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.
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.
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 uses disparate examples of AI resistance and theoretical lenses to render explicit an underlying logic which connects varied forms of resistance, grounded in the ethical value(s) that effort can manifest, and reframe one side of the debate between embracers and resisters.
R. Downes, Rebecca Mines· AI and Ethics· 0 citations
The paper’s central argumentative shift is to change the narrative from bias mitigation to bias management—treating bias not as a defect to be corrected but as an ongoing condition to be governed.
Gabriela Arriagada-Bruneau· Science and Engineering Ethi...· 0 citations
The article demonstrates how attempts to decentre the human often reconstitute new forms of authority in attempts to decentre the human and examines the possibility of surpassing standard anthropocentric approaches in AI while maintaining a critical philosophical engagement with the structurally necessary yet precarious character of organizing principles.
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.
The argument further holds that AI does not possess moral agency in the classical sense but functions as an infrastructural precondition for the reconfiguration of normative hierarchies—in an empirical rather than transcendental sense.
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
MIT News · Artificial Intelligence· news.mit.eduJul 7, 2026
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.