Turing Tests and ChatGPT’s Sleight of Hand

 

One of the many benchmarks for AI is the “Turing test,” Alan Turing’s adaptation of the “imitation game” where an interrogator must decide which of two respondents is a computer. It is, as many have pointed out, a strangely indirect test, one that depends on the credulity of the human interrogator and the capacity of the machine to deceive (Wooldridge 2020). Will they believe the computer? And will the computer be a good enough liar? As Pantsar (2025) comments, “For the machine to pass the test, it needs to impersonate a human successfully enough to fool the interrogator. But this is puzzling in the wide context of intelligence ascriptions. Why would intelligence be connected to a form of deception?”

 

On the one hand, measuring AI through its deceptive power has the benefit of avoiding the idiocy of attempting to establish a measure of intelligence, a task deeply imbricated in racial eugenics (Bender and Hanna 2025; Wooldridge 2020). On the other, generative AI applications seem to have been developed with deception in mind–deception from all parties. The application designers want to present outputs as just as good (if not better) than the products of human work. And the humans that utilize generative AI often seek to present these outputs as their own work. And even though ethical practice demands that we acknowledge the utilization of AI, the goal is that people consuming AI material will be unable to tell the difference or to mark where the machine ends and the human begins. So even though Turing tests may be a poor measure of machine “intelligence,” they seem to fit the moment.

 

But there’s another part of the Turing Test that is especially relevant: the organization of the “imitation game” itself. Let’s go back to Turing’s 1950 article. As Sterrett points out in a series of articles, there are two games described in Turing’s landmark essay, an "Original Imitation Game” and a “Standard Turing Test” (the terms are Sterrett’s) (Sterrett 2020: 469). The first one describes two rooms, one for the interrogator, and the other for a man and woman, who communicate with the interrogator via a teletype.

In order that tones of voice may not help the interrogator the answers should be written, or better still, typewritten. The ideal arrangement is to have a teleprinter communicating between the two rooms. Alternatively the question and answers can be repeated by an intermediary. 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. We now ask the question, ' What will happen when a machine takes the part of A 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? (Turing 1950: 434)

While people have certainly looked at the gender component here, most of the attention has been on the game itself and its overall intention: deception (Patterson et al 2018). I want to focus on the whole arrangement itself.

 

The game “works” through series of limitations. The interrogator can’t go into the other room, nor can the human burst into the interrogator’s room. The interrogator is also prevented from hearing them. Communication, Turing tells us, is best accomplished through teletype. Of course, one would need such an arrangement with a computer. Yet, game rules aren’t just arbitrary, and Turing’s test tells us much about social hierarchies and workplace organization. By the time Turing wrote his test, the office had taken its contemporary form–a spatialized organizational chart made up of offices communicating with each other through a variety of technologies: mail, telephone, teletype, pneumatic tube. Inter-office communication is also rendered through a variety of intermediaries and, as “organization man” develops, cybernetic systems of communication and decision-making come to dominate managerial processes (Whyte 1956).

 

In other words, Turing’s “imitation game” rules are a description of contemporary work. Turing’s readers would have easily conjured up an image of an organization where people don’t communicate face-to-face  with each other. This has only become more marked in the intervening decades, where people are duped into long-term relationships without ever meeting their con-artist. More to the point, we are more and more called upon to judge between human- and computer outputs, a task in which humans have proven occasionally successful. But perhaps only under special conditions. 

 

One of the theses that I have suggested (both on this blog and in published work) is that the triumph of automation, algorithm and AI are initially built not upon technological change, but upon behavioral and cognitive change. Before business owners can begin to replace workers with algorithmic process and generative AI, before, in other words, those outputs can be accepted as “just as good” or “better” than human work, humans and their labor must be constrained, delimited and “de-skilled.” And more than this - everyone has to be convinced that these narrowly defined outputs are as good as we humans get. With the Turing test, human communication is reduced to lines on a teletype. With algorithmic analysis, applying to a job, getting an apartment or reading a mammogram are reduced to scores that can be ranked, patterns that can be assigned probability, etc.

 

As I explained in an earlier essay, this process of labor alienation unfolds across several steps. First, human labor is parsed out into a series of constituent functions, consistent with the Taylorization that transformed factory labor. Second, those functions are reconceptualized as algorithms: steps in a chain of operations that proceed from inputs to outputs. These might be scripts for telemarketing, decision trees for insurance claims, procedures for reporting inventory loss, etc. Next, workers are confined to those algorithmic choices, and part of the de-skilling process means penalizing workers for deviating from the script. Finally, automation (AI applications and AI-infused platforms) replaces workers. 

 

 

 

[Produced through ChatGPT]

 

The important part here is the human side of the transformation. Humanity must be reduced, and people must be convinced that algorithmic processes are interchangeable with the etiolated human. In other words, the Turing test only works if we stay in our room, if we don’t shout, if we don’t bang on the walls with our fists. And there are 2 levels of deception - one in convincing (or forcing) people to reduce themselves to narrowly defined outputs, the other in misrecognizing those algorithmic products as the total of human possibility.

 

Does generative AI involve a similar series of reductions? Of course it does. In all kinds of professions, our labor has been reduced to the production of “content”: bland and repetitive text, stereotypical images, boilerplate scripts. People produce like this in accordance with late capitalism. The only way, after all, to monetize that YouTube channel is to constantly update with new material. And that new material must fit the algorithmic desires embedded in the platform. So: you start making a lot of content, and you align it with the algorithm. The next step is, of course, to replace you with generative content. ChatGPT may have come as somewhat of a shock to us educators in 2022, but it must have come to no surprise for platform laborers, who have been human generative AI for several years now.

 

References

 

Bender, Emily and Alex Hanna (2025). The AI Con. NY: Harper.

 

Collins, Samuel Gerald (2018). Welcome to Robocracy. Anthropology of Work Review.

 

Pantsar, Markus (2024). “Intelligence is not deception.” AI & Society.

 

Patterson, W., Boboye, J., Hall, S., Hornbuckle, M. (2018). The Gender Turing Test. In: Nicholson, D. (eds) Advances in Human Factors in Cybersecurity. AHFE 2017. Advances in Intelligent Systems and Computing, vol 593. Springer, Cham. https://doi.org/10.1007/978-3-319-60585-2_26

Sterrett SG (2020) The Genius of the “Original imitation Game” test. Mind Mach 30(4):469–486. https:// doi. org/ 10. 1007/S11023-020-09543-6

Turing, Alan M.(1950). “Computing machinery and intelligence”. Mind Q. Rev. Psychol. Philos. LIX(236), 433–460.

 

Whyte, William H. (1956). The Organization Man. NY: Simon and Schuster.

 

Wooldridge, Michael (2020). A Brief History of Artificial Intelligence. NY: Flatiron Books.

 

 

AAA Abstract Proposal: Summerland, Otherwise and and the Ghosts of Alternative Futures: the Limits of Multimodality in Anthropology and Spiritualism

 

As anthropologists work with collaborators in evoking alternatives to capitalist fascisms, they increasingly engage multimodal registers; games, design, graphic novels and soundscapes join film and text in innovative work that seeks to ground worlding in sensorial engagement and haptic experience. Here, the multimodal can support the emancipatory politics of communities where anthropologists work. But what of the politics of multimodal? Is there anything inherently emancipatory in the engagement with diverse platforms? In order to problematize the multimodal, this paper explores another moment in multimodal evocations of alternative futures–Spiritualism in the late 19th century. While “spirit rapping” may have been the first volley in the explosion of Spiritualist practice, the movement soon incorporated writing, drawing, sounds, photographs and multiple objects into its evocations of a “Beautiful Beyond” that represented not only the afterlife, but the utopian promise for humanity itself. For Alfred Russel Wallace, this was the “new bench of anthropology” that would engage him for the rest of his career–much to the chagrin of his skeptical contemporaries. In many ways, though, the multimodal proved inadequate to the task of evoking the hereafter, with these diverse media platforms dragged down by their obstinate corporeality and their quotidian fabrication. While our concerns are different today, comparing the fate of spirit photography, object levitation and apporting to the multimodal anthropologies of today can nevertheless help us explore the limits of multimodality in our similarly anxious age. Can the spirits we evoke be more effective than those nineteenth century ghosts? 

Multimodal Interrogations of Anthropologically Unintended Media - Video link

Matt Durington and I had a wonderful time giving a talk at UBC Okanagan. Thanks to Dr. Fiona McDonald and the Collaborative and Experimental Ethnography Lab. 

 

Multimodal interrogations video link 

Multimodal Anthropology Talk - Tuesday, March 25 @ 3 pm EST

 

NPS Ethnographic Report on Hampton Mansion National Historic Site

An article in the Baltimore Banner by Rona Kobell (https://www.thebaltimorebanner.com/community/local-news/hampton-national-historic-site-east-towson-URF5WGM5TZCAZMJAZBGRU7JYMY/) reminded me about the precarious state of knowledge under an authoritarian regime. Will our report on Hampton National Historic Site disappear from the National Park Service site? In all probability, yes - we are, after all, calling out the enslavement and racism that built the United States. I contributed a chapter on the echoes of that enslavement in the formation of contemporary Baltimore County. So, for now, here's the report: https://drive.google.com/file/d/17RT9t1iewAvNxYgjaWV2tStaoNLgcxgT/view?usp=sharing

 

 

CFP: 13 Ghosts of Multimodality

 

 

 

CFP: AAA 2025

 

13 Ghosts of Multimodality: Critiquing, Rejecting and Learning to Live with Multimodality’s Problems

Panel Organizer: Samuel Collins (scollins@towson.edu)

 

(Still from "The 13 Ghosts of Scooby-Doo" (1985))

William Castle was the director and producer of countless horror movies, many of which utilized various “gimmicks”--seats wired to deliver electrical shocks, puppets that appeared from behind the movie screen, props of all kinds. His film “13 Ghosts” (1960) was no exception: the movie recounts the efforts of a family to spend the night in a haunted house and the audience was given special glasses to see the ghosts or make them disappear, an effect (“Illiusion-O”) that critics found a distraction and that did not last into the re-making of the film in 2001. Indeed, many of Castle’s tricks didn’t work as intended: too much voltage to the seats, puppets that people would throw their popcorn at, props that ran far afield of the films they were supposed to support. These were the “ghosts” that bedeviled Castle films. Whatever their success or failure, however, Castle could be considered a multimodal pioneer–constantly trying to reach beyond film to engage other senses. And like Castle, we are also faced with our multimodal “ghosts”--the media that distract, that open alternative narratives, that escape us to create their own, refractory meanings or that produce their own attendant inequalities. Finally, we face some of the same charges of glib insouciance in adopting media that are often seen as outside of anthropology’s usual purview. Here, the gravity of anthropology itself haunts the work.

This panel considers all of these ghosts, and not necessarily to vanquish them. In the spirit of Avery Gordon, ghosts emerge from the past to demand that we act in the future to address an injustice. These multimodal ghosts challenge us to confront digital divides, interrogate what we mean by “collaboration,” and, ultimately, address ethnographic revanchism at the edges of an aesthetic multimodality. Alternately, as Alfred Russel Wallace believed, ghosts are messengers from a utopian future that might stimulate us to lean into the multimodal in order to “burn down” the colonialism of anthropology. Finally, like the hapless Zorba family in “13 Ghosts” who try to last the night the night in the haunted mansion, we might choose to leave–to reject the multimodal–or stay on, learning to live with meanings, platforms and narratives that do not always go as planned. Accordingly, this panel seeks to include papers in a variety of registers: theoretical, confessional, accusatory, communicating through text or through diverse media. Like Castle’s “Illusion-O” glasses, we shift perspectives to see the ghosts or render them invisible; this is both the promise of the multimodal and its inherent weakness. From one perspective, the multimodal helps us to understand and intervene in an increasingly unequal world; from another, power retreats behind a re-deployment of the auteur for a digital age.

Please submit abstracts (250 words) and title by March 14, 2025 to Samuel Collins

(scollins@towson.edu). Decisions will be made by March 21.

 

Cybernetics and Anthropology - Past and Present

 I continue to wrestle with the legacy of cybernetics in anthropology - and a future premised on an anthropological bases for the digital. 

https://histanthro.org/notes/communication-without-control-macy-conferences/

Network Ghosts in the Age of Generative AI

 

What are faculty thinking about generative AI? In my role at our faculty center, I speak to faculty often on the problems they face teaching in the era of AI, and the workarounds they've come up with. The advent of publicly available generative AI platforms was not something people in my field (anthropology) or other faculty in the social sciences and humanities were clamoring for. And yet here we are. This has led to many responses: anguish, certainly, but also ways of incorporating--or at east channeling--the usage of generative AI in the classroom.

But what about faculty outside of my university? I used NodeXL to download Reddit data from the "/Professors" subreddit using the keyword "AI." This generated records of about 2500 users posting, commenting or replying for a total of 7000 contributions to the debate. I then grouped the data in clusters of similar postings, and abstracted the top words from each group as indicated by "up-vote" (which functions as more of a "like" in Reddit). As you can see, faculty were not particularly optimistic about AI in 2024. Yes, there were a couple of more computopian posters (and at least one computer scientist) who chided the community for rejecting what they saw as inevitable. But most worried that their efforts to teach writing, critical thinking, methodology and analysis were thwarted by student reliance on generative AI. Cynically, they predicted their university's tolerance for AI cheating, and speculated over their ability to continue as faculty under these conditions.

In 2024, Reddit sold their content to Google to train their large language models. This would have been been more objectionable, perhaps, if it wasn't already abundantly clear that generative AI have already been trained on Reddit, which maintains a relatively open API at a time when most social media have monetized their social network data. But what happens to that Reddit data when its re-constituted by generative AI? I decided to prompt Microsoft's Co-Pilot (to which I have enterprise-level access) to generate a spreadsheet of a Reddit conversation on AI between professors. Here's the prompt: "I would like you to generate an excel file similar to a Reddit conversation on a subreddit called "professors." The posts should discuss ChatGPT and student work from the perspective of the professor, and should include comments and replies to those comments. There should be 4 columns in the spreadsheet: A (person commenting or replying); B (person whom A is replying to); C (the text of the comment or reply); and D (the date of the reply or comment). Please populate the spreadsheet with at least 20 comments and 350 replies to those comments."

Co-pilot returned a network with with just 10 users, with 350 edges representing multiple re-postings(?) of user posts. Re-posting really isn't a thing with Reddit, so perhaps there's some confusion here with XTwitter. Since this is a much smaller network, I just labeled the 10 nodes with key words from their posts. The comments are a near "upside down" to the actual Reddit discourse over 2024, generally praising the efficiencies of generative AI and, when critical, speculating over the need for faculty at all (hence the precarity). Of course, there's a snarky comment on "Clippy," the irritating Microsoft assistant. The network itself, while smaller, is also structurally different. The actual Reddit network has a density of .001158737. In network measures of density, "1" would represent 100% connection--everyone connected to everyone else. So .0012 may not seem like much, but it's typical of social media networks where, after all, most of us don't feed the trolls and we save our replies for issues (and users) that we really care about. On the other hand, my AI-generated network has one of 0.966666667--an almost perfectly connected network where everyone has replied to everyone in a style of a polite and ploddingly inclusive panel discussion.

So, I guess that Co-Pilot does a lousy job simulating a subreddit? Yes, but, I think, more than that. It wasn't that long ago (2023) when XTwitter adopted a fee-based model for API access. That decision placed Twitter data beyond the reach of most of us. When social media data disappears behind paywalls, we (ordinary researchers) no longer really have access to the "connected action" of social media. While we can certainly look at social media, this only exposes us to our respective corners of the media platforms we inhabit and the structural components of social media are lost. But what happens when social media content is sold to OpenAI or Google Gemini? When social media disappears into a large language model, both the content and the connections are lost, and the simulated networks produced through generative AI manage to misrepresent social media on both fronts. Since Co-Pilot's inner workings are opaque to us, it is unclear if these results are the result of deliberate choice, unintended bias or something else.