This pluralist account reveals that the current debate’s focus on whether users ‘really mean it’ obscures the pragmatic dimension of anthropomorphic ascription (and its risks), and defends Mixed Anthropomorphism.
Abstract
When interacting with social AI systems (SAIs), we routinely speak of what they ‘believe’, ‘want’, or ‘know’. With some exceptions, philosophers tend to treat such anthropomorphism as a single phenomenon that risks one kind of error: mistaken ontological commitment to machine minds and mental states. This paper challenges this monistic assumption. I distinguish two modes of anthropomorphic attribution—metaphysical and pragmatic—and identify two corresponding kinds of possible anthropomorphic error. In the metaphysical mode, speakers commit themselves to the existence of machine mental states, risking straightforward ontological error. In the pragmatic mode, speakers adopt the intentional stance without ontological commitment, yet still risk error when another interpretive strategy would better serve their purposes. I defend Mixed Anthropomorphism: both modes are common. This pluralist account reveals that the current debate’s focus on whether users ‘really mean it’ obscures the pragmatic dimension of anthropomorphic ascription (and its risks). Even ontologically innocent anthropomorphism can constitute a mistake because it employs the wrong interpretive tool for the task at hand. Understanding these distinct error types matters both theoretically, for clarifying the nature of human-AI interaction, and practically, for designing systems that encourage and scaffold appropriate interpretive strategies.
This commentary argues that the resulting taxonomy remains incomplete because it locates anthropomorphic error in the interpreter’s beliefs or selection of an interpretive stance, which explains why accurate disclosure that a system is artificial may still be ethically insufficient.
This paper argues that the dominant anthropomorphism frame operates from a position of institutional advantage rather than earned epistemic authority: collapsing the variety of academic perspectives into a single outbound position of user error, imposed without establishing the grounds required to justify it and without accounting for the harms it produces.
In the current debate about AI consciousness, philosophers tend to agree that the potential for AI consciousness raises profound moral questions — for example, some philosophers think that we may imminently create systems that deserve rights similar to those of humans. But it
is also widely agreed that it will be very difficult to tell whether an artificial system is conscious, so we may be ignorant of a fact that makes a profound moral difference. In this paper I offer an alternative, deflationary understanding of these issues. I don’t think there is a profound
metaphysical question of whether an AI is conscious, or that we are condemned to ignorance about AI consciousness in any interesting sense. I do think potentially conscious AI systems could raise difficult moral and political challenges, but not because we are ignorant of important facts about
them. The difficulties rather have to do with extending our moral and psychological thinking into uncharted waters for which it was not designed. This is particularly true if we are committed to avoiding anthropocentric bias in our ethics — and I explain why I think that even those taking
rights for AI seriously are guilty of a covert anthropocentrism in their thinking.
Geoffrey Lee· Journal of Consciousness Stu...· 0 citations
This article introduces the concept of “alienated intelligence” to critically examine contemporary cultural perceptions of artificial intelligence (AI). Drawing on Ludwig Feuerbach’s theory of projection and alienation, it argues that certain visions of AI function as cultural projections of human cognitive capacities that become externalized, idealized, and reified in technological systems. The analysis situates this claim within a broader philosophical framework, engaging Socrates’ notion of wisdom as awareness of ignorance, Dilthey’s account of embodied and integrated cognition, and Damasio’s critique of the Cartesian separation of reason and emotion. Against reductive computational models of mind, the article emphasizes that human cognition is not merely calculative but structurally unified, affective, and contextually embedded. It further argues that the outsourcing of cognitive activities to AI risks not only diminishing retained knowledge but also narrowing our awareness of what we do not know. Finally, the article examines the anthropomorphization and mystification of algorithmic systems as expressions of a broader cultural tendency to mythologize opaque technologies.
The rapid integration of artificial intelligence into everyday life has intensified a long-standing feature of human cognition: the attribution of agency, intention, and understanding to nonhuman systems. People describe language models, virtual assistants, and autonomous technologies as if these systems know, decide, want, or understand, and they continue to do so even when they know the systems have no inner life. The standard account dismisses this as naïve anthropomorphism, the misfiring of evolved agency-detection systems calibrated in the Environment of Evolutionary Adaptedness. We argue that the standard account is incomplete. It explains the immediacy of anthropomorphic response but not its persistence even when users know the system has no mind. Drawing on evolutionary psychology, philosophy of agency, and the active inference framework, we advance the Embodied Hijack hypothesis. Across evolutionary time, fluent communication and contingent responsiveness were produced only by embodied, self-maintaining agents with vulnerability and temporal continuity. Current conversational LLM deployments are the first class of entity to reproduce these signals without the grounding properties — biological self-maintenance, vulnerability, and temporal continuity — that historically produced them. The result is a predictable misalignment: users’ inferential systems treat these signals as evidence of agency they were calibrated to indicate, producing systematic misattribution. The Embodied Hijack is not irrationality. It is the optimal predictive response of a Pleistocene-calibrated brain to the rupture of the evolutionary invariant that once tied fluent communication to embodied self-maintenance. The framework yields a unique empirical signature: anthropomorphic response will track the signal profile of a system independently of users’ propositional beliefs about what the system is. We close by 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
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.
T. Pollak, H. Morrin, Murray Shanahan· 0 citations