Posthumanist AIRethinking ‘the human’ as a model for generative AI
MATT RATTO Faculty of Information, University of Toronto, Toronto, Canada, matt.ratto@utoronto.ca
SARAH GRAM Faculty of Information, University of Toronto, Toronto, Canada, sara.gram@mail.utoronto.ca
OLIVIA DOGGETT Faculty of Information, University of Toronto, Toronto, Canada, olivia.doggett@mail.utoronto.ca
PETER SELBY Center for Mental Health and Addiction, Toronto, Canada, peter.selby@camh.ca
NADIA MINIAN Center for Mental Health and Addiction, Toronto, Canada, Nadia.Minian2@camh.ca
MARTA MASLEJ Center for Mental Health and Addiction, Toronto, Canada, Marta.Maslej@camh.ca
OSNAT MELAMED Center for Mental Health and Addiction, Toronto, Canada, osnat.melamed@camh.ca
JONATHAN ROSE, Faculty of Engineering, Toronto, Canada, jonathan.rose@utoronto.ca
The power of generative AI systems is often described as their ability to produce ‘human-like’ creative outputs, including images and text.
But what is considered ‘human-like’? What humans and behaviors serve as the models for generative AI systems? And how might
unexamined concepts of ‘humans’ impact the design and operation of AI systems? Scholars have described how reductive concepts ofhumanness have been the source of many inequities in society including apartheid [1], colonialism )[2, 3, 4], and gender inequality )[5, 6,7, 8]. To advance our understanding of humanness in the context of generative AI, three empirical questions are important: what kinds ofhumans serve as the models for AI; what human traits are considered appropriate and appropriable; and how are these traits operationalized
within specific AI systems? We use these questions to support an initial engagement with the concept of posthumanist AI, building on
existing and growing posthumanist scholarship. If we are ourselves ‘posthuman’ as Katherine Hayles has defended, [9] what are the issues
with designing and building generative AI systems based on a reductive vision of humanness? What possibilities unfold when we open
our minds to a more expansive vision?
CCS CONCEPTS •Human-centered computing~Collaborative and social computing~Collaborative and social computing theory, concepts
and paradigms
Additional Keywords and Phrases: generative AI, humanness, design, posthumanism
ACM Reference Format:
Matt Ratto, Sarah Gram, Olivia Doggett, Peter Selby, Nadia Minian, Osnat Melamed, Jonathan Rose. 2024. Posthumanist AI: Rethinking‘the human’ as a model for generative AI. In GenAICHI: CHI 2024 Workshop on Generative AI and HCI.

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1 INTRODUCTION
Most of us have been amazed at what new transformer-based NLP systems can do. Whether it is gas-lighting a reporteron Valentine's Day to convince him to leave his wife, [10], acing standardized tests (except English), [11] or telling jokes[12], enthusiasm regarding LLM-powered conversation agents have centered around their apparent capacity to generatecreative outputs, learn by example, carry out introspective reflections, engage in chain-of-thought reasoning, and evendeceive and manipulate users. These technologies are troubling established (though constantly renegotiated) boundaries
between humans and computational systems, primarily by generating forms of social interaction that were previously only
considered possible between humans -- and sometimes extended to other biological entities such as pets and othercompanion species. [13] Moving beyond ontological questions regarding AI and humans, we see a need to more closely
examine what work is being done, deliberately or accidentally, by using humanness as a criterion for designing andevaluating AI systems. Here, we follow Suchman's guidance to move from: “…categorical debates to empiricalinvestigations of the concrete practices through which categories of human and nonhuman are mobilized” [14]. To begin
this work, we briefly focus on three main questions, which we use to explore why defining humanness remains a keyconcern. First, how did we come to correlate humanness and AI? Second, how are humans/humanness interpolated within
AI systems? Finally, how is humanness defined within technical literature associated with the evaluation of generative AIsystems? Noting its underdetermined nature within the development of generative AI, we highlight the need to better define
what counts as ‘human’ both for generative AI and for ourselves and end with a suggested starting place of posthumanism.
2 BACKGROUND
2.1 How did we come to correlate humanness and ai?
We have a long history in building things that represent human behaviors and capabilities with linguistic ability often
serving to define what remains purely human. Both Descartes and Turing used the capability of language to draw a clear
line between humans and other beings including animals and machines. Descartes notes that even if we could build amachine that perfectly mimicked human actions and appearance “…they could never use words or other signs arranged insuch a manner as is competent to us in order to declare our thoughts to others” [15] Similarly, Turing posited his famous
test which leveraged language use and its evaluation as a way to produce an empirically determinant way to addressmachine cognition:“We now ask the question, ‘What will happen when a machine takes the part of A in this game?’ Will theinterrogator 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?’” [16]Correlating language ability with language to intelligence continues to shape our understanding of machine cognition. But
here is where we run into the first issue. Attempts to deploy Turing’s test in applied settings have often resulted inconfusions about what counts as human. An attempt to administer an annual Turing Test was created in 1991. Called theLoebner Prize, it pitted humans recruited via a newspaper advertisement against multiple computational agents. Stuart
Shieber, writing about the results of the first contest, emphasized some of the challenges. He noted:“Ms. Cynthia Clay, the Shakespeare aficionado, was thrice misclassified as a computer. At least one of the judges
made her classification on the premise that (no) human would have that amount of knowledge about
Shakespeare… Ms. Lisette Gozo was honored as the most human of the agents for her discussion of women'sclothing, although one judge rated two computer programs above her.” [17]GenAICHI: CHI 2024 Workshop on Generative AI and HCI
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2In the quotes above, we see two problems; first that a human showing too much knowledge is mischaracterized as acomputer; second, that knowledge of a certain sort – women’s clothing – is considered ‘more human.’ Both examples show
how unexamined ideas of ‘humanness’ can result in forms of bias. The judges’ assumptions about what female humanknowledge is, e.g clothes and not Shakespeare, ends up impacting their comparison and ultimately what counts as humanvs. machine.
2.2 How are humans/humanness interpolated within AI systems?
2.2.1 Choices of datasets - what language being used to train?
In their now canonical paper on “Stochastic Parrots”, Bender, Gebru, McMillan-Major, and Mitchell note that "it is easy
to imagine that very large datasets, such as Common Crawl “…petabytes of data collected over 8 years of web crawling”
a filtered version of which is included in the GPT-3 training data, must therefore be broadly representative of the ways inwhich different people view the world" [18]. The authors argue that this fantasy is, in fact, untrue.
…white supremacist and misogynistic, ageist, etc. views are overrepresented in the training data, not onlyexceeding their prevalence in the general population but also setting up models trained on these datasets to further
amplify biases and harms.[18]They attribute this to a number of factors, including the primary use of Reddit data in training data sets, systematic
exclusionary tactics that reduce the participation by marginalized populations in online user-generated content more
broadly, and even techniques used to 'clean' and 'filter' this data that, while intended to remove pornography and hatespeech, also work to delete texts from online sites built for and by LGBTQ people. Monolinguistic data, mainly English,
and primarily western and represented by Latin characters [19] further encodes systematic bias into AI systems.
2.2.2 What counts as human - how is humanness evaluated?
In addition to the data used to train models, another way that humanness is brought into LLMs is through its use as acriterion for evaluating models. This is a less examined aspect of LLMs – what conceptual tools are used to determine thequality of their outputs? A search on the ACM Digital Archive reveals that ‘humanness’ is highly considered in ACMpapers that also reference LLMs. A basic search on the terms llm OR LLM AND 'human-like' OR 'human-mimicking'results in 326,979 hits. However, in most of these papers what is meant by ‘humanness’ or ‘human-like'-ness is neverdefined. Instead, it is assumed that readers and authors share a common understanding. Other articles rely on use surveys[20, 21, 22] following interactions with generative AI systems to evaluate how 'human-like' users found the bot. While‘humanness’ serves as a category of evaluation, what it means remains underdetermined, leaving decisions over whatcounts as human-like behavior to the evaluator. This would be fine if there was consistent agreement as to what attributes
are included, but it seems clear from the work described above that there is a need to unpack and denaturalize this category.
3 DEFINING 'HUMANNESS'
The brief answers to the three questions posed above highlight the need to better define humanness in relation to AI as webuild, train, and evaluate these systems.

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33.1 Posthumanism
Posthumanism, as distinct from transhumanism, [23] offers a radical denaturalization of the human that seems necessary,
given the ways generative AI systems ‘work the boundary’ between human and machines. Posthumanism is exemplified
in concepts such as Donna Haraway’s “cyborg” [6], which is, at its core, a critique and rejection of the boundaries
separating human, machine, and animal and an insistence on the plurality and multiplicity of the subject. Braidotti [7, 8])
has outlined the ramifications and potentialities of this, including the need to move beyond simplistic understandings ofindividualistic subjectivities, a recognition of the systematic or ‘networked’ nature of identity [24], and the importance of‘situated knowledge’ [25]. Further, Braidotti draws upon the anti-humanism of post-structuralist and anti-colonial scholars[2, 3, 4, 5] to examine the privileging of certain aspects of ‘humanness’, including rationality, reason, and a related set of“mental, discursive and spiritual values.” Unexamined ideas of humanness highlight the ways in which normalizing
humans within a white and unexamined ideal supports political and cultural acts of colonization.
The goal of posthumanist scholarship is to reopen social conventions to critique, allowing for more diversity of humansubject positions including those of non-white, non-European, and non-heterosexual identities, as well as humans with
non-neurotypical and divergent bodies. Mellamphy has called for a similar engagement with AI, noting:"While most discussions of AI are still anchored in humanistic/humancentric narratives, there are reasons forrejecting this model and for turning to alternative worldviews." [26]Just as Roe and Marathe have called for better definitions of race in HCI research [27] we see a need to engage similarly
with definitions of humanness. The posthumanist literature is key, not just to unpacking and reopening our normative andpotentially biased concepts of humanness, but also in positing alternatives. Concepts drawn from this literature can provide
starting points for the design of generative AI systems in ways that do not reify existing cultural biases through normative
and assumed concepts of humanness. We are currently exploring such terms as assemblage, cyborg, embodiment,
emergence, co-individuation, and others in relation to an applied project building a conversational agent [28] and will
publish the results in longer work.
CONCLUSION
In Vandana Shiva’s seminal work she describes the portrayal of science as universal, as a ‘monoculture of the mind’, whichworks to reduce and denaturalize indigenous ways of knowing [29]. In naturalizing certain assumptions about humans andhuman capacities within AI, we risk creating another kind of 'monoculture of the mind' where only certain types ofinteractions and forms of behavior are considered appropriately human. Could LLMs end up producing a kind of 'cognitive
imperialism'? More optimistically, what forms of cognition and interaction might we develop if we work from a more
complex and equitable concept of humanness?
ACKNOWLEDGMENTS
The authors would like to thank the CAMH and U of Toronto MIBot team for their continued conversations and reflections
on these themes.
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