Knowledge Resource
Research Summary: Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Summary & Analysis prepared by
- Aziz Shuaib Ausi
- Resource type
- Research Summary / Knowledge Resource
- Resource published on AZIZ OS
- 2 October 2026
- Reading time
- 1 min
- Publication type
- Knowledge Resource
- Availability
- Open access
About this Summary & Analysis
AZIZ OS provides independently prepared summaries and analytical interpretations of externally published research and knowledge sources. The underlying works remain attributable to their original authors and rights holders. This resource is intended to improve accessibility and understanding and does not replace the original publication.
This research paper provides an overview of anthropomorphism in Large Language Models (LLMs) and broader AI systems, synthesizing existing literature. It covers theoretical frameworks, the impact of language in portraying AI as human-like, and potential risks, concluding with mitigation strategies. The paper introduces a taxonomy of twenty-one concerns related to AI anthropomorphism.
Why it matters
Understanding the risks and implications of anthropomorphizing AI is crucial for the responsible development and deployment of advanced technological systems. It impacts public perception, user interaction, and the potential for misinterpretation of AI capabilities, which can have downstream effects on trust and adoption.
Key insights
- Anthropomorphism, the tendency to describe AI systems in human-like terms, is a prevalent phenomenon.
- The research synthesizes theoretical frameworks explaining why humans anthropomorphize AI.
- Linguistic framing significantly influences the perception of AI as human-like.
- A conceptual taxonomy of twenty-one risks associated with AI anthropomorphism has been developed.
- The paper examines whether anthropomorphizing AI is appropriate and identifies strategies for mitigation.
Source
arXiv — Computers and Society — https://arxiv.org/abs/2609.38486
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Citation
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- Verification ID
- ASA-EXE-2026-01088
- Version
- v1.0 · r0
- Issued
- 2 October 2026
- Resource prepared by
- Aziz Shuaib Ausi
- Resource status
- Research Summary / Knowledge Resource
- Underlying work
- Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Provenance status
- Attribution requires verification
- Rights
- Underlying publication rights remain with the respective copyright holder(s). Refer to the original source for authoritative publication and licensing information.
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