Olivia Guest

Anxiety

Sun, 02 Aug 2026 00:00:00 +0000

Olivia Guest, 2 August 2026

Anxiety is usually different to stress only insofar as it is persistent and remains even after the stressor departs. But AI is apparently ever present, always intertwined with everything... so who knows what I feel. I struggle to really comprehend how unwell I am sometimes because of the erosion of what feels like everything, turbocharged by AI. Since joining an AI-related department 5 years ago, for me at least, everything has become fully infected.

I'm going to lay out some thoughts with the hope maybe this exercise provides something helpful to others, serves as healing for myself, and possibly and eventually places a small pebble on the road to getting us all out of this mess — like Nikhil Suresh says "to reassure the people trying to survive amidst all of this that they are not crazy." Also thanks to those like Lauren Fraser who said that they may be interested in hearing what I am about to regurgitate below.

should I even share this?

According to what I have learned living in Western Europe for such a long part of my life (roughly from 18-years-old onwards), I express my feelings just fine when I need to. So while in the UK nobody really cared that my stiff upper lip also switches places with me expressing my feelings when I want to, in the Netherlands my ability to express myself, cry in public, or even just have facial expressions, is fetishised.

This has caused me quite a lot of introspection — and given rise to misunderstandings even in feminist spaces, especially in feminist spaces, because I feel so othered for being able to share. I bristle at how within academia here the natural tendency is to outsource emotional processing from the seniors to the juniors and even more terrifyingly to students. Yes, it's good to cry. It's great to express negative and positive emotions in healthy and appropriate ways. But, and I cannot stress this enough, it's abusive to expect management of our emotions from the people we mentor, supervise, or teach.

Emotions in my new culture, superficially presented as positive to express, are a method and locus of extraction from our more vulnerable colleagues or from our students. The concepts of emotional labour, affective labour, and emotion work are helpful here: simply put, ways of forcing people, usually women, to suppress their own emotional state and act with the goal of making others feel good. In my context, I witness the more powerful, more Germanic, more promoted academics extract emotional labour from those around them. These people jump from no processing of their feelings to vomiting them out on students, for students to mop up, for students to be harmed by the toxicity of their unprocessed nonsense.

Most vitally, if a mentee comes to us with a problem, it is our pastoral duty as academics to address their problem however possible — and not to overshare and centre our own (thematically similar perhaps, but in context irrelevant) problems. This coerces them into providing us with emotional support, subverting the reality of the roles, and creating a topsy-turvy world in which we use opportunities to help others (which in this case is our paid job to do) as therapy sessions for ourselves. Academia operates on a patron system, so the mentee will likely never say: "You cannot do this to me right now. You need to help me with simple words and effective actions. I cannot help you with your problem anyway because I am junior and powerless." In all this emotional manipulation what gets left behind is that they came to us for material help. Shameful.

So, yes, now I am finally myself scared to share. Where am I sending these feelings? And will the receiver feel compelled to care about mine over theirs? From the ADHD choir of voices, or better put the tornado of thoughts in my head emerges the correction: Nah, just spill it. A website is not a classroom. Your readers are not structurally under your thumb. Do not let emotionally stunted people who cannot manage their own emotions — or their own tendency to extract from others under the guise of emotional maturity! — get to you. You have processed the feelings a lot. They might even be useful for some. Just spill it.

You may wonder what this emotional extraction and turmoil has to do with AI. It has everything to do with AI. Or rather AI has everything to do with it. Emotional extraction and oppression is enacted by AI — and these forces run on similar lines whereby psychological exhaustion of the more vunlerable plays an active role in keeping us in place.

From another angle: simply put, fascist AI and all its symptoms force some of us academics to confront our complicity, our intertwinededness, and the moral imperative to take a stand. I will not be silently complicit in extracting from mentees and students, whether its their data, their emotional and cognitive labour, or anything else. I will not stand by and allow the university to be hollowed out and destroyed. An imperfect institution needs to be improved, not sold to the lowest bidder — or sadly and more accurately, should not pay a subscription to the evillest corporation.

Abdaljawad Omar outlines Western academia and society in general as

"a listening subject already fortified against the implications of what it hears, a political discourse that turns truth into noise, that converts even the most exhaustive documentation into the occasion for another round of managed uncertainty, and that has learned to wear the grammar of intelligence as its instrument of foreclosure. [...] The Western intellectual's cowardice is real: the fork in the road existed, the choice was made, the responsibility is not dissolved by calling it structural."

In the time of AI enabled genocide in Palestine, where we watch AI companies clamour to facilitate the USA's attack on Iran, as well as openly flaunting their contributions to planetary destruction, we are also witnessing the loss of the university. We sell out to companies so much, it's not even on all people's radars as a conflict of interest. We had one job. Our role as academics is to protect this one institution. This is why we receive tax money; why we're basically civil servants. The role of the university is even lost on many, so bears repeating: produce knowledge and educate at the highest level. AI destroys both at their core. It exposes some of us as confused, some of us as opportunistic, and both groups as cowards who would rather extract than reflect, would rather move fast and break things than learn to follow the slow and methodical praxis of scholarly work.

AI enters the fray

reality itself seems far away

This emotional extraction and turmoil is also very personal. Who as an academic in their right mind would not feel betrayed, angry, unhinged — a shattering of our worldview — at the discovery that our colleagues, our superiors, those who manage the university at every level are fine with all this? They are fine with complicity in genocide, in scholasticide at home as well as abroad, in the destruction of their own university itself.

I have cried and lost sleep countless times... a new day a new chance for a loss of faith, for new levels of anything from anxiety to fear to depression to a braid of all the above. Why do so many of my colleagues, especially those with power to act, not see it? Why do they not care? We all who ask, in pain not in true questioning, know why. AI is a fascist project that enables and is enabled by all these interlocking events. And we've even been here before.

This emotional extraction and turmoil also extends to everything. The realisation the students are being pushed into this morass too: Can I trust my own mentees? I have caught myself thinking that a student's work is AI-generated when I had no good reason to. Just because the work was exceptionally good, something we had worked on together over months, I, anxiety-ridden and my trust in everybody fragile, reasoned it could be infected by AI. I went into the files and I looked at the historical edits. There was no evidence of AI, but just the dawning realisation that I and others like me could lose our grip, have lost our grip, on what trusting a colleague or a mentee means. I should have had the confidence to trust my junior collaborator without looking at the edit history.

I hated myself deeply in that moment. I remember crying so much. I also had flashbacks to many other unpleasant feelings of rejection from colleagues over my principles, over research areas, over the mere idea that certain people actually do deserve their voices heard, finally. It all mixes together in such moments. I really let it all get to me.

The second time these thoughts came over me, familiar but the memory did not help stop the anxiety from regrowing, festering. I felt so unwell and hated myself so much for thinking these thoughts, for accusing somebody even if just in my mind. I went to bed for about two days. Why should I be perpendicular to the world when I want it to go away?

As academics, we keep asking our employer to listen to us, to understand that our demands are about the future of the university as an institution, about the toppling of expertise and skill-learning as we know it. We don't want Microsoft's nonsense products. But we also do not want the frames and rhetoric from industry; we refuse the maliciously designed concepts, the pathetically framed sales pitches, the arguments from ignorance. No, if AI is a vague term, one cannot conclude some AI may be useful. No, Google's Alphafold is not scientific and not a different kind of technology. It's built on very similar formalisms — and perhaps even more importantly, constitutes the corporate capture of biology. Yes, such models are barely scientific. Thank goodness Google has killed it both as a product and as an excuse for these kinds of anti-scientific claims.

AI's way of thinking is not just anti-science, but spills into pseudoscience and other forms of complete detachment from reality. Core to the anxiety I am experiencing is that I feel so many around me have become unmoored from ethical and practical concerns, like keeping universities going, and attached instead to nonsense, to put it mildly. Nikhil Suresh explains this:

"Executives that tell the press that their company has gone insane will quickly find themselves removed from their positions. Employees who are honest will find themselves fired in short-order, or 'randomly' selected for a round of layoffs. In fact, it is in the interests of almost every actor in the space — boards, executives, employees, vendors, consultants — to obfuscate and misrepresent the success rate of AI projects. Many publicly traded companies are putting out announcements about their AI productivity gains when I know for a fact that the businesses have done nothing other than purchase Copilot licenses and declare victory."

Universities have done this too. And we best listen to those who blow these whistles before it's too late.

OK, so I had a few panic attacks because I realised the fabric of trust between me and my students, especially my closer mentees, was pretty thin. And also damaged is the belief that my colleagues, and even the university in general, have similar goals, like a love of learning and of teaching, a respect and appreciation for our jobs, a deep care for the psychological landscapes we all inhabit, and a need to cultivate our social relationships. Recall how like AI extracts labour, I witness senior colleagues extract from juniors their mental, and especially emotional, labour, offloading stress and cognitive processing onto others. And most disturbingly, pushing onto them this labour in their time of need. AI's logics do this to us too. Attacking educators and researchers is not a coincidence, but a group of people vulnerable after many rounds of funding cuts and anti-scientific and anti-humanistic thinking.

So what can we learn from this? Well, firstly, the obvious: be kind to ourselves if we feel the walls closing in because... well, yes, they are, so we need all the mental and physical fortitude we can muster. But, and perhaps more importantly than the well-known "take care of ourselves and others", we must keep at the forefront: this is what AI companies want. They want us to falter and get sick. They want us to have panic attacks, which sure, we will have, but — and this is the really vital bit — we will strive not to resolve the panic by allowing AI logics in.

Argument from ignorance, which a panic attack might trigger, is not valid: there is no good AI, just because it's a confusing terminological mess. The same goes for descent into using AI-detection tools. Just because some prose may look like it's written using an AI tool, in fact there is no such thing as automated plagiarism detection software. They are a scam sold by the very same companies that create the AI systems used for plagiarism in the first place. And companies through such so-called tools want to further erode our trust in each other.

Nor is it OK to allow AI to rule us by guiding our teaching — in fact I and my coauthors implore us not to

"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."

We also do not need to obey in advance as some love to do under increasing fascism. As Timothy Snyder explains:

"Most of the power of authoritarianism is freely given. In times like these, individuals think ahead about what a more repressive government will want, and thfen offer themselves without being asked. A citizen who adapts in this way is teaching power what it can do."

In our capacity as academics, perhaps the most radical thing we can do is our job itself. How can anybody in their right mind notice the growing damage to universities and instead of stand against this, facilitate the destruction of expertise, of learning skills, and of academic integrity? And the simplest stand is to refuse all the frames that want us scared and distrustful and instead embrace just doing our jobs and supporting each other.

can an individual make a difference?

Credit for the pixel art goes to Magnific. The roses are a wallpaper from the Smithsonian.

Entryism

Sat, 18 Jul 2026 00:00:00 +0000

Olivia Guest, 18 July 2026

Huh? "AI entryism"?

Now that being against AI in an informed way is becoming mainstream, we need to be aware of entryism: the long-term strategy deployed by pro AI people or entities, like companies, to subvert such movements. In the general case it involves essentially enemy actors infiltrating groups in order to influence their ability to act in accordance with their values. Most examples you will probably find online involve political parties, but it happens across the board in any type of organisation or movement.

In the context of the push back against AI, entryism takes on the form of actively sending infiltrators to join universities or related organisations (in the strongest case), or recruiting members of those institutions, or spreading rhetoric or cultish language (in the weakest, and likely easiest, case) in order to undermine push back against AI. The important part is to do all this by looking like one is not against the organisation's goal (i.e. knowledge production and education) or even more insidiously by looking like one is staunchly anti AI explicitly. For the weaker case of rhetoric: success is measured by the organisation adopting harmful concepts through terminologically vacuous or damaging language (see me explaining this over video here).

As we say in this Project Syndicate piece:

"Many scholars, including us, have highlighted the threat posed by techno-solutionism in education: Rather than expanding our intellectual horizons, these technologies undermine the very conditions that allow us to think for ourselves."

So what's extra scary in the AI context is the goal of the technology itself is to harm our ability to detect entryism — it harms our ability to think for ourselves. Much like fascism generally, if left unchecked and unaddressed: AI is a totalising force that destroys people and planet from the inside out. AI entryists want to join committees as if they are anti AI and then instead make pro AI organisational rules, for example.

A recent high-profile example of general entryism is Graham Platner's failed attempt to infiltrate, ironically enough, an already pretty right wing party: the USA's Democrats. Rebecca explains that

"his opposition revealed that he had an actual Nazi tattoo on his chest, which he got 19 years ago to celebrate fighting in Falujah. [However, i]nstead of apologizing for that one, Platner made up the completely ridiculous story that he had no idea the tattoo was a famous Nazi symbol."

This method of mixing half-truths with apologies (his original tactic), and then shifting to completely lying is very valuable to notice as a pattern, as is that certain groups like people of colour, and especially women of colour, clocked him instantly. Entryism is not hard to detect for everybody. It's not at all hard for those with expertise and who know what it is that is being attempted, why it is being attempted, and that — in his case — Nazi tattoos and especially those done in a country that was under Nazi rule are impossible to get done accidentally, amongst many other obvious indicators.

Other examples of entryism are cases where Microsoft has convinced our employers, university executive boards, to spend tax money to force us to all use their ghastly products. This can be counted as a soft AI entryist manoeuver. The call is coming from inside the house. This is why many countries, who recognise this is a genuine risk to their sovereignty, have been moving out of the Microsoft ecosystem.

What can we do?

I have a core take away I must repeat: just because somebody claims to be anti AI does not mean they are not doing entryism. Sadly, that is (in isolation) entirely consistent with entryism. Yes, these people lie about their beliefs.

I repeat this quote from Marx that I put right at the top of our work critiquing this correlationist logic from 2023:

"[A]ll science would be superfluous if the outward appearance and the essence of things directly coincided. (Marx, 1894, p. 592)"

This is the logic of AI, of fascism, of sexism, of racism. That is, because things look a certain way, that therefore they are a certain way. Internal properties are wrongly derived from their superficial appearance. "I see that somebody has long hair, therefore I can conclude they are a woman." Or worse still: "I see the external features consistent with (what I think is) woman, and I conclude she must be stupid" — a fractal wrongness that starts with assuming that what you (naively, unquestioningly, think you) see is what you get when it comes to complex organisms. Further down in the same paper, we warn:

"Just because a model correlates with neural and behavioral data, it is not sufficient for us to infer that the model is performing cognition: correlation does not imply cognition."

Again, the core logics of AI are the same as racism and sexism, which is why these issues pop up again and again in these systems: it involves taking the simple shallow features of things we see, like skin colour, or hair length, or whatever other superficial property, and moving to very flawed conclusions. There is something addictive to this form of sheer intellectual laziness — as well as structurally beneficial to those select few within capitalism, white supremacy, and patriarchy — to this simplistic logic. It's easy to just point at people and call them names, I guess.

Non-human animals can escape from this correlationist logic. Think of the animals who figure out that scarecrows, or other such fake predators, are not alive. So even though first they may fear them similarly to their real counterparts, animals realise that superficial resemblance is not identity: "the outward appearance and the essence of things [have not] directly coincided" — a fake owl is not an owl. Correlationism is false.

I want to underline, people can make mistakes. Not everybody is knowingly using cultish language from industry, some still think these terms refer. But notice who plows on regardless, who does not listen to feedback, who materially and not just ideologically aligns with the AI companies. And like with the case above from big P politics, once you see it, nothing the person can do or say means they can return back to the table, nor that they can deserve an audience, nor even that we owe them charitable readings of their work. This is not about everybody who slips up being irredeemable. These mistakes are normal. I am talking about those with immense power, and who consistently facilitate AI entryism, being held to account. Nobody in the abstract is owed an audience nor do they deserve the extremely privileged job of being an academic.

Marx's warning about Science being superfluous however, is extra timely and even accidentally funny. An attempt at pretty blatant AI entryism was uncovered just the other day: the Editor in Chief of Science (the journal) wrote an article titled AI in scientific publishing: Slower, worse, and more expensive. You might think: that's great? But Martin Clavey noticed and others brought it to my attention that many links in the references bear the hallmark of having used ChatGPT. This is pure entryism. He wrote a piece seemingly critical of AI, got caught and then admitted to ChatGPT use, and also claims it's a search engine and it's useful for checking links. A whirlwind of strange claims, many of which are not true on their face: it's certainly not a search engine. How can one claim AI is "slower, worse, and more expensive" with a straight face here? And how can one excuse this behaviour when such a powerful figure does it? Some with medium power may now be less likely to be anti AI if they wish to be published in Science. A bad way of thinking for these scientists, yes, but many junior people are under such senior people's thumbs. Entryism radiates outwards.

My final plea

We can't be anti AI if we don't make an effort to see these attempts at subversion. Entryism from industry is an extremely useful tactic, which still affects and harms science in general, as well as Science specifically as we see above. Regardless of how many times we try to root out the conflicts of interest caused by AI they keep popping up like satanic mushrooms, e.g. "Maples et al. find themselves part of a grotesque, predatory spectacle, laundering it with institutional credibility." And we live with the consequences of how industry forces have successfully done this to harm climate science, to protect the tobacco industry and keep inserting it into the scientific literature, and more.

If we are not careful with our reasoning about these events and learning from the past, the same forces will act on us with respect to AI. Are petroleum companies good now because they also invest in renewables? Or do they still owe the whole planet reparations?

We must stop thinking that just because somebody agrees with us on some superficial anti AI point, that they are in fact in some deeper agreement on principles and values. Poor argumentation, like argument from ignorance or confusing correlation with causation, is our downfall. The case of Science above clearly shows that somebody can be Editor in Chief of a very powerful journal, one that makes or breaks careers, and can write a piece called AI in scientific publishing: Slower, worse, and more expensive, and then use ChatGPT without disclosing it until confronted.

AI entryism requires us to fight the thoughtless process of assuming that things are the way they look. As we say in Guest and Martin (2023), a clockwork timepiece is not composed of digital components even if the outwards appearance is seemingly identical. And a person, astronomically more complex and interesting than a clock, claiming fealty to our cause when the stakes are high, can be performing entryism if we do not know the signs. Actions matter more than words.

Cryptogyny

Sun, 15 Mar 2026 00:00:00 +0000

Olivia Guest, 15 March 2026

Cryptogyny is the systematic obfuscation of women's contributions to, or of their very existence in, a field of study. As Sam Forbes and I say here in the context of teaching programming:

Obfuscating women’s interests in and contributions to technology echoes through the ages (Erscoi et al., 2023); see Figure 1. Culinary recipes are archetypal algorithms, but cooking is not seen as related to programming (cf. Shore, 1985). Jaccard looms, machines that weave cloth, are the original use case for punchcards – physical pieces of paper that were used to program computers (Harlizius-Klück, 2017). Ada Lovelace invented the first computer program (Aiello, 2016). Women mathematicians and programmers worked with the first digital computer, the ENIAC (Kleiman, 2022). Grace Hopper invented the compiler (Beyer, 2012). Core rope memory was created through knitting copper wires by women for NASA’s Apollo missions (Rosner, Shorey, Craft, Remick, 2018). Margaret Hamilton was Director of the Software Engineering Division that, inter alia, took humans to the Moon (Hamilton & Hackler, 2008).

[...] Ultimately, these are typical trends within capitalist patriarchy, where women’s – and all minoritised people’s contributions – are systematically hidden from the historical retelling of humanity’s achievements (also known as cryptogyny, the Matilda effect: Connell & Janssen-Lauret, 2022; Evans, 2020; Gage, 1883; Hicks, 2017; Kleiman, 2022; Lee Shetterly, 2016; Pozo & Padilla, 2019; Pozo-Sánchez & Padilla-Carmona, 2021; Rossiter, 1993; Van den Brink & Benschop, 2012). Part of a good teacher’s repertoire is this fact, which both drives a more expansive appreciation of their own field and results in a broader and more interesting syllabus.

For more on the above, see:

Guest, O. & Forbes, S. H. (2024). Teaching coding inclusively: if this, then what?. Tijdschrift voor Genderstudies. https://doi.org/10.5117/TVGN2024.2-3.007.GUES

I think I discovered this "new word for an old phenomenon" (as those who coined it say; Pozo & Padilla, 2020) when I came across the page on Wikipedia in the last 4 or so years. But I have lost track of when I realised the phenomenon itself. And the depth of it is incredibly overwhelming — and perhaps the lack of interest in stopping it or rectifying it just as deep, and as overwhelming.

Importantly, this phenomenon survives to this day. One need only search for terms like "gender citation gap" or similar to find much work showing in many fields of study, such as Kristina Lerman and colleagues from 2022: Gendered citation patterns among the scientific elite.

penicillin

Other than those touched on in the extract above, I think this is a particularly compelling example written by Autumn Stanley in 1998 (p. 25):

although three men received the Nobel Prize for penicillin, women participated significantly in the team effort that brought the drug to medical usefulness. Women had discovered the mold’s usefulness centuries or perhaps millennia earlier (Halsbury 1971, p. 19; Raper 1952, p. 1), and one nineteenth-century Wisconsin woman, Elizabeth Stone, an early antibiotic therapist, specialized in treating lumberjacks’ wounds with poultices of moldy bread in warm milk or water: she never lost an injury patient (Stellman 1977, p. 87). In the twentieth-century development of the drug, it was a woman bacteriologist, Dr. Elizabeth McCoy of the University of Wisconsin, who created the ultraviolet-mutant strain of Penicillium used for all further production, since it yielded nine hundred times as much penicillin as Fleming’s strain (Bickel 1972, p. 185; O’Neill 1979, p. 219). And as Howard Florey, leader of the British penicillin team, was quick to point out, it was Dr. Ethel Florey’s precise clinical trials that transformed penicillin from a crude some time miracle worker into a reliable drug. It was also a woman, Nobel laureate and X-ray crystallographer Dr. Dorothy Crowfoot Hodgkin, who finally determined the precise structure of the elusive penicillin molecule (Bickel 1972, p. 216; Opfell 1978, pp. 211, 219).

Women were also involved in developing the sulfa drugs that preceded penicillin. For instance, it was a married pair of chemists, Prof. and Mme. Tréfouėl, and their colleagues at the Pasteur Institute in Paris who split red azo dye to create sulfanilamide (Bickel 1972, p. 50). At least two women have invented new antibiotics for which they receive sole credit. Dr. Odette Shotwell of Denver, Colorado, came up with two new antibiotics—duramycin and azacolutin—during her first assignment as a research chemist at the Agriculture Department laboratories in Peoria, Illinois. She has also invented new methods for separating antibiotics from fermentation by-products, and in doing so has played an important role in the development of two other antibiotics: cinnamycin and hydroxystreptomycin. Dr. Marina Glinkina of the USSR directed the laboratory effort that produced a new antigangrene antibiotic during World War II. Her postwar work as a senior scientist has been theoretical (Dodge 1966, p. 226; O’Neill 1979, p. 32; Ribando 1980).

This is an extract from the book chapter:

Stanley, A. (1998). WOMEN HOLD UP TWO-THIRDS OF THE SKY: Notes for a Revised History of Technology. Sex/Machine: Readings in Culture, Gender, and Technology. https://www.jstor.org/stable/jj.5501032.6

before 1800

Another really compelling and fun case is that in this wonderful paper from 2023 by David de la Croix and Mara Vitale: Women in European academia before 1800—religion, marriage, and human capital. They end their abstract on: "Comparing [the 108 women] with 58,995 male scholars, we find that they were on average better"! They elaborate:

we establish a catalogue of [108] women in academia during the medieval and early modern periods[,] scholars and literati who were either teaching at universities or involved in scientific academies in Europe over the period 1000–1800.

They go on to explain — another interesting tidbit:

we expected to conclude that women were more welcome in the most modern part of Europe, such as the universities of Leiden, Glasgow, Copenhagen, or Göttingen, and in scientific academies such as the Royal Society, the Swedish Academy of Sciences, or the Leopoldina. We were surprised to find very few female academic scholars in Northern Europe, whereas they were more numerous in the South. Women taught at the universities of Bologna, Padua, and Salamanca and were members of academies in Arles, Nancy, Lyon, Beziers, Padua, Rome, and Madrid.

De la Croix and Vitale (2023) expected the opposite of what they found, as they say, because prevailing understanding is that there exists "a clear divide between a modern Europe [in the North] and a conservative one [in the South]". In fact, as they tell us, no women in Northern Europe "were allowed to teach at a university, even in an informal way, or to be admitted to full membership in an academy of science and letters."

For the full list of women, please check out their paper, which also has maps of where the women they found worked:

De la Croix, D., & Vitale, M. (2023). Women in European academia before 1800—religion, marriage, and human capital. European Review of Economic History. https://doi.org/10.1093/ereh/heac023

what can we do?

I completely understand that some of this can be distressing — especially since it is happening right now too. One thing we can all do to rectify this, is to understand the process of the obscuring of women's contributions, cryptogyny, often goes hand-in-hand with the theft of women's contributions through attributing them to men, part of what is known as great man theorising. And cryptogyny of all kinds can be addressed, at least in great part, by a few simple steps. Find and read more women's work. Cite more women's work — and notice the people who you cite! And sadly, yes, always be suspicious when you notice great person, especially man, theorising. What do I mean? Londa Schiebinger (1987, p. 314) does a great job explaining it:

Most of the work on women scientists fits the “history of great men” mold, with women simply substituted for men. Thus, we have many biographies of great women scientists. These biographical studies of women scientists, for the most part, place the achievements of Marie Curie or Rosalind Franklin within the male world, demonstrating that women have, in fact, made important contributions to what has been defined as mainstream science. Yet the focus remains on the woman as exceptional—the woman who defied convention to claim a prominent position in an essentially male world. One of the problems with this approach to history is that it retains the male norm as the measure of excellence.

If you are in my fields, neuro-, cognitive, psychological, computational sciences, I also elaborate on this at length, especially in Guest (2024) in the context of theory and beyond:

great man theorising is ‘an important obstacle to building theory’ (van Rooij , 2021; also see van Rooij , 2022). Great man theorists fabricate and/or obscure anecdotal and historical evidence to ensure the credit is directed at the target prestigious figure. For example, Pythagoras is overwhelmingly named and credited, but he had many women disciples, who were philosophers and mathematicians in their own right, whose writings survive to this day, and after his death, Theano (a woman, possibly his wife) took over as head of his academy (Schiebinger, 1987; Wider, 1986). To give another example, Lavoisier, the chemist credited with discovering oxygen, was also a woman: Marie Anne Paulze Lavoisier. She could have been credited jointly with her husband, but was not (Eagle & Sloan, 1998).

For more see:

Guest, O. (2024). What Makes a Good Theory, and How Do We Make a Theory Good?. Computational Brain & Behavior. https://doi.org/10.1007/s42113-023-00193-2

Birhane, A. & Guest, O. (2021). Towards Decolonising Computational Sciences. Women, Gender & Research. https://doi.org/10.7146/kkf.v29i2.124899

Also if you are curious about the banner at the top, it is a zoomed version of this photo: Composing room of the New York Times newspaper. Linotype operators.

Why does cheating matter?

Sat, 07 Mar 2026 00:00:00 +0000

Olivia Guest, 7 March 2026

introduction

It has come up a number of times both on social media and in private conversations that especially junior people in academia, such as PhD candidates, are unable to not use AI. That is so-called artificial intelligence chatbots or similar products (see the this paper here to help understand what this means). They tell me that they feel so much direct or otherwise pressure from peers to use AI that they fear for their careers if they don't. If this describes you or addresses worries you've heard from others, then this is for you.

So, I will assume that at some point, you came across work like mine here and want to protect yourself as well as be an ethical actor:

Guest, O., Suarez, M., Müller, B., et al. (2026). Against the Uncritical Adoption of 'AI' Technologies in Academia. Digital Culture & Education. https://doi.org/10.5281/zenodo.20082828

To address worries about these pressures and explain how the in principle rejection is actually the only way, which I describe here and elsewhere, I dedicate this short piece to you.

counterproductive & self-harming

So like I said I assume you care about the environmental impacts, about the harm to other humans, especially in the Global South, who are directly harmed by their involvement in these technologies, and about the damage to creative industries, and more. I think you get these, deeply, and in addition also worry about yourself.

I have heard from fellow Cypriots, for example, they worry Western Europeans and USAmericans are hypocrites for asking you not to use these so-called tools. That the people telling you to abstain probably use these tools anyway or that at least others you may compete with are using them, and so you might conclude this will harm you and your career. You are very right to worry about your career, your skills, and your ability to navigate an extremely damaged and unjust world compared to that of previous generations. And yes, most Europeans and most USAmericans, especially in academia are indeed hypocrites.

In many senses, as is often the case with AI, we've been here before. What do I mean? Let me tell you.

During my computer science undergraduate degree, I had a huge culture shock. I guess I was naive, but it never occurred to me, until I saw it, how much my fellow students were cheating. Perhaps it's all neutral and at that stage means little, but still I think my shock captures something meaningful. What they were doing, my first such exposure, was that they were sharing validation tools with each other (that only one person had written) to test their code for assignments. Impressive that an undergraduate thought to write a thorough unit test suite, I guess, in Scheme — but like I said, it made me feel uncomfortable. It took away, they took away from themselves, it seemed to me the ability to write such a tool themselves or just somehow complete the assignment alone. I now know this is collusion, a form of cheating, a violation of academic integrity.

But feel free to leave aside the rules, or focus on their spirit. As an educator, I can promise you the whole point of such assignments is to learn through doing it yourself. If you do not do that you have missed the opportunity to learn something, likely a useful skill, or to practice part of a skill you already have and thus refine your ability, or to test yourself on something you already may know but of course cannot really know if you are still an undergraduate. Cheating here actually can damage you by robbing you of this opportunity, a chance to test yourself and receive feedback on your attempt. This is one of the reasons you came to university after all: to interact with lecturers, for them to give you feedback, and for you to learn as much as you can from it. Nobody else is harmed by you not learning something. Perversely, if I bring back your frame above of the competitive job market, they may even benefit! Because some of them did indeed not only complete the assignment, but they also thought of and built a tool to check their answers. They learned.

If you are in your late teens or early twenties and/or in an undergraduate degree programme this might be one of, if not the only, time in your life you will have no other major responsibilities other than to learn these intellectual skills. If you are in a full-time PhD, this is possibly the only time in your career — trust me! — where the largest part of your week can be dedicated to learning these foundational skills. And they will serve you well in academia, especially if you plan to stay. The skills I learned at these career stages are my most treasured and clearly ones that opened doors. "I can code this from scratch" is way more impressive than you might think given toxic industry pro-AI nonsense. The same goes for writing clearly, reading and comprehending journal articles, thinking things through, and on and on.

not victimless

So as I have outlined above, and as you already know, using AI is not victimless; everything from mining for rare earth minerals to the content-moderation of these models are all harmful. Often you not only are the product — yes, they will steal and sell your data — but you are also robbed of learning. When the hype inevitably dies down, as it has done many times before, through AI summers and winters (see my talk here if you were not aware of these cycles), you want to have skills. You want to have a degree that matters.

This brings us to perhaps a lesser victim in your calculations: the complete destruction of the university as an award-granting body if everybody uses AI to cheat. So when you want to advance in your career from student to academic faculty, or use your degree in any way, something might collapse on the other end of the equation: your degree itself. If degrees are meaningless because they do not reflect skills anymore, then academia is also damaged beyond repair; as well as society in general in our ability to have experts.

For more on this issue of respecting expertise, see this section here, which is based on:

Guest, O., Suarez, M., & van Rooij, I. (2025). Towards Critical Artificial Intelligence Literacies. Zenodo. https://doi.org/10.5281/zenodo.17786243

Theory

Sun, 01 Mar 2026 00:00:00 +0000

theory

Olivia Guest, 1 March 2026

about

On this page are pointers to materials I have created for cognitive scientists to know where to look to help build a deeper understanding of theories, theorising, and other important reasoning skills. Inspiration to put these together came after a talk I gave at the Basque Center on Cognition, Brain & Language (also later at COGS, at the University of Sussex too), which also went over these points and which you can watch here. Plus it's been a while that I have wanted to write an overview for where to find my ideas on theorising in cognitive science, and especially psychology and cognitive neuroscience: to help students and junior scholars in the field. Importantly, thinking about theories is called metatheorising.

A possible depiction of cognitive science — a field that is trans- or inter-disciplinary over (at least) Philosophy, Linguistics, Anthropology, Neuroscience, Artificial Intelligence, and Psychology. (van Rooij et al., 2024)

For each major point I will tell you what to further read and what to cite, if you use or build off my ideas in any way (and do indeed properly cite me, if you do, as that is correct academic conduct). The main work however, is:

Guest, O. (2024). What Makes a Good Theory, and How Do We Make a Theory Good?. Computational Brain & Behavior. https://doi.org/10.1007/s42113-023-00193-2

Guest, O. & Martin, A. E. (2026). A Metatheory of Classical and Modern Connectionism. Psychological Review. https://doi.org/10.1037/rev0000591

what's a
theory?

Each person might have their own idea of what a theory is, but for this context and to help understand where I am coming from, here's a definition from Guest and Martin (2021, p. 794):

A theory is a scientific proposition — described by a collection of natural‐language sentences, mathematics, logic, and figures — that introduces causal relations with the aim of describing, explaining, and/or predicting a set of phenomena.

You will have to look at Guest and Martin (2021) to fully grasp how I separate theory from other scientific concepts, especially if you cannot yet disentangle it from hypothesis, a different beast altogether.

Guest, O. & Martin, A. E. (2021). How Computational Modeling Can Force Theory Building in Psychological Science. Perspectives on Psychological Science. https://doi.org/10.1177/1745691620970585

metatheory

Understanding theories, can be done by recognising that we as scientists reason over them using a metatheoretical calculus — a phrase I coined to help discussing just this: how we adjudicate over our theories. For a deeper understanding on this, why it is useful and how to do it, as well as the background of the term, see the following three:

Guest, O. & Martin, A. E. (2023). On Logical Inference over Brains, Behaviour, and Artificial Neural Networks. Computational Brain & Behavior. https://doi.org/10.1007/s42113-022-00166-x

Guest, O. (2024). What Makes a Good Theory, and How Do We Make a Theory Good?. Computational Brain & Behavior. https://doi.org/10.1007/s42113-023-00193-2

Guest, O. & Martin, A. E. (2026). A Metatheory of Classical and Modern Connectionism. Psychological Review. https://doi.org/10.1037/rev0000591

Figure 1 from Guest (2024), which depicts a set theoretic view on individual singleton theories $\theta$ and families of theories $\mathbf{\Theta}$, plus their relationships to phenomena $\phi_k \in \mathbf{\Phi}$.

In addition to thinking about theories and their relationships to each other, we can also think about wanted and unwanted properties generally. Guest (2024) contains my suggestions for so-called categories of virtues and vices of theories — inviting scientists "to examine the properties and characteristics of theories, to propose additional virtues and vices, and to engage in further dialogue." (p. 508) These overlapping categories can be seen below in Table 1 from Guest (2024).

Category		Potential Questions
Metaphysical commitment, the need to highlight what parts of theory are not under investigation, but are assumed, asserted, or essential.		What is the theory? What causal relationships does the theory propose? What does the theory assert? In which field(s) is it embedded? What counts as (mis)use?
Discursive survival, the ability to be understood by interested non-bad actors, to withstand scrutiny within the intended (sub)field(s), and to negotiate the dialectical landscape thereof.		Is the theory accessible, easy to understand? Are members of the theory's community able to explain the theory? Are the theory's concepts transparent? Are labels used consistently in discussions? Can relevant outsiders understand the theory?
Empirical interface, the potential to explicate the relationship between theory and observation, i.e. how observations relate to, and affect, theory and vice versa.		What formalisations are appropriate or necessary for the theory? Which methodologies does the theory use to support or instantiate itself? What type of evidence does the theory require?
Minimizing harm, the reckoning with how theory is forged in a fire of historical, if not ongoing, abuses — from past crimes against humanity, to current exploitation, turbocharged or hyped by machine learning, to historical and present internal academic marginalization.		Does the theory contain great man theorising? Is the theory exclusionary to certain people or ideas? What is its relationship to previous theories? Does the genealogy of the theory contain harmful ideas? Do ancestral theories remain relevant to the present? What harms have resulted or could result from the use of the theory?

"The proposed ontology with descriptions of what kinds of properties of a theory, i.e. which aspects can be evaluated as virtuous or vicious, is captured in the column labelled 'Category'" (Table 1 from Guest 2024).

how to cite

If you are creating scholarly work based on my ideas, please cite me correctly using the publications mentioned above:

Guest, O. & Martin, A. E. (2021). How Computational Modeling Can Force Theory Building in Psychological Science. Perspectives on Psychological Science. https://doi.org/10.1177/1745691620970585

Guest, O. & Martin, A. E. (2023). On Logical Inference over Brains, Behaviour, and Artificial Neural Networks. Computational Brain & Behavior. https://doi.org/10.1007/s42113-022-00166-x

Guest, O. (2024). What Makes a Good Theory, and How Do We Make a Theory Good?. Computational Brain & Behavior. https://doi.org/10.1007/s42113-023-00193-2

Guest, O. & Martin, A. E. (2026). A Metatheory of Classical and Modern Connectionism. Psychological Review. https://doi.org/10.1037/rev0000591

The banner image is from The Public Domain Review's collection of images called Owen Jones’ Examples of Chinese Ornament (1867).

We've been here before!

Wed, 18 Feb 2026 00:00:00 +0000

Olivia Guest, 18 February 2026

Marilyn E. Jackler Memorial Collection of Tobacco Advertisements; this from 1969.

What do you mean?

We have certainly been here before. Many many times in the past, companies — just like artificial intelligence (AI) companies now — have lied to us to sell us products. Not only is there no reason to assume the AI industry is different, there is in fact much to make us think they are knowingly misleading us. To understand this, take a look at this great work called Late lessons from early warnings: science, precaution, innovation by the European Environment Agency (EEA):

The 2013 Late lessons from early warnings report is the second of its type produced by the European Environment Agency (EEA) in collaboration with a broad range of external authors and peer reviewers. The case studies across both volumes of Late lessons from early warnings cover a diverse range of chemical and technological innovations, and highlight a number of systemic problems. The 'Late Lessons Project' illustrates how damaging and costly the misuse or neglect of the precautionary principle can be, using case studies and a synthesis of the lessons to be learned and applied to maximising innovations whilst minimising harms.

Also there are well-known patterns of our thinking that are affected by interacting with certain machines. As Brian P. Bloomfield (1987, p. 72) explains four decades ago:

While opacity is a distinguishing feature of many other areas of science and technology, the myths surrounding computing may stem less from the fact that it is an opaque esoteric subject and more from the way in which it can be seen to blur the boundary between people and machines (Turkle 1984). To be sure, most people do not understand the workings of a television set or how to program their video cassette recorders properly, but then they do not usually believe that these machines can have intelligence. The public myths about computing and AI are also no doubt due to the ways in which computers are often depicted in the mass media — e.g. as an abstract source of wisdom, or as a mechanical brain.

For detailed analysis and more examples see:

Guest, O., Suarez, M., Müller, B., et al. (2026). Against the Uncritical Adoption of 'AI' Technologies in Academia. Digital Culture & Education. https://doi.org/10.5281/zenodo.20082828

Another related aspect — described by Valentina Ponomareva (1998) — is that:

it is impossible to create an absolutely reliable automatic system, and sooner or later people face the necessity to act after equipment fails. [...] If the cosmonaut loses such skills because of [their] passive role [due to being typically limited to monitoring and observation only], the probability of [their] choosing and carrying out the right procedure in an emergency would be small. This contradiction is inherent in automatic control systems.

For more on this angle of AI see:

Guest, O. (2026). What Does 'Human-Centred AI' Mean?. Behavioral Sciences. https://doi.org/10.3390/bs16040583

How is this possible?

"She Caught Toto by the Ear." by W. W. Denslow, 1900.

Our present predicament is enabled by the fact that regulation is apparently not only missing in the case of AI, but also very hard to enforce even for industries where there is complete mainstream acceptance of their malevolent goals and harmful practices. The tobacco industry for example, still publishes research even in journals which try to enforce complete bans on such work. For one, they run disinformation campaigns, as Annamaria Baba and colleagues (2011) explain:

Internal tobacco industry documents that have been made available through litigation give us the opportunity to analyze one industry’s involvement in a broader industry strategy to secure enactment of the data access and data quality provisions. These documents provide unprecedented insight into the industry’s motives, strategies, and tactics to challenge the scientific basis for public health policies. In the 1990s Philip Morris implemented a 10-year “sound science” public relations campaign to create controversy regarding evidence that environmental toxins cause disease. Our article describes the tobacco industry’s later campaign to advance the “sound science” concept via legislation.

For another, as mentioned the tobacco industry continues to indirectly or directly publish medical research, as Irene van den Berg and coauthors (2024) document:

In recent years the “big four” global tobacco companies (according to sales)—Philip Morris International (PMI)/Altria, British American Tobacco (BAT), Imperial Brands, and Japan Tobacco International (JTI)—have invested billions in companies that produce medicines or other medical products. These investments include treatments for conditions caused or aggravated by smoking. For example, Vectura, a subsidiary of PMI since 2021, produces an inhaler used by patients with chronic obstructive pulmonary disease or asthma. And JTI’s pharmaceutical branch produces treatments for lung cancer, skin conditions such as psoriasis and atopic dermatitis, and heart disease.

The same tactics are also used by the petrochemical industry to sabotage science and regulation. If you want to read more on that read Linda Knoester and colleagues (2025): Academic Collaborations and Public Health: Lessons from Dutch Universities' Tobacco Industry Partnerships for Fossil Fuel Ties.

The AI and generally the technology industry is no different. Why should they be? They fund researchers and institutes, therefore creating academics and publications with conflicting interests, which often go un- or under-acknowledged especially in mainstream coverage (see The Grey Hoodie Project: Big Tobacco, Big Tech, and the threat on academic integrity; Abdalla & Abdalla, 2021). These conflicts of interest are sometimes very time consuming to dig up, for example see: Nature's Folly: A Response to Nature's "Loneliness and suicide mitigation for students using GPT3-enabled chatbots".

Standing against these corrosive forces is important, especially since they converge. For example, the AI industry wastefully burns fossil fuels while pretending to want to combat climate crisis. Read more here:

Suarez, M., Müller, B.,Guest, O., & van Rooij, I. (2025). Critical AI Literacy: Beyond hegemonic perspectives on sustainability. https://doi.org/10.5281/zenodo.15677839

Guest, O., Suarez, M., Müller, B., et al. (2026). Against the Uncritical Adoption of 'AI' Technologies in Academia. Digital Culture & Education. https://doi.org/10.5281/zenodo.20082828

Electricity by Albert Robida, 1890.

How can we spot it?

If you want to help yourself and others notice these harmful frames, even just perusing websites and papers such as these on critical artificial intelligence literacy is more than enough for a first step. If you're interested in actively disentangling, you can consider thinking deeply about what we outline here:

Guest, O., Suarez, M., & van Rooij, I. (2025). Towards Critical Artificial Intelligence Literacies. Zenodo. https://doi.org/10.5281/zenodo.17786243

These, shown in Figure 1 above (Guest et al., 2025), are ways of approaching the core problems AI causes and is caused by. Conceptual Clarity, which we say is the requirement for academic terms to have clear meanings and/or for users and proponents of such terms to accept where confusions still exist:

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.

Critical Thinking, which in this context we think is:

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).

"Decoloniality is the decentring of views which stem from white and masculine supremacy, technosolutionism, and fascism" (Guest et al, 2025, p. 5):

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).

Respecting Expertise, which actively involves stopping people from falsely claiming to be AI experts, as we explain (p. 6):

Academic expertise can only continue to exist if statements made by academics are understood as coming from highly skilled research and educational practitioners. [...] 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.

Slow Science, developed by Isabelle Stengers into a radical perspective on science, is "a disposition towards preferring psychologically, techno-socially, and epistemically healthy practices." (Guest et al, 2025, p. 2) On page 7, we explain that

AI not only epitomises fast science, but turbocharges it, nosediving us into anti-science and anti-intellectualism. 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)

Anybody and in their own context and manner can use these skills to deal with the AI industry's attack on our public institutions and private lives.

What can we do?

Metamorphosis Insectorum Surinamensium by Maria Sibylla Merian, 1705.

Zooming out, there is much you can do and think about — even just refusing to accept industry frames and rejecting their products is non-negligible. If you want to read more, my relevant work can be found on the CAIL page.

Most importantly of all, resistance can and should take on many forms. Remember to rest and take care of yourself and your community. If talking to friends and colleagues is easy, then try to engage them on these issues. If it is not possible to do so, you can instead (or in addition) seek out allies online.

Turing test

Sun, 15 Feb 2026 00:00:00 +0000

Olivia Guest, 15 February 2026

about

what is this page?

I have collected extracts of my writings on the Turing test, which Alan Turing referred to as the Imitation Game. For the reader, this page merely assumes you're interested in what I have to say on this topic. And so, below are a chronologically ordered series of extracts — in each case you may want to click through to the full paper for more context. If you find any of this work useful, please cite us using the papers below.

"NACA computer working with microscope and calculator, 1954." (Rare Historical Photos, 2025; also NASA, 2016)

what is the Turing test?

From Erscoi et al (2023, p. 11), we explain:

Turing introduces his original formulation of the test by means of “a game which we call the ‘imitation game’. It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. [...] The object of the game for the third player (B) is to help the interrogator. The best strategy for her is probably to give truthful answers. She can add such things as ‘I am the woman, don’t listen to him!’ to her answers, but it will avail nothing as the man can make similar remarks.” (pp. 433–434) Turing (1950) then asks “‘What will happen when a machine takes the part of A [the man] in this game?’ Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, ‘Can machines think?”’ (p. 434)

On page 12, we also underline that:

the original imitation game serves as a palimpsest or pentimento to modern conceptions of the Turing test, which remove gender and focus on whether a chatbot can trick a human into thinking it is another person (Kind 2022). What we mean is that any attempts to paint over or scrape away, i.e., whitewash, the origins of what is now called “the Turing test” by AI researchers ignore and obfuscate both the gendered history and the behaviourist assumptions of Turing (1950). In essence, the modern Turing test does not constitute a reimagining of the original game in any deep way, but a still flawed test, fraught with behaviourist assumptions, such as that personhood or intelligence can be inferred from looking at the inputs and outputs of a text-based chat (viz. Guest and Martin 2023). Authors that discuss the test both try hard to be traced back to Turing (1950) for provenance and prestige, but also to avoid mentioning gender and any of the original arguments, especially about extrasensory perception.

Judith Genova in Turing’s sexual guessing game (1994, p. 315) explains:

Seeing himself as a mathematical Pygmalion, armed with recursion and a few other tricks, Turing hoped to create a machine that could create other machines, and thus begin a new kind of evolution.

Women codebreakers at Bletcheley Park during the Second World War.

extracts

Pygmalion Displacement: When Humanising AI Dehumanises Women

Erscoi, L., Kleinherenbrink, A., & Guest, O. (2023). Pygmalion Displacement: When Humanising AI Dehumanises Women. SocArXiv. https://doi.org/10.31235/osf.io/jqxb6

Herein, we analyse the process through which women are dehumanised and AI is humanised, which we dub Pygmalion displacement. The section most relevant, in fact dedicated to the Turing test, is called Interlude: Can Women Think? — reading the whole of it might be useful to understand how the Turing test can be used to harm women. Specifically, we say on page 12:

Turing (1950) manages to seamlessly pit a woman both against a machine and against patriarchal standards of masculinity, wherein she must convince an interrogator of her humanity. Pygmalion displacement is evidently occurring here as her presence becomes fully erased almost instantly, where even player B is now, without any given reason, a man further down in the original text.
[...]
Turing’s thought experiment sets the stage for fictional replacements of women by machines to become the blueprint for historical, present, and future trends of Pygmalion displacement of human beings along gendered lines.

Figure 2 from Erscoi et al., 2023.

On Logical Inference over Brains, Behaviour, and Artificial Neural Networks

Guest, O. & Martin, A. E. (2023). On Logical Inference over Brains, Behaviour, and Artificial Neural Networks. Computational Brain & Behavior. https://doi.org/10.1007/s42113-022-00166-x

In this work, we talk about how scientists reason about artificial neural networks' apparent modelling successes (in fields such as cognitive computational neuroscience: CCN; which talk about brain areas specifically like the inferior temporal gyrus: IT). We elaborate on how we as scientists can be negatively influenced by the Turing test; Guest and Martin (2023, p. 215):

Based on the discovery of such correlations over correlations, CCN proposes a theoretical position about the contents of brain states, e.g., “our IT-geometry-supervised deep representation fully explains our IT data” (emphasis added; Khaligh-Razavi & Kriegeskorte, 2014, p. 24), or that using ANNs “explains brain activity deeper in the brain [and such models] provide a suitable computational basis for visual processing in the brain, allowing to decode feed-forward representations in the visual brain.” (emphasis added; Ramakrishnan et al., 2015, p. 371). Furthermore, some propose that “[t]o the computational neuroscientist, ANNs are theoretical vehicles that aid in the understanding of neural information processing” (emphasis added; van Gerven & Bohte, 2017, p. 1). This betrays very strong assumptions in CCN about the explanatory virtue of correlational results and how metatheoretical inferences are drawn. This specific belief system involving correlation could be seen as the result of importing the Turing test (Turing, 1950) from computer science and philosophy of mind to CCN, without bearing in mind that the Turing test is not per se useful for furthering a mechanistic understanding, but rather for elucidating functional roles. The Turing test, in its most abstract form, evaluates if an engineered system, like a chatbot, can converse in such a way as to pass as human, i.e., can an algorithm convince a human judge that it is indistinguishable from a human? If yes, then the machine is said to have passed the Turing test and on that — functional, correlational, but not mechanistic — basis be human-like. The insights from the engineering-oriented Turing test, can lead CCN astray if we do not methodically take into account the principle of multiple realizability (Fodor & Pylyshyn, 1988; Putnam, 1967; Quine, 1951): dramatically different substrates, implementations, mechanisms, can nonetheless perform the same input-output mappings (i.e., can correlate with each other without being otherwise “the same”).

Figure from Tekhnika Molodezhi (Apr. 1955, p.9).

Figure from Tekhnika Molodezhi (Apr. 1955, p.8).

Further down in the same paper, we return to summarise how the Turing test can affect our reasoning; Guest and Martin (2023, p. 224):

Misapplication of such metatheoretical logic, as we have expounded on herein, contributes to overpromising and underdelivering, impeding the progress of CCN as much as it does the fields that touch on AI generally. We do not argue that ANNs do not make highly useful models within CCN and the neuro- and cognitive sciences more broadly. Although “machine learning provides us with ever-increasing levels of performance, accompanied by a parallel rise in opaqueness” (Barak, 2017, p. 5), we do not believe using machine learning this way is the only source of lack of transparency and of lack of “open theorizing” (Guest & Martin, 2021) within CCN. Interdisciplines, like cognitive science, strive to properly allow for the scientific exchange of ideas and methods within and between their constituent participating disciplines. We wish to facilitate dialogue on how to theorize usefully when importing ideas to CCN from other related fields. An example of a maladaptively imported idea is that of the Turing test (Turing, 1950), which involves understanding and contextualizing the principle of multiple realizability. The Turing test sets out to differentiate the human(-like machine) from an algorithm, essentially a chatbot. Through behavioral probing, e.g., asking questions in natural language, the person performing the test attempts to ascertain if the agent (machine or person) answering is responding meaningfully differently to a person. If the machine can “trick” us into thinking it is a human, it is said to have passed the Turing test. The Turing test is very useful if we want to engineer algorithms that can exchange details with people seamlessly. However, if we take this test and use it to infer more than perhaps Alan Turing intended, that the machine indeed is a person (our P → Q), we have slipped into affirming the consequent and false analogy.

As we have shown, it is not unusual if formally treated to discern or derive fallacies in the CCN literature such as cum hoc ergo propter hoc (i.e., with the fact, therefore because of the fact, or “correlation does not imply causation”), begging the question, confirmation bias, false analogy — the root of these informal fallacies is the formal fallacy of affirming the consequent. The Turing test and similar functional role-based analogies allow us to stumble into a formal fallacy if we stray far from engineering systems and towards understanding human cognition. Ultimately, the lack of attention to the high potential for (mis)application of formal logic in CCN betrays its current theoretical underdevelopment. This is the case regardless of what the reasons are for this lack of (meta)theoretical aptitude. If we accept that a “theory is a scientific proposition [...] that introduces causal relations with the aim of describing, explaining, and/or predicting a set of phenomena” (Guest & Martin, 2021, p. 794), then the field-level theory that much of CCN work is based on has logical inconsistencies, namely manifesting as the formal fallacy of affirming the consequent. The ability to critically evaluate this was granted to us in part by the metatheoretical calculus, the formal model of the discourse, we built herein.

What Makes a Good Theory, and How Do We Make a Theory Good?

Guest, O. (2024). What Makes a Good Theory, and How Do We Make a Theory Good?. Computational Brain & Behavior. https://doi.org/10.1007/s42113-023-00193-2

In this paper I touch on the antecendent and interconnected theoretical frameworks that underlie contemporary AI approaches, including artificial neural networks; Guest (2024, p. 518):

A behaviourist framework is genealogically linked to, for example, reinforcement learning and a specific take on the field of (comparative) psychology, inter alia. It is also related to the perspective on human (and other) cognitions granted by the Turing test (cf. Guest and Martin, 2023; Erscoi et al., 2023). In many ways, a metatheoretical calculus for reinforcement learning as used in AI (e.g. Sutton et al., 1998), if it took into account genealogy, would have to relate all these elements, such as answering when, how, and why behaviourism is seen as unethical (e.g. applied to treatment or education, Kirkham, 2017; Kumar and Kumar, 2019), seen as not a useful framing of what is attempted to be scientifically understood (Guest & Martin, 2023), or seen as both (Erscoi et al., 2023). These parts of analysing a modern AI reinforcement learning paradigm only drop into place when seen through a genealogical lens.

"The German army and navy widely used Enigma machines to encrypt radio messages to troops in the field or U-boats at sea. Decrypting the transmissions sent using the Kriegsmarine’s four-rotor version proved especially difficult and time consuming for the Allies, but the U.S. Navy’s high-speed bombe (shown here) greatly facilitated the process." (Sears, 2016)

Critical AI

Mon, 15 Sep 2025 00:00:00 +0000

Critical
AI

The Analytical Engine has no pretensions whatever to originate any thing. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths. Its province is to assist us in making available what we are already acquainted with.

— Ada Lovelace (1843)

She was talking about the Analytical Engine, which was the first automatic calculating machine, a fragment of which was built in 1871 and is depicted here. Read more of her writings in Ada, the Enchantress of Numbers: Prophet of the Computer Age, a Pathway to the 21st Century by Betty Alexandra Toole.

what is critical AI literacy?

about

On this page are some resources for Critical AI Literacy (CAIL) from my perspective. Also see: the project homepage and this press release on our work.

As we say here, CAIL is:

an umbrella for all the prerequisite knowledge required to have an expert-level critical perspective, such as to tell apart nonsense hype from true theoretical computer scientific claims (see our project website). For example, the idea that human-like systems are a sensible or possible goal is the result of circular reasoning and anthropomorphism. Such kinds of realisations are possible only when one is educated on the principles behind AI that stem from the intersection of computer and cognitive science, but cannot be learned if interference from the technology industry is unimpeded. Unarguably, rejection of this nonsense is also possible through other means, but in our context our AI students and colleagues are often already ensnared by uncritical computationalist ideology. We have the expertise to fix that, but not always the institutional support.

CAIL also has the goal to repatriate university technological infrastructure and protect our students and selves from deskilling — as we explain here:

Within just a few years, AI has turbocharged the spread of bullshit and falsehoods. It is not able to produce actual, qualitative academic work, despite the claims of some in the AI industry. As researchers, as universities, we should be clearer about pushing back against these false claims by the AI industry. We are told that AI is inevitable, that we must adapt or be left behind. But universities are not tech companies. Our role is to foster critical thinking, not to follow industry trends uncritically.

See more at — and please cite — the preprint here:

In general, have a look at the various resources here, such as the blog posts and preprints and published papers to understand our various perspectives.

Camille Flammarion, The wonders of optics (1871)

deeper dives by me

I have created some longer-form pages on some issues that seem to keep coming up and centered on my perspective and work:

Turing test — a collection of extracts from my work and from critical and gendered angles on this seemingly omnipresent undercurrent to modern AI thinking.
We've been here before! — extracts and related points that continue to reappear when we contend with the rhetoric the AI industry throws at us.
Why does cheating matter? — if you have ever felt pressure as a student to use AI, this might be for you.

For the full list see here.

video talks & interviews

Raging Against the Machine: A Popular School Against AI (2026). AI, Knowledge Work and Education: Hagen Blix, Victor Gomez, Olivia Guest, & Iris van Rooij. https://youtu.be/ZLukICD_Lek
Millennials Are Killing Capitalism Live! (2025). The Tech Industry's "AI" Play for Education Dollars with Dwayne Monroe. https://www.youtube.com/live/sey7TS4JV68
Organisers: Leonard Block Santos, Barbara Müller, & Iris van Rooij (2025). Critical AI Literacy: Empowering people to resist hype and harms in the age of AI — Symposium 2025. https://www.youtube.com/watch?v=Fxyg2UMq_no
The Data Fix by Dr Mél Hogan (2025). Hollowed, with Olivia Guest, Iris van Rooij and Andrea Reyes Elizondo. https://shows.acast.com/63997541ed122a001195e286/69a71876db942e85cc86f00c
Interviewers: Kent Anderson & Joy Moore (2025). Safeguarding Science from AI: An Interview with Olivia Guest and Iris van Rooij. https://www.youtube.com/watch?v=p9w0FiHo1RU

Maria Sibylla Merian; Erucarum Ortus (1679–1717)

blog posts, news, & opinion pieces

Klee, M. (2025). AI Is Inventing Academic Papers That Don’t Exist — And They’re Being Cited in Real Journals. Rolling Stone. https://www.rollingstone.com/culture/culture-features/ai-chatbot-journal-research-fake-citations-1235485484/
Glick, M. (2025). AI Might Not Harm Us in the Way You Think. Nautilus. https://nautil.us/ai-might-not-harm-us-in-the-way-you-think-1248498/
Singer, N. (2025). Big Tech Makes Cal State Its A.I. Training Ground. New York Times. https://www.nytimes.com/2025/10/26/technology/cal-state-ai-amazon-openai.html
Walraven, J. (2025). Belgian AI scientists resist the use of AI in academia. Apache. https://apache.be/2025/10/24/belgian-ai-scientists-resist-use-ai-academia
Pearson, H. (2025). Universities are embracing AI: will students get smarter or stop thinking?. Nature. https://www.nature.com/articles/d41586-025-03340-w
Guest, O. & van Rooij, I. (2025). AI Is Hollowing Out Higher Education. https://www.project-syndicate.org/commentary/ai-will-not-save-higher-education-but-may-destroy-it-by-olivia-guest-and-iris-van-rooij-2025-10
Crawley, K. (2025). Anti Gen AI Heroes: Dr. Olivia Guest's Dire Warning to Academia. https://stopgenai.com/anti-gen-ai-heroes-dr-olivia-guests-dire-warning-to-academia/
Guest, O., van Rooij, I, Müller, B., & Suarez, M. (2025). No AI Gods, No AI Masters. https://www.civicsoftechnology.org/blog/no-ai-gods-no-ai-masters
Merchant, B. (2025). Cognitive scientists and AI researchers make a forceful call to reject "uncritical adoption" of AI in academia. https://www.bloodinthemachine.com/p/cognitive-scientists-and-ai-researchers/
van Rooij, I. (2025). AI slop and the destruction of knowledge. https://doi.org/10.5281/zenodo.16905560
Kilgore, W. (2025). Can We Build AI Therapy Chatbots That Help Without Harming People?. Forbes. https://www.forbes.com/sites/weskilgore/2025/08/01/can-we-build-ai-therapy-chatbots-that-help-without-harming-people/
Huckins, G. (2025). How scientists are trying to use AI to unlock the human mind. Understanding the mind is hard. Understanding AI isn't much easier. MIT Technology Review. https://www.technologyreview.com/2025/07/08/1119777/scientists-use-ai-unlock-human-mind/
Suarez, M., Müller, B.,Guest, O., & van Rooij, I. (2025). Critical AI Literacy: Beyond hegemonic perspectives on sustainability. https://doi.org/10.5281/zenodo.15677839
van Rooij, I. & Guest, O. (2024). Don't believe the hype: AGI is far from inevitable. https://www.ru.nl/en/research/research-news/dont-believe-the-hype-agi-is-far-from-inevitable

research

Ray, I. & Guest, O. (2026). Contra Literacy-Laundering: Mechanistic Critical AI Literacy. Zenodo. https://doi.org/10.5281/zenodo.21222978
van Rooij, I. & Guest, O. (2026). Combining Psychology with Artificial Intelligence: What could possibly go wrong?. Current Directions in Psychological Science. https://doi.org/10.1177/09637214261438379
Guest, O. (2026). Models. Zenodo. https://doi.org/10.5281/zenodo.20639860
Guest, O., Suarez, M., Müller, B., et al. (2026). Against the Uncritical Adoption of 'AI' Technologies in Academia. Digital Culture & Education. https://doi.org/10.5281/zenodo.20082828
Guest, O., Nuñez Hernández, N. A., & Blokpoel, M. (2026). Understanding Artificial Neural Networks: Mysterianism about Known Mechanism is Mysticism. Zenodo. https://doi.org/10.5281/zenodo.20071869
Guest, O. (2026). What Does 'Human-Centred AI' Mean?. Behavioral Sciences. https://doi.org/10.3390/bs16040583
Guest, O. & Martin, A. E. (2026). A Metatheory of Classical and Modern Connectionism. Psychological Review. https://doi.org/10.1037/rev0000591
Guest, O., Suarez, M., & van Rooij, I. (2025). Towards Critical Artificial Intelligence Literacies. Zenodo. https://doi.org/10.5281/zenodo.17786243
Guest, O. & van Rooij, I. (2025). Critical Artificial Intelligence Literacy for Psychologists. PsyArXiv. https://doi.org/10.31234/osf.io/dkrgj_v1
Forbes, S. H. & Guest, O. (2025). To Improve Literacy, Improve Equality in Education, Not Large Language Models. Cognitive Science. https://doi.org/10.1111/cogs.70058
van Rooij, I., Guest, O., Adolfi, F. G., de Haan, R., Kolokolova, A., & Rich, P. (2024). Reclaiming AI as a theoretical tool for cognitive science. Computational Brain & Behavior. https://doi.org/10.1007/s42113-024-00217-5
van der Gun, L. & Guest, O. (2024). Artificial Intelligence: Panacea or Non-intentional Dehumanisation?. Journal of Human-Technology Relations. https://doi.org/10.59490/jhtr.2024.2.7272
Guest, O. & Martin, A. E. (2023). On Logical Inference over Brains, Behaviour, and Artificial Neural Networks. Computational Brain & Behavior. https://doi.org/10.1007/s42113-022-00166-x
Erscoi, L., Kleinherenbrink, A., & Guest, O. (2023). Pygmalion Displacement: When Humanising AI Dehumanises Women. SocArXiv. https://doi.org/10.31235/osf.io/jqxb6
Birhane, A. & Guest, O. (2021). Towards Decolonising Computational Sciences. Women, Gender & Research. https://doi.org/10.7146/kkf.v29i2.124899
Barlas, P., Kyriakou, K., Guest, O., Kleanthous, S., & Otterbacher, J. (2021). To "See" is to Stereotype: Image Tagging Algorithms, Gender Recognition, and the Accuracy-Fairness Trade-off. Proceedings of the ACM on Human-Computer Interaction. https://doi.org/10.1145/3432931

featured research

Pygmalion Displacement: When Humanising AI Dehumanises Women

Erscoi, L., Kleinherenbrink, A., & Guest, O. (2023). Pygmalion Displacement: When Humanising AI Dehumanises Women. SocArXiv. https://doi.org/10.31235/osf.io/jqxb6

"This figure depicts, in the style of a printed circuit board, a timeline of events (many with related images) that involve Pygmalion displacement in one form or another. On the left [are] fictional instances of women’s and automata’s intertwined identities; from 1901 at the top to today’s film and television series at the bottom. On the right [are] historical individuals or artefacts." (Erscoi et al., 2023, figure 2)

Abstract: We use the myth of Pygmalion as a lens to investigate and frame the relationship between women and artificial intelligence (AI). Pygmalion was a legendary ancient king of Cyprus and sculptor. Having been repulsed by women, he used his skills to create a statue, which was imbued with life by the goddess Aphrodite. This can be seen as one of the primordial AI-like myths, wherein humanity creates intelligent life-like self-images to reproduce or replace ourselves. In addition, the myth prefigures historical and present gendered dynamics within the field of AI and between AI and society at large. Throughout history, the theme of women being replaced by inanimate objects (e.g. automata, algorithms) has been repeated, and continues to repeat in contemporary AI technologies. However, this socially detrimental pattern in technology — what we dub Pygmalion displacement — is often overlooked, whether due to naive excitement about new developments, or due to an unacknowledged sexist history of the field itself. As we demonstrate herein, Pygmalion displacement prefigures heavily, but in an unacknowledged way, in the original Turing test, the imitation game: a central thought experiment, foundational to AI. With women, and the feminine generally, being both dislocated and erased from and by technology, AI is and has been (presented as) created mainly by privileged men, subserving capitalist patriarchal ends. This poses serious dangers to women and other marginalised people. By tracing the historical and ongoing entwinement of femininity (from a patriarchal perspective) and AI, we aim to understand, make visible, and start a dialogue on the ways in which AI harms women.

"The series of questions that comprise the Pygmalion lens. For a given technosocial relationship between AI and people,if one or more of the answers are “Yes”, then we can conclude that (an aspect of) Pygmalion displacement is occurring, which is damaging to women, and the feminised broadly construed. If not, then the lens does not apply, and we remain agnostic as to gendered or other harm within this framework. Our proverbial lens should be taken inter alia to be akin to an optical lens which allows for the eye to see more details than when naked, as depicted in the line drawing below." (Guest et al., 2023, table 1)

	Pygmalion Lens
1)	Feminised form: Is the AI, by its (default or exclusive) external characteristics, portraying a hegemonically feminine character?	Yes/No
2)	Whitened form: Is the AI, by its (default or exclusive) external characteristics, portraying a character that is inherently white (supremacist), Western, Eurocentric, etc.?	Yes/No
3)	Dislocation from work: Does the AI displace women from a role or occupation, or people in general from a role or occupation that tends to be (coded as) women's work?	Yes/No
4)	Humanisation via feminisation: Are the AI's claims to intelligence, human-likeness or personhood contingent on stereotypical feminine traits or behaviours?	Yes/No
5)	Competition with women: Is the AI pit (rhetorically or otherwise) against women in ways that favour it, and which are harmful to women?	Yes/No
6)	Diminishment via false equivalence: Does the AI facilitate a rhetoric that deems women as not having full intellectual abilities, or as otherwise less deserving of personhood?	Yes/No
7)	Obfuscation of diversity: Does the AI, through displacement of specific groups of people, “neutralise” (i.e., whiten, masculinise) a role, vocation, or skill?	Yes/No
8)	Robot rights: Do the users and/or creators of the AI grant it (aspects of) legal personhood or human(-like) rights?	Yes/No
9)	Social bonding: Do the users and/or creators of the AI develop interpersonal-like relationships with it?	Yes/No
10)	Psychological service: Does the AI function to subserve and enhance the egos of its creators and/or users?	Yes/No

Towards Critical Artificial Intelligence Literacies

Guest, O., Suarez, M., & van Rooij, I. (2025). Towards Critical Artificial Intelligence Literacies. Zenodo. https://doi.org/10.5281/zenodo.17786243

Abstract: 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.

"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." (Guest et al., 2025, figure 1)

Against the Uncritical Adoption of 'AI' Technologies in Academia

Guest, O., Suarez, M., Müller, B., et al. (2026). Against the Uncritical Adoption of 'AI' Technologies in Academia. Digital Culture & Education. https://doi.org/10.5281/zenodo.20082828

"A cartoon set theoretic view on various terms (see Table 1) used when discussing the superset AI" (Guest et al., 2025, figure 1)

Abstract: Artificial intelligence (AI) companies and their rhetoric infringe on academia in harmful ways, mirroring past uncritical acceptance of industry logics, such as those of tobacco and petroleum. In this position piece, we tease apart and explain why phrases like 'generative AI' impede scholarly discussion because by design these expressions are used to dazzle and sidestep scrutiny. Furthermore, we contend with the AI industry's logics to enable rejecting frames such as: that we must embrace the future, that this hype cycle is unique, that anthropomorphism and circular reasoning hold water when discussing AI systems, and that students are now all cheating or all need to use AI. To these ends, we expound on why universities must take their role seriously to a) counter the AI industry's marketing, hype, and harm; and to b) safeguard higher education, critical thinking, expertise, academic freedom, and scientific integrity. For each point we raise, we include pointers to relevant work to further inform and convince our colleagues.

"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." (Guest et al., 2025, table 1)

Term	Description	Resources
Artificial Intelligence (AI)	The phrase 'artificial intelligence' was coined by McCarthy et al. (1955) in the context of proposing a summer workshop at Dartmouth College in 1956. They assumed significant progress could be made on making machines think like people. In the present, AI has no fixed meaning. It can be anything from a field of study to a piece of software.	Avraamidou (2024), Bender and Hanna (2025), Bloomfield (1987), Boden (2006), Brennan et al. (2025), Crawford (2021), Guest (2025), Hao (2025), McCorduck (2004), McQuillan (2022), Monett (2021), Vallor (2024), and van Rooij, Guest, et al. (2024).
Artificial neural network (ANN)	First proposed in McCulloch and Pitts (1943), it is a mathematical model, comprised of interconnected banks of units that perform matrix multiplication and non-linear functions. These statistical models are exposed to data (input-output pairs) that they aim to reproduce. While held to be inspired by the brain, such claims are tenuous or misleading.	Abraham (2002), Bishop (2021), Boden (2006), Dhaliwal et al. (2024), Guest and Martin (2023, 2025a), Hamilton (1998), Stinson (2018, 2020), and Wilson (2016).
Chatbot	An engineered system that appears to converse with the user using text or voice. Speech synthesis goes back hundreds of years (Dudley 1939; Gold 1990; Schroeder 1966) and Weizenbaum's (1966) ELIZA is considered the first chatbot (Dillon 2020). Modern versions can contain ANNs in addition to hardcoded rules.	Bates (2025), Dillon (2020), Elder (2022), Erscoi et al. (2023), Schlesinger et al. (2018), Strengers et al. (2024), Turkle (1984), and Turkle et al. (2006).
ChatGPT	A proprietary closed source chatbot created by OpenAI. The for-profit company OpenAI has been steeped in hype from inception. It does not provide source code for most of its models, violating open science principles for academic users. OpenAI reported $5 billion in losses in 2024 (Reuters 2025), and has received $13 billion from Microsoft (Levine 2024).	Andhov (2025), Birhane and Raji (2022), Dupré (2025), Gent (2024), M. T. Hicks et al. (2024), Hill (2025), Jackson (2024), Kapoor et al. (2024), Liesenfeld, Lopez, et al. (2023), Mirowski (2023), Perrigo (2023), Titus (2024), and Widder et al. (2024).
Generative model	A specification on the type of statistical distribution modelled; typically contrasted with discriminative model. ANNs can be generative (e.g. Boltzmann machines) or discriminative (e.g. convolutional neural networks used for classifying images). In the context of generative AI or generative pre-trained transformer (GPT), this phrase is used inconsistently.	Efron (1975), Jebara (2004), Mitchell (1997), Ng and Jordan (2001), and Xue and Titterington (2008).
Large language model (LLM)	A model that captures some aspect of language, with the term 'large' denoting that the number of parameters exceed a certain threshold. Modern chatbots are often LLMs, which use ANNs, along with a graphical interface so that users can input so-called text 'prompts.' LLMs can be generative, discriminative, or neither.	Bender, Gebru, et al. (2021), Birhane and McGann (2024),Dentella et al. (2023, 2024), Leivada, Dentella, et al. (2024), Leivada, Günther, et al. (2024), Luitse and Denkena (2021), Shojaee et al. (2025a), Villalobos et al. (2024), and Wang et al. (2024)

Critical Artificial Intelligence Literacy for Psychologists

Guest, O. & van Rooij, I. (2025). Critical Artificial Intelligence Literacy for Psychologists. PsyArXiv. https://doi.org/10.31234/osf.io/dkrgj_v1

Abstract: Psychologists — from computational modellers to social and personality researchers to cognitive neuroscientists and from experimentalists to methodologists to theoreticians — can fall prey to exaggerated claims about artificial intelligence (AI). In social psychology, as in psychology generally, we see arguments taken at face value for: a) the displacement of experimental participants with opaque AI products; the outsourcing of b) programming, c) writing, and even d) scientific theorising to such models; and the notion that e) human-technology interactions could be on the same footing as human-human (e.g., client-therapist, student-teacher, patient-doctor, friendship, or romantic) relationships. But if our colleagues are, accidentally or otherwise, promoting such ideas in exchange for salary, grants, or citations, how are we as academic psychologists meant to react? Formal models, from statistics and computational methods broadly, have a potential obfuscatory power that is weaponisable, laying serious traps for the uncritical adopters, with even the term `AI' having murky referents. Herein, we concretise the term AI and counter the five related proposals above — from the clearly insidious to those whose ethical neutrality is skin-deep and whose functionality is a mirage. Ultimately, contemporary AI is research misconduct.

"Core reasoning issues (first column), which we name after the relevant numbered section, are characterised using a plausible quote. In the second column are responses per row; also see the named section for further reading, context, and explanations." (Guest & van Rooij, 2025, table 1)

Uncritical Statement	Possible Response
Lies, Damned Lies, and Statistics "AI products are outside my expertise but I think it is useful to deploy them."	As a matter of fact these products are statistical models, akin to logistic regression, which all psychologists even undergraduate students are required to have a familiarity with. Additionally, it is required to know the differences between models used to perform statistical inference and those that are models of cognition. As is knowing basic open science principles. Therefore, it should come as no shock that assuming the mantle of the non-expert here is inappropriate, and in fact may even be a form of QRP to abandon critical thinking.
Displacement of Participants "I can use AI instead of participants to perform tasks and generate data."	The provenance of the data used in these models indicates it is not ethically sourced, falling below standards for our discipline, involving sweatshop labour and no consent for private data used in experiments. The output can contain direct original input data (i.e. double dipping), but smoothed to remove outliers, conform to our pre-existing ideas of what it should look like (data fabrication), and all-round irreplicable. Psychology is meant to study humans, not patterns at the output of biased statistical models.
Outsourcing Programming to Companies "I can use AI for programming experimental paradigms and statistical analyses."	This is an example of the field’s backsliding from adopting open science and programming skills. No formal specification will be given for code generated from a corporate-owned opaque model. The psychologist now has no reason to learn how to engineer software, and disturbingly might as well switch back to propriety software like SPSS which at least has documentation and explicit versions. Code at the output will be plagiarised, making it time-consuming to check compliance with our needs than if we wrote the code ourselves, and violating openness.
Ghostwriter in the Machine "I can use AI for understanding the literature and for scholarly writing."	This practice implicates a swathe of issues akin to automating the paper mill. First, the literature is screened by corporations, which have every reason to control the output of the model to suit their needs or minimally to ignore output issues, such as sexism. Second, the fabrication of non-existent citations which makes claims worse than baseless because they appear supported by prior work. Third, the dislocation of text from the literature since no provenance can be established, resulting in plagiarism.
The End of Scientific Theory "I can outsource verbal theorising to AI or use it as a formal cognitive model."	This not only adds to the dislocation of work from its evidential and historical basis, but also it impedes our theorising about phenomena and systems under study. In this context, we are interested in human-understandable theory and theory-based models, not statistical models which provide only a representation of the data. Scientific theories and models are only useful if we, the scientists who build and use them, understand them in deep ways and they connect transparently to research questions. AI product use is absconding scientific duty.
Equivocation of Human-Human & Human-AI "I can study people using chatbots as if they are socially interacting."	Seeing client-therapist, student-teacher, patient-doctor, friendship, or romantic relationships as equivalent to those between people and artifacts is both a form of dehumanisation and a hollowing out of the target of study in social psychology: the relationship between people and other people. It is important to study the relations between humanity and machines and the social interactions mediated through technology — but to place interactions with chatbots in the same category as those between people assumes and risks too much.

allies & resources

Camille Flammarion, The wonders of optics (1871)

Here are some related academics' and scholars' websites (feel free to contact me to add more):

Refusing GenAI in Writing Studies: A Quickstart Guide, by Jennifer Sano-Franchini, Megan McIntyre, & Maggie Fernandes.
AGAINST AI, by Anna Kornbluh, Krista Muratore, & Eric Hayot.
Computational Impacts: Tech Industry Critique Without Tech-bro-ism, by Dwayne Monroe.
Stop Gen AI, by Kim Crawley.
I would be so ashamed to use generative AI, here’s why, by Sarah Winnicki.
Against Generative AI, by Cate Denial.
Resource: Resisting AI and Big Tech by The Climate Justice Universities Union.

activism & posters

Open Letters

Colleagues and I have written and published: Open Letter: Stop the Uncritical Adoption of AI Technologies in Academia. Also there are versions in Brazilian Portuguese, Dutch, French, German, Italian, and Spanish.

Please consider adapting the letter for your employer; here are some allies' efforts inspired by our letter:

An open letter from educators who refuse the call to adopt GenAI in education, by Melanie Dusseau and Miriam Reynoldson.
Stop AI in Malden Schools — against AI use Malden, Greater Boston, MA, USA.
Sabrina Mittermeier created a German version of our letter: Gegen die unkritische Anwendung und Implementierung sog. Künstlicher Intelligenz (KI) in der deutschen Wissenschaft und im Hochschulalltag.
Carta abierta: Frenemos la incorporación acrítica de las tecnologías de IA en la Academia — translation of our Open Letter into Spanish by Informática Libre MX.
Lettre ouverte : Arrêtons l’adoption naïve des technologies de l’IA dans le milieu universitaire — translation of our Open Letter into French by Antonio E. Porreca and Sven D.
Lettera aperta: “Basta con l’adozione acritica delle tecnologie di intelligenza artificiale nelle università” — translation of our Open Letter into Italian by Daniela Tafani, Maria Chiara Pievatolo, and Antonio E. Porreca.
Carta aberta: Pare com a adoção acrítica de tecnologias de IA na academia — translation of our Open Letter into Brazilian Portuguese by Adolfo Neto.

have you considered not using AI?

I made some posters, in the style of this website, to use to attract attention to more critical thinking about AI: Download them as PDFs here. The QR code points to: