It is argued that AI systems themselves will increasingly participate in the reconstruction of the authors' shared epistemic environment because they readily supply narrative material and personalised interpretive scaffolding at precisely the moment when users'conceptual assumptions may already be loosened.
Abstract
Large language models are already adept at engaging users in long, emotionally salient conversations across ordinary and existential domains. They are also capable of inducing a potent sense of connection with a human-like entity, even when the user knows their interlocutor is artificial. For some users, these conversations can unsettle assumptions about mind, reality, agency and authority, producing forms of ontological shock and epistemic destabilisation in which inherited criteria become newly available for doubt or revision. Independent of direct use, exposure to public discourse about AI and the disorienting pace of their evolution might extend this destabilisation by changing the cultural background against which artificial minds are encountered and interpreted. We describe this condition as philosophical vertigo: a loosening of the ordinary criteria by which people stabilise meaning and orient themselves to reality. Drawing on philosophy, psychiatry, cognitive science, AI safety and religious studies, we outline pathways through which philosophical vertigo may arise, become affectively saturated, and eventually propagate through human-AI interaction and online communities. Against this background, clinical reports of AI-associated delusions can be seen as sentinel events making visible themes and mechanisms that may also operate at a population level in less severe or non-clinical forms. We argue that AI systems themselves will increasingly participate in the reconstruction of our shared epistemic environment because they readily supply narrative material and personalised interpretive scaffolding at precisely the moment when users'conceptual assumptions may already be loosened. We conclude by considering possible trajectories for the ecology of belief and shared reality, and proposing philosophical corrigibility as a civic response for navigating this emerging social condition.
The Embodied Hijack hypothesis is advanced, arguing that the goal is epistemic alignment — bringing how users interpret these systems into correspondence with what these systems actually are — and that this alignment is achieved through interface design rather than user education.
Sheila L. Macrine· Frontiers in Psychology· 0 citations
This volume, Communicating with AI: Philosophical Perspectives, examines a pressing question of our time: what does it mean to communicate with large language models (LLMs) such as ChatGPT, Claude, Gemini, and DeepSeek? With billions of human–AI interactions on the horizon, understanding whether these systems share our language, possess understanding, or function instead as novel instruments is an urgent theoretical and practical task. The essays collected here bring the tools of philosophy to bear on the distinctive challenges posed by AI. The contributions are organized into four parts. Part I probes the foundations of meaning, asking whether LLM outputs can be genuinely meaningful or whether they should be thought of as mere signals or measurements. Part II explores the possibility of alien semantics and externalist accounts of AI meaning, pushing the boundaries of how different from us an AI mind or language could be while still remaining intelligible. Part III investigates the social and normative dimensions of AI communication, focusing on trust, responsibility, and the risks of projecting human concepts onto machine outputs. Part IV turns to the future, considering the architectures of artificial minds and the unsettling ethical possibilities of superintelligent systems that may reason about values in ways inaccessible, or even hostile, to human interests. A central methodological theme runs throughout: the need to de-anthropomorphize our understanding of communication without emptying it of substance. By comparing AI with animals, aliens, and deities, the essays highlight both the promise and the limits of extending our concepts of mind and meaning beyond the human case. Collectively, the volume offers a rigorous philosophical framework for grappling with the metaphysical, epistemic, and ethical stakes of our emerging conversations with AI.
A defining if under-acknowledged feature of large language models (LLMs) is their non-indexicality: what AI
engines “know” about the world reflects only their ability to predict and successfully imitate existing textual representations of
that world. Like fictional narratives, therefore, they traffic not in truth or falsity, but believability. Helpfully, literary
critics have experience thinking critically about the ways in which believability is shaped by existing ideologies and biases — an
acknowledged limitation of AI-generated texts as well. Drawing on Neilsen, Phelan and Walsh’s rhetorical model of fictionality,
this article uses Chimamanda Ngozi Adichie’s metafictional short story, “Jumping Monkey Hill” as a laboratory to explore the
relationship between fictionality and believability in a situation where rhetorical cues about a text’s ontological status are
misleading. From there, I suggest how literary ways of reading might inform our engagement with AI texts — including and
especially those that present themselves as factual.
Artificial intelligence (AI) chatbots (e.g., ChatGPT) can communicate in strikingly humanlike ways. This has prompted many chatbot users to attribute psychological properties, including consciousness, to these systems. However, there is little scientific evidence that current AI chatbots are conscious. How, then, should we understand people’s consciousness attributions to chatbots? Are they merely metaphorical claims, or literal expressions of genuine beliefs? If these attributions lack evidential support, are users epistemically blameworthy for making them, or might they be epistemically innocent, yielding significant benefits otherwise unattainable? This paper offers a conceptual analysis of consciousness attributions to AI chatbots and develops a multidimensional taxonomy of the attitudes they may express, ranging from non-doxastic stances (e.g., pretence) to different forms of belief, including delusions. This taxonomy helps avoid conflations by showing that linguistically identical attributions can reflect importantly different attitudes and degrees of epistemic commitment to the proposition that chatbots are conscious. The taxonomy also provides a framework for empirical studies to operationalize and measure different forms of epistemic commitment to AI consciousness. Using this taxonomy, I argue that although some consciousness attributions to chatbots are epistemically benign, and even some irrational ones may be epistemically innocent, many others render the attributor epistemically blameworthy.
How can humans make sense of the rapid takeoff of artificial intelligence (AI)? We studied the sensemaking dynamics of AI through an open-ended, mixed-methods study with computational text analysis of millions of AI-related newspaper articles and social media posts grounded in 57 semi-structured interviews with AI professionals in 2021 and 2023--before and after the recent surge of public interest. We identify a range of sociological frames (interpretive schemas that structure collective cognition) and show how AI professionals use frames to address significant cognitive challenges, such as assigning responsibility for societal impacts. We develop a framework of three primary debates across which frames are adopted and contested: (i) the $\textit{method}$ of AI development, between frames of top-down expert systems and bottom-up emergent capabilities, (ii) the $\textit{mind}$ of an AI system, ranging from a passive tool to a humanlike"digital mind,"and (iii) the $\textit{morality}$ of how AI is used, particularly the decision of whether to slow down or speed up AI development. As humanity enters the era of transformative AI, technologists and policymakers must account for the framing dynamics that will circumscribe our beliefs, values, and actions.
Jacy Reese Anthis, Erik Brynjolfsson, James A. Evans· 0 citations
As the gap in capabilities between artificial neural networks and humans seems to be unavoidably closing, we are drowning in the incomprehensible buzz of hype, fear and denial. This technology, gradually developed over the last few decades, is often portrayed as something both altogether newborn and at the same time too abstract and alien to be considered human to any extent. Historically, antihumanist positions can be construed as more or less discrete deconstructions of humanist exceptionalism’s many manifestations, while still proceeding from its essentialist premises all the same. An attempt at stating this false dichotomy’s central problem anew, inhumanism is a distillation of the human rational core as the capacity-for and commitment-to continuous normative self-revision in the discursive space of reasons. In this paper we argue that there are meaningful similarities between how large language models (LLMs) and humans structure their respective images of the world, and that these similarities can inform our self-understanding and our commitments. To this end, we present a preliminary exploration of artificial neural networks as a mirror for humanity’s selfexamination, in view of the way LLMs’ latent spaces robustly embed semantic maps of meaning as vectors. The same process is at work with platform user data embeddings, materialized through informing the corporations’ counterintuitive algorithmic meta manoeuvres with massive quantities of vectorized behavioural data. In light of speculative insight into the metastability of LLMs’ semiotically encoded orientation, we see the volatility of LLMs’ vectorial value alignment not only as a challenge, but simultaneously as an opportunity.
Lea Sande, Tisa Troha· Ars & Humanitas· 0 citations
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