Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations

arXiv:2609.38486v1 Announce Type: new Abstract: Large Language Models (LLMs) and more broadly Artificial Intelligence (AI) systems are often described and understood in human-like terms, a phenomenon known as \emph{anthropomorphism}. This paper provides a synthesis of recent literature on anthropomorphism in AI, covering theoretical frameworks, the role of language in framing AI as human-like, the various risks of anthropomorphizing machines, and strategies to mitigate these issues. After examining why we tend to anthropomorphize AI systems and whether we are right to do so, we highlight the impact of linguistic framing on anthropomorphism. Then, we introduce a conceptual taxonomy of risks associated with AI anthropomorphism. This taxonomy groups twenty-one concerns within five analytical categories: epistemic, affective, human agency, normative, and societal and institutional risks. Finally, we relate these concerns to proposed interventions in design, communication, education, and governance. We argue that a better understanding of AI systems requires concepts and theories grounded in their organization and demonstrated capacities. The linguistic shaping of anthropomorphic perceptions should form part of this scientific effort, since our descriptions influence both how these systems are understood and the roles we allow them to occupy in society.

Item discusses risks and mitigations of AI anthropomorphism.

This is a literature review and conceptual taxonomy, not an empirical finding about real-world harm or benefit. It raises legitimate concerns about anthropomorphic framing but offers no measured outcomes, so implications remain speculative and modestly cautionary rather than strongly dystopian or optimistic.

The paper identifies twenty-one specific risks across five categories resulting from anthropomorphizing AI. These risks collectively point to significant negative implications for human understanding, agency, norms, and societal structures. The proposed mitigations exist but do not negate the identified threats.

The paper identifies specific risks of anthropomorphism in AI and proposes clear mitigation strategies. It advocates for a scientific understanding of AI, which is strongly optimistic for human flourishing.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   43f35e1e-8b3c-473b-bd4c-a23c198e3833
           https://arxiv.org/abs/2609.38486
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EVIDENCE   2 extracted excerpts
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      -26  (Adverse)
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CONFIDENCE 74%