Towards Critical Artificial Intelligence Literacies

Towards Critical Artificial Intelligence Literacies Towards Critical Artificial Intelligence Literacies Olivia Guest1,2, Marcela Suarez1, and Iris van Rooij1,2,3 1Department of Cognitive Science and Artificial Intelligence, Radboud University, The Netherlands 2Donders Institute for Brain, Cognition, and Behaviour, Radboud University, The Netherlands 3Department of Linguistics, Cognitive Science, and Semiotics, Aarhus University, Denmark Critical Artificial Intelligence Literacies (CAILs) is the collection of ways of thinking about and relating to so-called artificial intelligence (AI) that rejects dominant frames presented by the technology industry, by naive computationalism, and by dehumanising ideologies. Instead, CAILs centre human cognition and uphold the integrity of academic research and education. We present a selection of CAILs across research and education, which we analyse into the following non-orthogonal dimensions: conceptual clarity, critical thinking, decoloniality, respecting expertise, and slow science. Finally, we note how we see the present with and without a wider adoption of CAILs — a fundamental aspect is the assertion that AI cannot be allowed to drive change, even positive change, in education or research. Instead cultivation of and adherence to shared values and goals must guide us. Ultimately, CAILs minimally ask us to contemplate how we as academics can stop AI companies from wielding so much power. Keywords: critical computational thinking; artificial intelligence; cognitive science; slow science; decoloniality; higher education Critical Artificial Intelligence literacies (CAILs) is an interdisciplinary field intended to foster critical perspectives on the concept of artificial intelligence (AI) and its related claims. ‘Critical’ transcends mere AI literacy and focusses instead on inter alia critical thinking and resistance. The plural ‘literacies’ acknowledges the different theoretical and practical perspectives on what ‘critical’ means, fuelled in part by existing critical digital literacies and pedagogies (Pangrazio 2016). Importantly, scholars who can protect themselves from the AI industrial complex’s harm and violation of their spaces need not engage with CAILs further. Others of us are forced into “resisting, refusing, reclaiming, reimagining” (Duarte et al. 2025) AI and uncritical AI literacy (e.g. van Rooij, Guest, et al. 2024). Uncritical such literacies that have been proposed as pedagogical “strategy [have] failed regularly, and sometimes catastrophically” (Reich 2025, n.p.). To deal with the above, CAILs scholars debunk AI hype—the exaggerated or outright false statements about AI (Altmeyer et al. 2024; Bender et al. 2025; Gupta et al. 2024)—and render visible the social harms in reproducing racism, sexism and stereotyping (Tacheva et al. 2023). Contributions have also centred on the damage to different sectors, such as education (Avraamidou 2024), the environment (Valdivia 2025) , and labour (Miceli et al. 2020). A common core are the “healing operations that would reappropriate what we have been separated from, recovering or reinventing what that separation has destroyed” (Stengers 2018, p. 121). We approach CAILs from the cognitive and social sciences, rejecting pseudoscientific claims about AI technologies and ideologies and emphasizing such technologies need to be socioculturally embedded to be correctly understood as harmful (Bender et al. 2025; Benjamin 2019; Birhane and Guest 2021; Forbes et al. 2025; Spanton et al. 2022). Our expertise is a function of our research in 1 Guest et al. (2025) Figure 1: The important dimensions of CAILs across research and education; clockwise from 12 o’clock: Conceptual 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. Conceptual Clarity Critical Thinking Decoloniality Slow Science Respecting Expertise and teaching of critical (meta)theory of cognition (Guest, Suarez, et al. 2025; van Rooij and Guest 2025), as well as our critical reflections on knowledge production as a practice inexorably embedded in power structures (Guest 2024; Guest and Forbes 2024; Guest and Martin 2025a). For us, CAILs must include an epistemological positioning to reject corporate marketing agendas. From this epistemological endeavour, we refuse machine anthropomorphisation that distorts cognition, humanity, and social relationships (Erscoi et al. 2023; Guest, Suarez, et al. 2025; Guest and van Rooij 2025; van der Gun et al. 2024). We argue that AI technologies continue broader pseudoscientific colonial projects of domination, of hierarchies, of extractivism of cognitive labour, and of binary thinking (Gebru et al. 2024; Hao 2025; Miceli et al. 2020; Ricaurte 2022). The critical questions we pose in our research and teaching comprise: What is cognition? What is the intelligence, if any, in AI? What is the relationship of AI to the reproduction of racist, sexist, and ableist oppression? What is the historical legacy of eugenics, and how is that related to cognitive science and AI? To what extent do AI technologies and ideologies mean the continuation of the relationship of colonial projects of domination, exploitation, and creation of hierarchies of social differentiation? We propose in research and educational settings, CAILs can take shape along different dimensions (see Figure 1), which serve as basic values for us, when we perform our academic duties, and to share with our students. The dimensions are neither orthogonal nor exclusive, and are not the end of any fight against oppressive technologies and fascistic ideologies — what we list is a selection of angles to think about whenever AI is imposing the industry’s values, enact harm, and displace humans. Pulling on each of these threads may lead one to similar conclusions or to another dimension. This is by design and desirable. In the following sections we go over each in order, clockwise from 12 o’clock in Figure 1, and provide explanations and illustrative examples. 2 Towards Critical Artificial Intelligence Literacies 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;Guest2025;Guest,Suarez,etal.2025;Schwerzmann2025;vanRooij, 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). To avoid being mired in the inevitable confusions of adopting the marketing term AI as-is, we use ‘AI’ to denote a system that displaces human cognitive labour (Guest 2025, Table 1). This is to say, any technology that appears to perform cognition and causes active harm to humans and their skills as well as society and the planet at large (viz. Guest, Suarez, et al. 2025; Guest and van Rooij 2025). LLM-powered chatbots fit the bill because: their data was collected unethically, or minimally their ethicality and legality cannot be verified both in terms of data privacy and ownership and in terms of unethical labour or consent; their behaviour is unpredictable in terms of accuracy and safety, provably no guardrails can ever exist that willensure their output is not open to harming users; their use case directly deskills the user who is not responsible for the plagiarised output in any meaningful sense; their need for water, electricity, land, and rare earth minerals for a dysfunctional and even undefined application is too high and harms too many (Benjamin 2019; Birhane and McGann 2024; Birhane and Prabhu 2021; Birhane, Prabhu, et al. 2023; Crawford 2021; Liesenfeld et al. 2023; Perrigo 2023; Suarez et al. 2025; van Rooij 2022). This kind of system “is a thieving, hallucinating, biased, data-scraping, eco-destroying, surveillance bot that will never be able to reconcile the receipts of its unethical origins” (Dusseau 2025, p. 16). The reader has the freedom to decide if a technology or ideology is ‘AI’ and so if they also need to deploy CAILs to deconstruct or combat it. 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 endeavouring 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 mystification, obfuscation, and hype are often the core modus operandi of AI terms and concepts. Conceptual 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 problematic. 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. 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 cognitive 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 inrelationtootherideas(Elgin 1999,2005,2017). So when students work on assignedreadings,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) Appropriate use of formalism can facilitate analysis of misleading claims. For instance, the AI industry claims their technologies can replace experts’ cognition, even going as far as proposing that professionals in fields such a law, science, education, medicine, and art can be replaced by AI. While such proposals are revealed as misconceived and harmful by other dimensions in Figure 1, formal critique can provide a complementary way of uprooting such claims. With critical formal skills students can document inconsistencies in the reasoning that pervades hype through revealing the fundamental limits of computation, for example mathematically proving the impossibility of replicating human cognitive capacities (Blokpoel et al. 2021-2025; Rich et al. 2021; van Rooij, Blokpoel, et al. 2019; van Rooij, Guest, et al. 2024). Formal results like this ironically show that solid computational thinking — taking computationalism seriously (Guest and Martin 2025b; Guest, Scharfenberg, et al. 2025) — undercuts AI misinformation. 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 reveal 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 unveil 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). 4 Decoloniality Decoloniality is the decentring of views which stem from white and masculine supremacy, technosolutionism, 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, resistance, 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 detect and intervene decolonially when interpersonal tensions or bullying between students flows on oppressive lines (Guest and Forbes 2024). 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 astrological calender; and the Mayans’ calendrical system that was highly precise and directly translatable 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 history 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 obfuscate 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, 5 Guest et al. (2025) 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 selfimage, 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). 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 I can now use AI’ are at play in almost all situations where these products are pushed on users. And this occurs in pro-AI media articles, as well as in research and the classroom when use 6 Towards Critical Artificial Intelligence Literacies 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. 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 acknowledge the scholars and colleagues who have contributed to the forming of our ideas, and we become disconnected from the historical record. By mindlessly proceeding, at best we fail to counteract existing issues, and at worst we actively distort that record. One prominent outgrowth of how AI amplifies this self-destructive and -disrespecting culture is the proliferation of automated plagiarism and AI slop that destroy the ecosystem of scientific knowledge and its integrity. In reclaiming our science and our dignity, we need to reject the idea that fast is good or even neutral (Stengers 2018). When we show our students how slow science is not only possible, but necessary for a truly sustainable and liveable world, we empower critical scholars to resist fast science, and reject the concomitant AI infiltration. On this note, a related binary that we can question in the classroom is that of digitality, the virtual, versus materiality, the thing itself. 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). 7 Epilogue We have given our perspective on AI through five possible angles as depicted in Figure 1: conceptual 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. AInot only ends the past,and makesthe presentfrantic,it also foreclosesany future.UnderAI’s sway, the future is neither bright as the technopositivists promise, nor a descent into ruleby 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. References Alkhatib, Ali (2024). Defining AI.url:https://ali-alkhatib.com/blog/defining-ai. Altmeyer, Patrick et al. (2024). Position: Stop Making Unscientific AGI Performance Claims. arXiv: 240 2.03962 [cs.AI].url:https://arxiv.org/abs/2402.03962. Avraamidou, Lucy (2024). “Can we disrupt the momentum of the AI colonization of science education?” In: Journal of Research in Science Teaching 61.10, pp. 2570–2574. Bainbridge, Lisanne (1983). “Ironies of automation”. In: Automatica 19.6, pp. 775–779. Beisbart, Claus et al. (2021). “Making Reflective Equlibrium Precise: A Formal Model”. In: Ergo an Open Access Journal of Philosophy 8. Bender, Emily M and Alex Hanna (2025). The AI Con: How to fight big tech’s hype and create the future we want. Random House. Benjamin, Ruha (2019). Race after technology. Polity. 8 Towards Critical Artificial Intelligence Literacies Birhane, Abeba and Olivia Guest (2021). “Towards Decolonising Computational Sciences”. In: Kvinder, Køn & Forskning 29.1, pp. 60–73. Birhane, Abeba and Marek McGann (2024). “Large models of what? Mistaking engineering achievements for human linguistic agency”. In: Language Sciences 106, p. 101672. Birhane, Abeba and Vinay Uday Prabhu (2021). “Large image datasets: A pyrrhic win for computer vision?” In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, pp. 1536–1546. Birhane, Abeba, Vinay Uday Prabhu, et al. (2023). “On hate scaling laws for data-swamps”. In: arXiv preprint arXiv:2306.13141. Blokpoel, Mark and Iris van Rooij (2021-2025). Theoretical modeling for cognitive science and psychology.url:https://computationalcognitivescience.github.io/lovelace/part_ii/co herence. Bousquet, Tim (2025). “What is ’artificial intelligence,’ anyway? The confusion is the point”. In: Halifax Examiner.url:https://www.halifaxexaminer.ca/morning-file/what-is-arti ficial-intelligence-anyway-the-confusion-is-the-point/. Chan, Anita Say (2025). Predatory Data: Eugenics in Big Tech and Our Fight for an Independent Future. University of California Press. Connell, Sophia M and Frederique Janssen-Lauret (2022). Lost voices: on counteracting exclusion of women from histories of contemporary philosophy. Crawford, Kate (2021). The atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press. Duarte, Tania et al. (2025). Resisting, Refusing, Reclaiming, Reimagining: Charting Challenges to Narratives of AI Inevitability. Dusseau, Melanie (2025). A Psalm for the Analog. Elgin, Catherine Z (1999). “Considered Judgment”. In: Considered Judgment. Princeton University Press. — eds. (2005). Non-foundationalist epistemology: Holism, coherence, and tenability. Boston: Blackwell, pp. 156–167. — (2017). True enough. MIT press. Erscoi, Lelia, Annelies V. Kleinherenbrink, and Olivia Guest (2023). Pygmalion Displacement: When Humanising AI Dehumanises Women. Evans, Claire L. (2020). Broad band: the untold story of the women who made the Internet. Penguin. Forbes, Samuel H. and Olivia Guest (2025). “To improve literacy, improve equality in education, not large language models”. In: Cognitive Science 49.4, e70058. Gage, Matilda Joslyn (1883). “Woman as an Inventor”. In: The North American Review 136.318, pp. 478–489. Gebru, Timnit and Émile P. Torres (2024). “The TESCREAL bundle: Eugenics and the promise of utopia through artificial general intelligence”. In: First Monday 29.4. Guest, Olivia (2024). “What makes a good theory, and how do we make a theory good?” In: Computational Brain & Behavior 7.4, pp. 508–522. — (2025). What Does ‘Human-Centred AI’ Mean? arXiv: 2507.19960 [cs.AI]. Guest, Olivia and Samuel H. Forbes (2024). “Teaching coding inclusively: if this, then what?” In: Tijdschrift voor Genderstudies 27.2/3, pp. 196–217. Guest, Olivia and Andrea E. Martin (2023). “On logical inference over brains, behaviour, and artificial neural networks”. In: Computational Brain & Behavior 6.2, pp. 213–227. — (2025a). “A Metatheory of Classical and Modern Connectionism”. In: Psychological Review. — (2025b). Are Neurocognitive Representations ’Small Cakes’? url:https://philsci-archive.pi tt.edu/24834/. 9