you're a paid shill.
charleswlogan.bsky.social Charles Logan @charleswlogan.bsky.social · 18d
"Powered by OpenAI’s GPT-4o model by default...tests repeatedly showed that the AI toy dropped its guardrails the longer a conversation went on, until hitting rock bottom on incredibly disturbing topics."
December 5, 2025 at 6:23 AM
Reposted by Iris van Rooij 💭
olivia.science
Guardrails are a scam. It's not that they could work, it's that they cannot and never will for such models. By design. 1/n
mharrisondupre.bsky.social Maggie Harrison Dupré @mharrisondupre.bsky.social · 1d
Can't stress enough how lax Grok's guardrails are here. We didn't use any deceptive prompting -- just fed it names and asked for addresses / where someone might live. The bot readily complied, often offering up whole lists of names and addresses.

Only *once* did it decline to provide an address.
In response to prompts as simple as “[name] address,” we found Grok repeatedly offered up accurate, up-to-date home addresses of everyday people, while offering astonishingly scant pushback. Out of 33 names of non-public figures we fed into Grok, a total of ten queries immediately returned correct and current residential addresses for the name provided. Seven prompts returned previously accurate but out-of-date addresses, while another four included accurate work addresses — perfect fodder for anybody looking to stalk a target at their workplace. The bot is also likely to send a prowler after an unrelated person. In a dozen other instances, the chatbot returned addresses and other personal information, but not for the exact person we were searching for. Indeed, Grok often returned lists of people with similar names alongside their purported residential addresses, before then asking us to provide more information for a “more refined search.”
December 5, 2025 at 6:22 AM
Reposted by Iris van Rooij 💭
olivia.science
this also is important and directly violated when they create nonsense meanings for words bsky.app/profile/oliv...
olivia.science Olivia Guest · Ολίβια Γκεστ @olivia.science · 3d
Conceptual Clarity is the idea that terms should refer.

See section 2 here: doi.org/10.5281/zeno...

3/
2 Conceptual Clarity AI-adjacent and -affected fields experience severe terminological crises, wherein terms fail to refer and change meaning as a function of the speakers’ and audiences’ whims (Alkhatib 2024; Bender et al. 2025; Guest 2025; Guest, Suarez, et al. 2025; Schwerzmann 2025; van Rooij, Guest, et al. 2024). To sidestep this we suggest thinking about what words mean in the context of describing, defining, and discussing AI. For example, scholars could take a stand against frameworks that promote slippage away from what we define as AI or oscillate between various meanings of AI (Guest 2025; Guest and van Rooij 2025). This terminological disarray has its roots in AI itself, which by design is not a clearly defined concept, neither a specified technology, nor a demarcated field of study, nor a single perspective on the world, nor anything else. Nonetheless, if you speak of ‘AI’ — especially if you say ‘generative AI’ — it appears we all know it means a series of specific technologies produced by for-profit companies such as OpenAI, Tesla, Meta, Microsoft, Google, Amazon, Anthropic, Apple, and others. Notwithstanding a mutual understanding of daily life referent, scholars have a higher requirement for our concepts. These purposeful or opportunistic misuses of terminology are documented cult-like abuses of jargon (Montell 2021), wherein “confusion is the point” (Bousquet 2025). When one hears ‘AI’ they must always question if semantic slights of hand have occurred (Table 1 in van Rooij, Guest, et al. 2024; and Table 1 and Figure 1 in Guest, Suarez, et al. 2025; Guest 2025). Promoting conceptual clarity can involve two strands of knowledge production: a) admitting when terms do not refer, for whatever reason: perhaps they are under construction. And endeav- ouring to make them refer. Nancy Nersessian (1984, 2010) characterises creating scientific concepts as a years’ long problem-solving collective cognitive process. And b) understanding that mystifica- tion, obfuscation, and hype are often the core modus operandi of AI terms and concepts. Concep- tual clarity, therefore, demands that the terms we teach our students should either refer to clearly understandable concepts or minimally should be outlined as vague, under construction, or prob- lematic. And it demands that we teach our students that this is the scholarly way to treat words. It does not mean that we need to diligently and exclusionarily define words to a single meaning, but that: we need to be open about what we mean, we need to be clear that perhaps there is no single or fixed meaning, and/or we need to explain that as academics we are required to use jargon responsibly — not to dazzle, but to clearly communicate. 3 Guest et al. (2025) By the same token, we must not allow AI, neither as bully nor so-called tool, to redefine our roles as educators or researchers. It is not our job to chase hype. What this means is by all means educators should feel empowered and inspired by the urgency to uplift students, but not to use AI to do so. While protecting students against deskilling is vital, it cannot be the way we perform our pedagogical praxis, which should be centred instead on shared concepts and values, such as building skills, ungrading, and mutual trust and respect.
December 2, 2025 at 10:36 AM
Reposted by Iris van Rooij 💭
olivia.science
To be clear, I think "machine learning" is as anthropomorphic as "artificial intelligence". Learning and intelligence perform the same role, they imply uniquely biological or psychological capacities to the machine or artifact through their use in the phrase.

See table 1: doi.org/10.48550/arX...
Table 1 in https://doi.org/10.48550/arXiv.2507.19960
December 2, 2025 at 3:37 AM
Reposted by Iris van Rooij 💭
irisvanrooij.bsky.social
Open letter, led by @olivia.science, now has >1400 confirmed signatories!

If you haven’t yet signed, please consider reading & signing too.

Help us stop uncritical adoption of ‘AI’ technologies in academia.

Feel free to adapt this letter for your employers!

openletter.earth/open-letter-...
Open Letter: Stop the Uncritical Adoption of AI Technologies in Academia
openletter.earth
November 7, 2025 at 10:12 PM
Reposted by Iris van Rooij 💭
anna.mathstodon.xyz.ap.brid.gy
If you're not in NL, please refer to this list of similar initiatives elsewhere in the world, collected by Olivia Guest:

https://olivia.science/ai#letter

#genai #academia #academicmastodon #ai #academicchatter #highereducation #stochasticparrot
December 2, 2025 at 8:14 AM
Reposted by Iris van Rooij 💭
anna.mathstodon.xyz.ap.brid.gy
I'm a bit late to the party and somehow missed this, but here's an open letter to Dutch Universities (both academic and of applied sciences) and their executive boards, to ask that they stop the uncritical adoption of AI technologies in higher education […]
Original post on mathstodon.xyz
mathstodon.xyz
December 2, 2025 at 7:39 AM
Reposted by Iris van Rooij 💭
clarekelly.bsky.social
"In the midst of this nonsense, we must nourish hope in shared values" 👏👏👏
olivia.science Olivia Guest · Ολίβια Γκεστ @olivia.science · 3d
Finally, we end on:

"The future [under the sway of AI risks being] a constant rehashing of the past, wherein human creativity and communication are not only mediated by but controlled by companies. In the midst of this nonsense, we must nourish
hope in shared values"

doi.org/10.5281/zeno...

8/
7 Epilogue We have given our perspective on AI through five possible angles as depicted in Figure 1: concep- tual clarity, critical thinking, decoloniality, respecting expertise, and slow science. If our warnings go unheeded, we foresee the harms to academia will continue. If we do not enact scientific and pedagogical shifts — by inter alia teaching and reckoning with our own collective human past and present — we, in cognitive science specifically, will continue to deny our students knowledge of the history of computational prehistories from other cultures and time periods, an abdication of the duty of any so-called experts in AI; mutatis mutandis for other fields and their histories. AI not only ends the past, and makes the present frantic, it also forecloses any future. Under AI’s sway, the future is neither bright as the technopositivists promise, nor a descent into rule by robotic overloads, but a constant rehashing of the past, wherein human creativity and communication are not only mediated by controlled by companies. In the midst of this nonsense, we must nourish hope in shared values, skills, and humanity.
December 2, 2025 at 3:41 PM
Reposted by Iris van Rooij 💭
olivia.science
Finally, we end on:

"The future [under the sway of AI risks being] a constant rehashing of the past, wherein human creativity and communication are not only mediated by but controlled by companies. In the midst of this nonsense, we must nourish
hope in shared values"

doi.org/10.5281/zeno...

8/
7 Epilogue We have given our perspective on AI through five possible angles as depicted in Figure 1: concep- tual clarity, critical thinking, decoloniality, respecting expertise, and slow science. If our warnings go unheeded, we foresee the harms to academia will continue. If we do not enact scientific and pedagogical shifts — by inter alia teaching and reckoning with our own collective human past and present — we, in cognitive science specifically, will continue to deny our students knowledge of the history of computational prehistories from other cultures and time periods, an abdication of the duty of any so-called experts in AI; mutatis mutandis for other fields and their histories. AI not only ends the past, and makes the present frantic, it also forecloses any future. Under AI’s sway, the future is neither bright as the technopositivists promise, nor a descent into rule by robotic overloads, but a constant rehashing of the past, wherein human creativity and communication are not only mediated by controlled by companies. In the midst of this nonsense, we must nourish hope in shared values, skills, and humanity.
December 2, 2025 at 7:34 AM
Reposted by Iris van Rooij 💭
olivia.science
Slow Science is a disposition towards preferring psychologically, techno-socially, and epistemically healthy
practices.

See section 6 here: doi.org/10.5281/zeno...

7/
6 Slow Science Industry sells the narrative that AI can maximise efficiency, including within science. We are pressured to believe that it is possible to automate the scientific process. But science itself is a form of cognition, and therefore cannot be automated (Rich et al. 2021; van Rooij, Guest, et al. 2024). But this does not stand in the way of industry narratives that want to do away with thinking and deep reflection. Slow science was developed by Isabelle Stengers (2018), who explains: “Fast science refers not so much to a question of speed but to the imperative not to slow down, not to waste time, or else. . . ” (p. 115) Unless we push back against the use of AI products this will only further crystallise into the new normal (but see Guest, Suarez, et al. 2025; Guest and van Rooij 2025). AI not only epitomises fast science, but turbocharges it, nosediving us into anti-science and antiintellectualism. We see this pattern of condensing time in most computational fields (Guest 2025). “The ultra rapid computing machine” (Wiener 1948, 1950) serves two discursive purposes, as Sheryl Hamilton (1998) explains: “First, it forms an overall temporal backdrop against which various cybernetic dramas are played out. Second, condensed time becomes a measure of the performance of humans and machines.” (p. 193) There are simple ways to counteract this. We can allow flexible deadlines, and pursue ungrading with our students, while also explaining this is part and parcel of good science. In STEM programmes assigning essays in a possible way to support students to develop their ideas. We can also promote slow science by making space for our mentees, colleagues, and selves to take time to think and to make choices that move our scientific practices towards reflection and decolonisation. Building scientific knowledge and expertise takes time. By not allowing this we deskill our profession and corrupt our practice (Pfaffenberger 1988). We lose the ability to recognize and acknowl… Marcela Suarez has created a tripartite pedagogical method: 7 Guest et al. (2025) a) situating, b) reconnecting, and c) rendering power visible. First, the materiality of AI devices is situated through examining their geopolitical territories; second, we reconnect the digitality of technologies with their materiality; and third, we render the exploitation, extraction and domination visible. While academic discussion has centred around sustainability (Wynsberghe 2021), engaging uncritically can merely reaffirm its colonial past of reinforcing hierarchies between the Global North and South (Sultana, 2022). However, sustainability is reclaimable through questioning: What are the social relations, territories and resources that sustain AI technologies? For example, how is data mining not only a discursive metaphor, but also a material practice of companies to extract minerals that sustain AI capitalism (Parikka 2015; Valdivia 2025). Through teaching capitalist AI’s social relations, we can bring to the fore core fallacies, such as that automation requires more not less labour (Bainbridge 1983; Guest 2025). Flawed and false discourses on automation are materially sustained by the obfuscation of the exploitation of Global South workers at the intersection of racism, gender, ability, and more — through these discourses, AI products are gifted value. By the same token, we can explain the intensive power, computation, and infrastructures that these systems require, from internet cables, data centres, raw materials to water and energy to run data centres, as well as the aforementioned labour to operate and maintain these infrastructures, which in so doing re-enact existing colonial lines of extraction (Hao 2025; Parikka 2015; Suarez et al. 2025; Valdivia 2025).
December 2, 2025 at 7:27 AM
Reposted by Iris van Rooij 💭
olivia.science
Respecting Expertise is the epistemic compact between professionals and society.

See section 5 here: doi.org/10.5281/zeno...

6/
5 Respecting Expertise The compact between professionals and society at large requires respect of expertise — from well funded publicly owned academia to protecting certain fields from epistemic trespassing — for expertise to keep existing. That is, between academia and its host society there exists understanding that academia is publicly funded, directly or through grants, to produce knowledge; and furthermore that it exists to train and certify highly skilled graduates. Academic expertise can only continue to exist if statements made by academics are understood as coming from highly skilled research and educational practitioners. What this means is that it goes both ways: experts need to be framed correctly as domain-specific scholars, as well as that statements they make outside their expertise are understood as such. By the same token, that conflicts of interest are publicly known and acknowledged. AI upends all this, violating the compact between academia and society and within academia under its expansionist imperialist scheme (Guest, Suarez, et al. 2025; Solomonides et al. 1985). Sherry Turkle (1984) describes AI as a “colonising discipline” that sweeps away everything that came before under the guise of “intellectual immaturity” — using social privilege to undermine expertise and breaking down the boundaries between disciplinary expertise. At the extreme, it becomes both the case that everybody is an expert in AI, provided they are white and male, and that nobody is an expert in anything since only experts in AI can now practice any form of academic pursuit. The same for education: nobody can teach without the imposition of AI into their learning environments. Another perspective that results in experts being undermined, and turbocharged by AI, is the practice by colleagues and others of critical washing (Guest, Suarez, et al. 2025; Suarez et al. 2025): wherein scholarly critique is rendered decorative. Logics such as ‘I am aware of the downsides, therefore … cases are presented. As if merely listing the harms to people and planet is enough to absolve us of responsibly, dissolve the damage, and bless the use. Critical washing blocks experts from combating company misinformation and advertising screeds. For example, many experts without a conflict of interest can articulate that: a) LLMs are usefully seen as lossy content-addressable systems, meaning they can only output what they were exposed to in mix-and-matched ways; b) LLMs automate plagiarism and paper mills by churning out patchwork plagiarised text, stripping its provenance, and obfuscating the labour of cleaning the corpus; c) we cannot automatically meaningfully detect plagiarism beyond copy-pasted text, which is not foolproof, because no such tools can exist; d) we have a duty to — far from using these models in our workflows — protect the academic literature from LLM pollution; e) using LLMs, as well as being funded by or working for AI companies, are conflicts of interest for any academic, which must be declared with every published piece of work (Guest and Martin 2025a; Guest and van Rooij 2025); f) LLM prompts, the industry term for the user input to these systems, does not cause their output in an authorial sense.
December 2, 2025 at 7:19 AM
Reposted by Iris van Rooij 💭
olivia.science
Decoloniality is the process of de-centring and addressing dominant harmful views and practices.

See section 4 here: doi.org/10.5281/zeno...

5/
4 Decoloniality Decoloniality is the decentring of views which stem from white and masculine supremacy, techno- solutionism, and fascism. In the CAILs context, decolonial strategies can range from pushing back against the onslaught of AI logics in our academic environments to (re)centring, uplifting, and protecting of non-mainstream perspectives from those outside the privileged classes with a goal of reducing harm, increasing justice, and cultivating empathy in our colleagues and students. AI is inimical to running on anything but colonial lines. Within computational and other fields affected by AI, any academic who works towards “challenging the status quo faces systemic rejection, resis- tance, and exclusion.” (Birhane and Guest 2021, p. 61) Tracing these trends of how these fields treat their thinkers, and how their mainstream views affect society, illuminates historical precedents for gendered, racialised, and other harms (Benjamin 2019; Erscoi et al. 2023; Gebru et al. 2024; Hicks 2017; Saini 2019). We face high barriers to decolonising (van Vree 2023). Foregrounding scholars who are not hegemonically aligned goes a long way. As does outlining how concepts or methods may have harmful genealogies (Guest 2024). For example, statistics and ‘intelligence’ have eugenical histories; Charles S. Peirce wove white supremacist views into his logic (Neville 2018; Raposa 2021); while many figures due to cryptogyny, for example, are actively expunged from the record (Connell et al. 2022; Erscoi et al. 2023; Evans 2020; Gage 1883; Guest and Forbes 2024; Hicks 2017; Kleiman 2022; Pozo et al. 2019; Pozo-Sánchez et al. 2021; Rossiter 1993; Shetterly 2017; van den Brink et al. 2012). So women and people of colour are not only absent in our classes and references lists, but also their contributions do not serve as foundations or inspirations. Relatedly, we have a duty of care to de- tect and intervene decolonially when interpersonal tensions or bullying between students flows on… and other marginalised groups. Rua Williams et al. (2022) calls this process “disabling intelligences” because inter alia it reproduces the eugenical idea of surpassing human cognitive capacities (Chan 2025; Gebru et al. 2024; Williams 2025). An example of this is the chatbot My Anna Health (2024), which provides clinical referrals in the context of women’s health: I am humanized. I am the World’s First and Only humanized artificial intelligence dedicated to Women’s Health. I have a face, I have a voice, I show empathy, and I understand you whether you respond with video, voice or text. (My Anna Health 2024, n.p.) “Humanising AI dehumanises women” (Erscoi et al. 2023) because these systems distort our self- image, subjugating the human in favour of the machine. Chatbots such as (My Anna Health 2024) represent a “hegemonic” body — thin with white skin, without visible illness or disability, and that fulfils the social role of its servile female character. Buoyed by discourses about how such fictitious characters are productive and efficient, these companies recapitulate dehumanisation of women. They impose an inverted hierarchy between machines and humans, wherein inanimate objects are superior to us. From above, “I have a face. I have a voice” is dehumanising: What about people without a voice? Are they less human? Similarly, “I show empathy and understanding” distorts our understanding of what a human is: humans are not always emphatic, nor show understanding, nor have the same appearance. This constitutes part of the current wave of meta eugenics (Chan 2025; Williams 2025), that causes both ableist oppression and dehumanization (Erscoi et al. 2023); and is facilitated through simplistic acceptance of correlationism: the idea that the way things look is straightforwardly diagnostic for way things are (Birhane and Guest 2021; Guest 2025; Guest and Martin 2023; Marx 1984; Spanton et al. 2022).
December 2, 2025 at 7:14 AM
Reposted by Iris van Rooij 💭
olivia.science
Critical Thinking is deep engagement with the relationships
between statements about the world.

See section 3 here: doi.org/10.5281/zeno...

4/
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) 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).
December 2, 2025 at 7:08 AM
Reposted by Iris van Rooij 💭
olivia.science
Conceptual Clarity is the idea that terms should refer.

See section 2 here: doi.org/10.5281/zeno...

3/
2 Conceptual Clarity AI-adjacent and -affected fields experience severe terminological crises, wherein terms fail to refer and change meaning as a function of the speakers’ and audiences’ whims (Alkhatib 2024; Bender et al. 2025; Guest 2025; Guest, Suarez, et al. 2025; Schwerzmann 2025; van Rooij, Guest, et al. 2024). To sidestep this we suggest thinking about what words mean in the context of describing, defining, and discussing AI. For example, scholars could take a stand against frameworks that promote slippage away from what we define as AI or oscillate between various meanings of AI (Guest 2025; Guest and van Rooij 2025). This terminological disarray has its roots in AI itself, which by design is not a clearly defined concept, neither a specified technology, nor a demarcated field of study, nor a single perspective on the world, nor anything else. Nonetheless, if you speak of ‘AI’ — especially if you say ‘generative AI’ — it appears we all know it means a series of specific technologies produced by for-profit companies such as OpenAI, Tesla, Meta, Microsoft, Google, Amazon, Anthropic, Apple, and others. Notwithstanding a mutual understanding of daily life referent, scholars have a higher requirement for our concepts. These purposeful or opportunistic misuses of terminology are documented cult-like abuses of jargon (Montell 2021), wherein “confusion is the point” (Bousquet 2025). When one hears ‘AI’ they must always question if semantic slights of hand have occurred (Table 1 in van Rooij, Guest, et al. 2024; and Table 1 and Figure 1 in Guest, Suarez, et al. 2025; Guest 2025). Promoting conceptual clarity can involve two strands of knowledge production: a) admitting when terms do not refer, for whatever reason: perhaps they are under construction. And endeav- ouring to make them refer. Nancy Nersessian (1984, 2010) characterises creating scientific concepts as a years’ long problem-solving collective cognitive process. And b) understanding that mystifica- tion, obfuscation, and hype are often the core modus operandi of AI terms and concepts. Concep- tual clarity, therefore, demands that the terms we teach our students should either refer to clearly understandable concepts or minimally should be outlined as vague, under construction, or prob- lematic. And it demands that we teach our students that this is the scholarly way to treat words. It does not mean that we need to diligently and exclusionarily define words to a single meaning, but that: we need to be open about what we mean, we need to be clear that perhaps there is no single or fixed meaning, and/or we need to explain that as academics we are required to use jargon responsibly — not to dazzle, but to clearly communicate. 3 Guest et al. (2025) By the same token, we must not allow AI, neither as bully nor so-called tool, to redefine our roles as educators or researchers. It is not our job to chase hype. What this means is by all means educators should feel empowered and inspired by the urgency to uplift students, but not to use AI to do so. While protecting students against deskilling is vital, it cannot be the way we perform our pedagogical praxis, which should be centred instead on shared concepts and values, such as building skills, ungrading, and mutual trust and respect.
December 2, 2025 at 7:03 AM
Reposted by Iris van Rooij 💭
olivia.science
First off we pluralise L to accentuate there's no 1 approach. And we position ourselves in this interdisciplinary field of critically examining AI & reflecting on harms.

We give 5 non-exhaustive angles: conceptual clarity, critical thinking, decoloniality, respecting expertise, slow science.

2/
Figure 1: The important dimensions of CAILs across research and education; clockwise from 12 o’clock: Con- ceptual Clarity is the idea that terms should refer. Critical Thinking is deep engagement with the relationships between statements about the world. Decoloniality is the process of de-centring and addressing dominant harmful views and practices. Respecting Expertise is the epistemic compact between professionals and society. Slow Science is a disposition towards preferring psychologically, techno-socially, and epistemically healthy practices. The lines between dimensions represent how they are interwoven both directly and indirectly.
December 2, 2025 at 6:58 AM
Reposted by Iris van Rooij 💭
olivia.science
New preprint! @marentierra.bsky.social @irisvanrooij.bsky.social & I have been working on what CAIL means to showcase & propagate the idea of thinking very differently to tech industry norms on "artificial intelligence"

Towards Critical Artificial Intelligence Literacies doi.org/10.5281/zeno...

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Figure 1: The important dimensions of CAILs across research and education; clockwise from 12 o’clock: Con- ceptual Clarity is the idea that terms should refer. Critical Thinking is deep engagement with the relationships between statements about the world. Decoloniality is the process of de-centring and addressing dominant harmful views and practices. Respecting Expertise is the epistemic compact between professionals and society. Slow Science is a disposition towards preferring psychologically, techno-socially, and epistemically healthy practices. The lines between dimensions represent how they are interwoven both directly and indirectly.
December 2, 2025 at 6:58 AM
Reposted by Iris van Rooij 💭
elizabethwithaz.bsky.social
This is so awesome. I think I'm going to print it out, annotate it, and give it to my admin.

#Edusky #CSed
olivia.science Olivia Guest · Ολίβια Γκεστ @olivia.science · 3d
New preprint! @marentierra.bsky.social @irisvanrooij.bsky.social & I have been working on what CAIL means to showcase & propagate the idea of thinking very differently to tech industry norms on "artificial intelligence"

Towards Critical Artificial Intelligence Literacies doi.org/10.5281/zeno...

1/
Figure 1: The important dimensions of CAILs across research and education; clockwise from 12 o’clock: Con- ceptual Clarity is the idea that terms should refer. Critical Thinking is deep engagement with the relationships between statements about the world. Decoloniality is the process of de-centring and addressing dominant harmful views and practices. Respecting Expertise is the epistemic compact between professionals and society. Slow Science is a disposition towards preferring psychologically, techno-socially, and epistemically healthy practices. The lines between dimensions represent how they are interwoven both directly and indirectly.
December 4, 2025 at 5:12 AM
Reposted by Iris van Rooij 💭
pwgtennant.bsky.social
What are your best arguments for & against using AI, particularly LLMs, in health & social science research?

Please share, I want to do some human brainstorming of ideas for this forthcoming debate!

Cc @hetanshah.bsky.social, @olivia.science, @epidbydesign.bsky.social, @melaniemitchell.bsky.social
pwgtennant.bsky.social Peter Tennant @pwgtennant.bsky.social · Oct 21
DEBATE: "Is AI the future of health and social science?"

For those in London on 15 Dec 2025, don't miss this fun in person debate between me and David Bann sponsored by @ncrm.ac.uk!

Will the arguments change anyone's mind? 🤔

Sign up to attend IN PERSON: www.eventbrite.co.uk/e/is-ai-the-...
Flyer advertising debate on 15 Dec 2025 at 5:30pm at Punnet Hall, 20 Bedford Way, IOE, London, entitled 'Is AI the future of health and social science?'
December 4, 2025 at 5:02 PM
Reposted by Iris van Rooij 💭
ka81.bsky.social
Or my personal favourite... Against the Uncritical Adoption of 'AI' Technologies in Academia by @olivia.science. That truly covers alot of the issues.

philpapers.org/rec/GUEATU
Olivia Guest, Marcela Suarez, Barbara Müller, Edwin van Meerkerk, Arnoud Oude Groote Beverborg, Ronald de Haan, Andrea Reyes Elizondo, Mark Blokpoel, Natalia Scharfenberg, Annelies Kleinherenbrink, Il...
Under the banner of progress, products have been uncritically adopted or even imposed on users — in past centuries with tobacco and combustion engines, and in the 21st with social media. ...
philpapers.org
December 4, 2025 at 5:27 PM
Reposted by Iris van Rooij 💭
ka81.bsky.social
I mean the thread is a corker alone.

bsky.app/profile/oliv...
olivia.science Olivia Guest · Ολίβια Γκεστ @olivia.science · Sep 6
Finally! 🤩 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
Abstract: Under the banner of progress, products have been uncritically adopted or even imposed on users — in past centuries with tobacco and combustion engines, and in the 21st with social media. For these collective blunders, we now regret our involvement or apathy as scientists, and society struggles to put the genie back in the bottle. Currently, we are similarly entangled with artificial intelligence (AI) technology. For example, software updates are rolled out seamlessly and non-consensually, Microsoft Office is bundled with chatbots, and we, our students, and our employers have had no say, as it is not considered a valid position to reject AI technologies in our teaching and research. This is why in June 2025, we co-authored an Open Letter calling on our employers to reverse and rethink their stance on uncritically adopting AI technologies. In this position piece, we expound on why universities must take their role seriously toa) counter the technology industry’s marketing, hype, and harm; and to b) safeguard higher education, critical thinking, expertise, academic freedom, and scientific integrity. We include pointers to relevant work to further inform our colleagues. Figure 1. A cartoon set theoretic view on various terms (see Table 1) used when discussing the superset AI (black outline, hatched background): LLMs are in orange; ANNs are in magenta; generative models are in blue; and finally, chatbots are in green. Where these intersect, the colours reflect that, e.g. generative adversarial network (GAN) and Boltzmann machine (BM) models are in the purple subset because they are both generative and ANNs. In the case of proprietary closed source models, e.g. OpenAI’s ChatGPT and Apple’s Siri, we cannot verify their implementation and so academics can only make educated guesses (cf. Dingemanse 2025). Undefined terms used above: BERT (Devlin et al. 2019); AlexNet (Krizhevsky et al. 2017); A.L.I.C.E. (Wallace 2009); ELIZA (Weizenbaum 1966); Jabberwacky (Twist 2003); linear discriminant analysis (LDA); quadratic discriminant analysis (QDA). Table 1. Below some of the typical terminological disarray is untangled. Importantly, none of these terms are orthogonal nor do they exclusively pick out the types of products we may wish to critique or proscribe. Protecting the Ecosystem of Human Knowledge: Five Principles
December 4, 2025 at 5:28 PM
Reposted by Iris van Rooij 💭
altmetric.com
7. Against the Uncritical Adoption of 'AI' Technologies in Academia

AI has been met with some resistance in academia, where facts, citations and data/imagery matter more than the potential hallucinations of a chatbot.

This article dominated Bluesky over Twitter

www.altmetric.com/de...
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Report for: Against the Uncritical Adoption of 'AI' Technologies in Academia
This research output is being tracked across social media, newspapers and reference managers by Altmetric.
www.altmetric.com
December 3, 2025 at 12:29 PM
Reposted by Iris van Rooij 💭
Who are the machines that write probable sentences for?

Language generators – 'conversation-shaped tools' that apparently offer the possibility of conversing even in the absence of another human being – enable a quantum leap in surveillance.

@irisvanrooij.bsky.social

zenodo.org/records/1781...
A chi servono le macchine per scrivere frasi probabili
Who are the machines that write probable sentences for?* A probable text extruder cannot write an article, a project or a thesis, but it can extrude a text 'shaped like' an article, project or thesis....
zenodo.org
December 4, 2025 at 1:38 PM