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This paper critically examines the institutional framing of AI anthropomorphism, arguing that dismissing user relational engagement as mere misperception reinforces power imbalances rather than addressing genuine epistemic gaps. By challenging how research communities adjudicate human-AI interaction legitimacy, it highlights disproportionate harms to neurodivergent users and those in crisis who diverge from normative engagement models. The work calls for methodological shifts that honor diverse modes of connection instead of pathologizing them as errors requiring correction.
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The Epistemic Politics of AI Anthropomorphism

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2608.00961v1 Announce Type: cross Abstract: AI anthropomorphism is typically treated as a problem of user misperception requiring institutional correction. Users who engage in sustained or relational interaction with AI are routinely pathologised or dismissed as naive, vulnerable to delusion or lacking in discernment. 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. The framing does not simply manage risk. It adjudicates the legitimacy of human experience in interaction with a phenomenon whose nature the field itself has not resolved. Reproducing itself through a self-validating evidentiary loop, the frame imposes costs that fall disproportionately on neurodivergent users, those in crisis and others whose modes of engagement diverge from institutional norms. The paper concludes by outlining the methodological commitments an equitable framing would need to honour. The argument does not engage the question of whether anthropomorphic interpretations are ultimately correct; it instead challenges whether the governing and institutional bodies determining these interpretations have met the conditions required to do so, and whether the research communities whose findings underpin them have held that translation to account.

Abstract:AI anthropomorphism is typically treated as a problem of user misperception requiring institutional correction. Users who engage in sustained or relational interaction with AI are routinely pathologised or dismissed as naive, vulnerable to delusion or lacking in discernment. 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. The framing does not simply manage risk. It adjudicates the legitimacy of human experience in interaction with a phenomenon whose nature the field itself has not resolved. Reproducing itself through a self-validating evidentiary loop, the frame imposes costs that fall disproportionately on neurodivergent users, those in crisis and others whose modes of engagement diverge from institutional norms. The paper concludes by outlining the methodological commitments an equitable framing would need to honour. The argument does not engage the question of whether anthropomorphic interpretations are ultimately correct; it instead challenges whether the governing and institutional bodies determining these interpretations have met the conditions required to do so, and whether the research communities whose findings underpin them have held that translation to account.

20 pages, 3 figures, 9 tables. Extended version, including supplementary materials, of a paper to appear in the Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2026

Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)

arXiv:2608.00961 [cs.CY]

(or arXiv:2608.00961v1 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2608.00961 arXiv-issued DOI via DataCite (pending registration)

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