mlsanityData to 7 Oct 2026

From "AI" to Probabilistic Automation: How Does Anthropomorphization of Technical Systems Descriptions Influence Trust?

arXiv 2404.16047 ↗First posted 8 Apr 2024cs.HC cs.AIcited by 18

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Abstract (arXiv)

This paper investigates the influence of anthropomorphized descriptions of so-called "AI" (artificial intelligence) systems on people's self-assessment of trust in the system. Building on prior work, we define four categories of anthropomorphization (1. Properties of a cognizer, 2. Agency, 3. Biological metaphors, and 4. Properties of a communicator). We use a survey-based approach (n=954) to investigate whether participants are likely to trust one of two (fictitious) "AI" systems by randomly assigning people to see either an anthropomorphized or a de-anthropomorphized description of the systems. We find that participants are no more likely to trust anthropomorphized over de-anthropmorphized product descriptions overall. The type of product or system in combination with different anthropomorphic categories appears to exert greater influence on trust than anthropomorphizing language alone, and age is the only demographic factor that significantly correlates with people's preference for anthropomorphized or de-anthropomorphized descriptions. When elaborating on their choices, participants highlight factors such as lesser of two evils, lower or higher stakes contexts, and human favoritism as driving motivations when choosing between product A and B, irrespective of whether they saw an anthropomorphized or a de-anthropomorphized description of the product. Our results suggest that "anthropomorphism" in "AI" descriptions is an aggregate concept that may influence different groups differently, and provide nuance to the discussion of whether anthropomorphization leads to higher trust and over-reliance by the general public in systems sold as "AI".

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 9 of its 55 references that are in the map. First posted on arXiv 8 Apr 2024.

Builds on

9 of 55 references.

References · 9

  1. 2018Metrics for Explainable AI: Challenges and Prospectscited by 257
  2. 2022Talking About Large Language Modelscited by 174
  3. 2020Conceptual Metaphors Impact Perceptions of Human-AI Collaborationcited by 47
  4. 2023Mirages: On Anthropomorphism in Dialogue Systemscited by 30
  5. 2021"Look! It's a Computer Program! It's an Algorithm! It's AI!": Does Terminology Affect Human Perceptions and Evaluations of Algorithmic Decision-Making Systems?cited by 13
  6. 2021Guilty Artificial Mindscited by 9
  7. 2022Deanthropomorphising NLP: Can a Language Model Be Conscious?cited by 8
  8. 2018Robots Racialized in the Likeness of Marginalized Social Identities are Subject to Greater Dehumanization than those racialized as Whitecited by 3
  9. 2024What Motivates People to Trust 'AI' Systems?cited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 8; 2026: 4.

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  1. 2025ELEPHANT: Measuring and understanding social sycophancy in LLMscited by 115
  2. 2026The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systemscited by 27
  3. 2025AI Alignment at Your Discretioncited by 20
  4. 2024Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliancecited by 15
  5. 2025A Taxonomy of Linguistic Expressions That Contribute To Anthropomorphism of Language Technologiescited by 14
  6. 2025Thinking beyond the anthropomorphic paradigm benefits LLM researchcited by 13
  7. 2024Evaluating AI Evaluation: Perils and Prospectscited by 11
  8. 2025HumT DumT: Measuring and controlling human-like language in LLMscited by 11
  9. 2024"I Am the One and Only, Your Cyber BFF": Understanding the Impact of GenAI Requires Understanding the Impact of Anthropomorphic AIcited by 7
  10. 2025Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articlescited by 6
  11. 2025Sycophantic AI Decreases Prosocial Intentions and Promotes Dependencecited by 5
  12. 2024False Sense of Security in Explainable Artificial Intelligence (XAI)cited by 4
6 more
  1. 2024How ChatGPT Changed the Media's Narratives on AI: A Semi-Automated Narrative Analysis Through Frame Semanticscited by 4
  2. 2024The impact of labeling automotive AI as "trustworthy" or "reliable" on user evaluation and technology acceptancecited by 0
  3. 2025Everything Counts: The Managed Omnirelevance of Speech in Human-Voice Agent Interactioncited by 0
  4. 2026Six misconceptions about large language models: A minimal model and diagnostic taxonomycited by 0
  5. 2026Assessing mentalization in humans and large language modelscited by 0
  6. 2026Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigationscited by 0