# The Epistemic Politics of AI Anthropomorphism

arXiv: https://arxiv.org/abs/2608.00961 · HTML: https://mlsanity.com/paper/2608.00961/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 119 references that are in the map.
- First posted on arXiv 2 Aug 2026.

## Builds on (most cited first, 18 of 18)

- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization](https://mlsanity.com/paper/2401.18079/) (2024)
- [LLMs Get Lost In Multi-Turn Conversation](https://mlsanity.com/paper/2505.06120/) (2025)
- [Lessons From an App Update at Replika AI: Identity Discontinuity in Human-AI Relationships](https://mlsanity.com/paper/2412.14190/) (2024)
- [Humanizing Machines: Rethinking LLM Anthropomorphism Through a Multi-Level Framework of Design](https://mlsanity.com/paper/2508.17573/) (2025)
- [Humanlike AI Design Increases Anthropomorphism but Yields Divergent Outcomes on Engagement and Trust Globally](https://mlsanity.com/paper/2512.17898/) (2025)
- ["Please, don't kill the only model that still feels human": Understanding the #Keep4o Backlash](https://mlsanity.com/paper/2602.00773/) (2026)
- [Unifying Sparse Attention with Hierarchical Memory for Scalable Long-Context LLM Serving](https://mlsanity.com/paper/2604.26837/) (2026)
- [Exploring Large Language Models Through a Neurodivergent Lens: Use, Challenges, Community-Driven Workarounds, and Concerns](https://mlsanity.com/paper/2410.06336/) (2024)
- [The GPT-4o Shock Emotional Attachment to AI Models and Its Impact on Regulatory Acceptance: A Cross-Cultural Analysis of the Immediate Transition from GPT-4o to GPT-5](https://mlsanity.com/paper/2508.16624/) (2025)
- [Does My Chatbot Have an Agenda? Understanding Human and AI Agency in Human-Human-like Chatbot Interaction](https://mlsanity.com/paper/2601.22452/) (2026)
- [Relational Dissonance in Human-AI Interactions: The Case of Knowledge Work](https://mlsanity.com/paper/2509.15836/) (2025)
- [What if AI systems weren't chatbots?](https://mlsanity.com/paper/2605.07896/) (2026)
- [Antisocial behavior towards large language model users: experimental evidence](https://mlsanity.com/paper/2601.09772/) (2026)
- [A Checklist for Trustworthy, Safe, and User-Friendly Mental Health Chatbots](https://mlsanity.com/paper/2601.15412/) (2026)
- [Intimacy as Service, Harm as Externality: Critical Perspectives on AI Companion Platform Accountability](https://mlsanity.com/paper/2604.06381/) (2026)
- [Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Power](https://mlsanity.com/paper/2604.21043/) (2026)

## Abstract (arXiv)

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.