Olivia Guest · Ολίβια Γκεστ
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olivia.science
Olivia Guest · Ολίβια Γκεστ
@olivia.science
17K followers 1.9K following 5.6K posts
https://olivia.science

assistant professor of computational cognitive science · she/they · cypriot/kıbrıslı/κυπραία · σὺν Ἀθηνᾷ καὶ χεῖρα κίνει
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olivia.science
and some seem to be curious about why I think this; I guess I looked for myself too. I recommend it actually if you want to understand this type of misogyny
Zoomer Antimillenarian ‪@surcomplicated.bsky.social‬ Follow Any actually existing universe can only instantiate some infinitesimal minute fraction of all consistent mathematical patterns. van Rooij thinks that this requires ‘fine-tuning’ when in fact any random selection will do. What she’s doing is on par w/ crank failed string theorists & youth pastors. ‪atticus goldfinch‬ ‪@atticusgf.bsky.social‬ · 2d Hell, she even leans into the "fine-tuning" argument. Claims evolution faces insurmountable complexity barriers, & doesn't get to escape them. That evolution didn't result in cognition. What did? Well, the universe is "friendly" and gives solvable tasks. This is "fine-tuning"! Intelligent design!
December 25, 2025 at 8:38 AM
olivia.science
Yeah, this is a huge obsession with these people, also see footnote 2 from:

Guest, O. & Martin, A. E. (2025). A Metatheory of Classical and Modern Connectionism. Psychological Review. doi.org/10.1037/rev0...
PDF: repository.ubn.ru.nl/bitstream/ha...
This characterization of human-likeness is the case only in complex or dubious ways (e.g., Leivada, Günther, & Dentella, 2024). Also, the concept of deeper models being more human-like is consistent with the classical dualist perspective: “The difference between machines and natural objects is simply one of degree for Descartes[. M]achines are works of nature differing from other natural objects only by degree of complexity” (Hattab, 2009, p. 85). 3 As in other m
December 16, 2025 at 9:18 PM
olivia.science
OK, bonus, some art: an Olmec head destroying a Tesla by @chavismarmol.bsky.social — chavismarmol.com you have GOT to visit that website; it is amazing! 🤩

Read more: hyperallergic.com/878913/artis...

3/3
Last Monday, March 11, artist Chavis Mármol dropped a nine-ton Olmec head made of quarry stone onto a blue Tesla Model 3. (all photos by and courtesy the artist) more: https://hyperallergic.com/878913/artist-chavis-marmol-crushes-tesla-with-colossal-olmec-head-sculpture/
December 14, 2025 at 8:06 PM
olivia.science
Another cool one is the Mayan calendrical system: "highly precise and directly translatable to a system of gears" doi.org/10.5281/zeno... — much like gears in clockwork & the Antikythera Mechanism of course.

See more: maya.nmai.si.edu/sites/defaul...
www.mayaarchaeologist.co.uk/public-resou...

2/n
The Maya Calendar System Using their knowledge of astronomy and mathematics, the Maya developed one of the most accurate cal- endar systems in human history. The Maya calendar system has its roots in older, Mesoamerican1 indig- enous civilizations, particularly the Olmec. The Maya calendar is complex and serves both practical and ceremoni- al purposes. The Maya calendar system includes several calendars that mea- sure time periods of varying lengths. These calendars are based on solar, lunar, planetary, and human cycles. There are three most commonly known cyclical calendars used by the Maya. These include the Haab which is a 365- day solar calendar, the Tzolk’in which is a 260-day sacred calendar, and the Calendar Round of 52 years. In addition, the Maya developed the Long Count calendar to date mythical and historical events chronologically. 1 Definitions for words in red-colored font can be found in the Glossary page in the Resources section of the “Living Maya Time” website. A contemporary representation of the Tzolk’in (inner green circle) and Haab (outer brown circle) calendars. The Haab The Maya solar calendar, called Haab, is a count of 365 days and thus approximates the solar year. The word “haab” means “year” in the Yucatec Mayan language. The Haab is composed of 18 months made of 20 days each, plus one month made of 5 days. A month made of 20 days is called a uinal. Each uinal has its own name. These 18 months together equal 360 days. The last month made of 5 days is called Wayeb. The 19 months together total 365 days. 18 x 20 + 5 = 3652 Table 1: Haab Months and Days Pop Wo’ Sip Sotz’ Sek Xul Yaxk’in Mol Ch’en Yax Sak’ Keh Mak K’ank’in Muwan Pax K’ayab Kumk’u Wayeb 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7… the calendar also shown as gears! from https://www.mayaarchaeologist.co.uk/public-resources/maya-world/maya-calendar-system/
December 14, 2025 at 7:53 PM
olivia.science
Some examples of decolonial automata/computations, apropos of giving a mini talk on this tomorrow:

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

🐯 Tipu's tiger is amazing; sadly stolen & now in the V&A, created in India & depicting a tiger mauling an English man.

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On the flip side, we can include positive examples in our teaching on the history of AI: Tipu’s tiger from late 18th century India, featuring the eponymous robotic cat mauling a European man (Karp et al. 2013); the Antikythera Mechanism from ancient Greece, which was a clockwork astro- logical calender; and the Mayans’ calendrical system that was highly precise and directly translat- able to a system of gears. We can also explain the origin of ‘algorithm’ and ‘algebra’, conceptually developed during the Islamic Golden Age. These serve not only to properly contextualise the his- tory of AI (Mayor 2018), but to reimagine with students what could be done differently. Regardless of AI, science has always been intertwined with domination, extraction, binary thinking, and the imposition of hierarchies (Tacheva et al. 2023). For example, the Cartesian dualist idea that the mind can be separated from the body is at the core of AI hype. Corporate discourses around AI feature preposterous claims, such that ‘ChatGPT has PhD level cognition capacities’, and they will be able to surpass human intelligence in the near future (Welt 2025), implying that technologies are human-like. However, far from LLMs having cognitive capacities, companies ob- fuscate the labour that is extracted from data workers (Miceli et al. 2020). In the classroom, we should question the mind and body binary and render visible its eugenical and colonial legacies. While we refute corporate claims, we also take them seriously as dehumanising workers, women, https://en.wikipedia.org/wiki/Tipu%27s_Tiger Tipu's Tiger, Tippu's Tiger or Tipoo’s Tiger is an 18th-century automaton created for Tipu Sultan, the ruler of the Kingdom of Mysore (present day Karnataka) in India. The carved and painted wood casing represents a tiger mauling a near life-size European man. Mechanisms inside the tiger and the man's body make one hand of the man move, emit a wailing sound from his mouth and grunts from the tiger. In addition a flap on the side of the tiger folds down to reveal the keyboard of a small pipe organ with 18 notes.[1] zoomed in to the tiger biting the man's neck Tipu's Tiger with the organ keyboard visible
December 14, 2025 at 7:45 PM
olivia.science
If you're based in NL please consider reading & signing our open letter on reclaiming so-called AI from the AI Delta Plan & co:

"Thoughtfully Shaping Our Digital Future" openletter.earth/zorgvuldig-a...

Big thanks to @lekkerresistance.bsky.social @irisvanrooij.bsky.social @marentierra.bsky.social
Thoughtfully Shaping Our Digital Future To the parties forming the government in the Senate and House of Representatives, as well as the outgoing administration, We are writing to you in recognition of your crucial responsibility for shaping current and future AI policy, overseeing digitization, and upholding public values. We are a coalition of scientists, experts, and representatives of civil society organizations. We believe it is essential to address these matters together. This letter has two objectives: a) Provide context for current plans, including the AI Delta Plan, the AIC4NL position paper, and the Invest-NL AI Deep Dive. These investment proposals often rely on assumptions that lack scientific evidence and do not fully reflect public values. b) To offer a constructive, well-substantiated alternative approach to digital futures based on people, nature, and democracy. We believe a collaborative process should guide decisions on the needs, scope, and nature of investments by bringing together scientists, civil society organizations, and stakeholders.
December 13, 2025 at 11:12 AM
olivia.science
I do think some stuff is specifically of a (sub)type, but I 100% agree not disjoint at all

e.g. arxiv.org/abs/2507.19960 bsky.app/profile/oliv...
There it is a definite social relation between [people], that assumes, in their eyes, 454 the fantastic form of a relation between things. In order, therefore, to find an 455 analogy, we must have recourse to the mist-enveloped regions of the religious 456 world. In that world the productions of the human brain appear as independent 457 beings endowed with life, and entering into relation both with one another 458 and the human race. So it is in the world of commodities with the products of 459 [people]’s hands. (Marx, 1867) 460 The reasoning problems become evermore severe in our misunderstandings of these most 461 modern machines (Guest & Martin, 2023). As Taina Bucher (2018, p. 50) explains: “When a 462 machine runs smoothly, nobody pays much attention, and the actors and work required to 463 make it run smoothly disappear from view (Latour, 1999).” A next step in this devolution 464 and devaluation of cognitive labour is that now the user too, deskilled and displaced, also 465 disappears from view. What voice does the human, now reduced to only a user, have if 466 their verbal (column 1, Table 4) and visual (column 2) expressions are just the copy-pasted 467 output of a device that performs patchwork plagiarism? What human connection do they 468 have when their friends and romantic partners have been replaced with inanimate objects 469 (column 3)? 470
December 12, 2025 at 2:50 PM
olivia.science
Took its sweet time, but absolutely worth it for my Xmas reading! 🤩

@francesegan.bsky.social

doi.org/10.7551/mitp...
Photo of the book deflating mental representation by Frances Egan
December 10, 2025 at 4:35 PM
olivia.science
Thanks to you both for being so positive... really means a lot to be honest. I also got a weirdly positive message from His Brittanic Majesty's Revenue and Customs 🙂‍↕️

Weirdest reply! "No,no, we owe you 670 odd quid!" 🤣

Yes, really wanna move back.
Letter from HMRC claiming I owe them 674.40 spondulix like ok m8 whatever
December 8, 2025 at 8:34 PM
olivia.science
The point isn't assessment standards...

doi.org/10.31234/osf...
we must be critical when we see statements such as: “It’s worth noting that AI can pass the clinical social worker exam — even without seeing the questions. But that shows the weaknesses of exams more than the strength of AI” (Caldwell, 2024, n.p.). An AI product passing an exam means nothing about the “weak- nesses” of exams in the abstract. Anybody can ‘win’ a marathon if driven by car. Does that make it a weakness of the marathon race as an endurance event? Or does it reflect a deeper category error on our behalf, like with the misapplication of statistical tests, that an assumption has been violated? The race as the exam assumes an unaided human performs it. Any digital computer can sur- pass a human at many feats, e.g. calculations per second, but that grants no humanity to the machine. 8 Do not Embrac
December 6, 2025 at 7:34 AM
olivia.science
It's a difficult needle to thread because a bully (AI industry) has no moral compass nor will stop bullying if we acquiesce. But that doesn't also mean we don't fight back. We try to make this clear from the abstract here too: "AI cannot be allowed to drive change."

doi.org/10.5281/zeno...

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Abstract from https://doi.org/10.5281/zenodo.17786243
December 6, 2025 at 7:23 AM
olivia.science
Yup, as @irisvanrooij.bsky.social @marentierra.bsky.social and I say in Towards Critical Artificial Intelligence Literacies doi.org/10.5281/zeno...

"[Guardrails] pose an infinite regress of humans in the loop needed to make pretend of what AI was supposed to solve"
extract from pages 4 and 5 Guest, O., Suarez, M., & van Rooij, I. (2025). Towards Critical Artificial Intelligence Literacies. Zenodo. https://doi.org/10.5281/zenodo.17786243
December 5, 2025 at 1:05 PM
olivia.science
so it begins
scholar now has AI
December 3, 2025 at 11:14 AM
olivia.science
This could be useful, esp the figure. The gist is you struggle because some of these words are not scholarly, but marketing nonsense, designed to confuse and mystify. bsky.app/profile/oliv...

See especially this part from here: doi.org/10.5281/zeno...
extract from section 2
December 2, 2025 at 9:20 AM
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...

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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
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
olivia.science
Respecting Expertise is the epistemic compact between professionals and society.

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

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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
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...

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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
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
olivia.science
Conceptual Clarity is the idea that terms should refer.

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

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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
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.

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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
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
olivia.science
I don't think that's what is happening. I think they are driving themselves to the end of a marathon and claiming they ran it.

doi.org/10.31234/osf...
we must be critical when we see statements such as: “It’s worth noting that AI can pass the clinical social worker exam — even without seeing the questions. But that shows the weaknesses of exams more than the strength of AI” (Caldwell, 2024, n.p.). An AI product passing an exam means nothing about the “weak- nesses” of exams in the abstract. Anybody can ‘win’ a marathon if driven by car. Does that make it a weakness of the marathon race as an endurance event? Or does it reflect a deeper category error on our behalf, like with the misapplication of statistical tests, that an assumption has been violated? The race as the exam assumes an unaided human performs it. Any digital computer can sur- pass a human at many feats, e.g. calculations per second, but that grants no humanity to the machine. 8 Do not Embrace AI
December 2, 2025 at 5:05 AM
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
olivia.science
this will keep happening because:

guardrails and any other control over these models is a scam
https://www.wired.com/story/poems-can-trick-ai-into-helping-you-make-a-nuclear-weapon/
November 30, 2025 at 2:44 PM