The Epistemic Politics of AI Anthropomorphism

2026-08-01

AI Summary

This study challenges the dominant paradigm in AI anthropomorphism research, which treats users’ anthropomorphic understandings of AI as cognitive biases while disregarding their experiential validity and systematically marginalizing neurodiverse and other vulnerable populations. Moving beyond technocentric assumptions, the project offers the first epistemological-political analysis of this field, integrating interdisciplinary critical theory, epistemological critique, and institutional discourse analysis to expose how prevailing frameworks sustain epistemic hegemony through self-validating cycles of evidence. The work not only reveals the deep-seated biases and ethical risks embedded in current approaches but also articulates the methodological commitments necessary for developing inclusive paradigms of human–AI interaction, thereby laying the theoretical groundwork for more equitable human–machine relations.

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.

Problem

• AI anthropomorphism
• epistemic authority
• institutional framing
• neurodivergent users
• human-AI interaction

Innovation

• AI anthropomorphism
• epistemic authority
• institutional framing
• neurodivergent users
• methodological equity