# From tools to thieves: Measuring and understanding public perceptions of AI through crowdsourced metaphors

arXiv: https://arxiv.org/abs/2501.18045 · HTML: https://mlsanity.com/paper/2501.18045/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 124 references that are in the map.
- First posted on arXiv 29 Jan 2025.

## Builds on (most cited first, 20 of 20)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale](https://mlsanity.com/paper/2211.03759/) (2022)
- [Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models](https://mlsanity.com/paper/2305.18189/) (2023)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models](https://mlsanity.com/paper/2404.16019/) (2024)
- [In conversation with Artificial Intelligence: aligning language models with human values](https://mlsanity.com/paper/2209.00731/) (2022)
- [Conceptual Metaphors Impact Perceptions of Human-AI Collaboration](https://mlsanity.com/paper/2008.02311/) (2020)
- [Believing Anthropomorphism: Examining the Role of Anthropomorphic Cues on Trust in Large Language Models](https://mlsanity.com/paper/2405.06079/) (2024)
- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)
- [AnthroScore: A Computational Linguistic Measure of Anthropomorphism](https://mlsanity.com/paper/2402.02056/) (2024)
- [The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering](https://mlsanity.com/paper/2305.16519/) (2023)
- [Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model](https://mlsanity.com/paper/2106.02596/) (2021)
- [Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation](https://mlsanity.com/paper/2402.01705/) (2024)
- [Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance](https://mlsanity.com/paper/2407.07950/) (2024)
- [AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollocks](https://mlsanity.com/paper/2401.06178/) (2024)
- [Discovering Differences in the Representation of People using Contextualized Semantic Axes](https://mlsanity.com/paper/2210.12170/) (2022)
- ["Ghost of the past": identifying and resolving privacy leakage from LLM's memory through proactive user interaction](https://mlsanity.com/paper/2410.14931/) (2024)
- ["I Am the One and Only, Your Cyber BFF": Understanding the Impact of GenAI Requires Understanding the Impact of Anthropomorphic AI](https://mlsanity.com/paper/2410.08526/) (2024)

## Built on by (most cited first, 10 of 10)

- [ELEPHANT: Measuring and understanding social sycophancy in LLMs](https://mlsanity.com/paper/2505.13995/) (2025)
- [Thinking beyond the anthropomorphic paradigm benefits LLM research](https://mlsanity.com/paper/2502.09192/) (2025)
- [Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs](https://mlsanity.com/paper/2601.04435/) (2026)
- [Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence](https://mlsanity.com/paper/2510.01395/) (2025)
- [Verbalizing LLMs' assumptions to explain and control sycophancy](https://mlsanity.com/paper/2604.03058/) (2026)
- [Cultural Compass: A Framework for Organizing Societal Norms to Detect Violations in Human-AI Conversations](https://mlsanity.com/paper/2601.07973/) (2026)
- [Language Models Exhibit Inconsistent Biases Towards Algorithmic Agents and Human Experts](https://mlsanity.com/paper/2602.22070/) (2026)
- [A Comparative Evaluation of Structural Topic Models and BERTopic for Short, Open-Ended Survey Responses](https://mlsanity.com/paper/2605.23093/) (2026)
- [Does Anthropomorphic Language Impact Public Perceptions of AI?](https://mlsanity.com/paper/2606.29121/) (2026)
- [Six misconceptions about large language models: A minimal model and diagnostic taxonomy](https://mlsanity.com/paper/2608.20421/) (2026)

## Abstract (arXiv)

How has the public responded to the increasing prevalence of artificial intelligence (AI)-based technologies? We investigate public perceptions of AI by collecting over 12,000 responses over 12 months from a nationally representative U.S. sample. Participants provided open-ended metaphors reflecting their mental models of AI, a methodology that overcomes the limitations of traditional self-reported measures by capturing more nuance. Using a mixed-methods approach combining quantitative clustering and qualitative coding, we identify 20 dominant metaphors shaping public understanding of AI. To analyze these metaphors systematically, we present a scalable framework integrating language modeling (LM)-based techniques to measure key dimensions of public perception: anthropomorphism (attribution of human-like qualities), warmth, and competence. We find that Americans generally view AI as warm and competent, and that over the past year, perceptions of AI's human-likeness and warmth have significantly increased (+34%, r = 0.80, p < 0.01; +41%, r = 0.62, p < 0.05). These implicit perceptions, along with the identified dominant metaphors, strongly predict trust in and willingness to adopt AI (r2 = 0.21, 0.18, p < 0.001). Moreover, we uncover systematic demographic differences in metaphors and implicit perceptions, such as the higher propensity of women, older individuals, and people of color to anthropomorphize AI, which shed light on demographic disparities in trust and adoption. In addition to our dataset and framework for tracking evolving public attitudes, we provide actionable insights on using metaphors for inclusive and responsible AI development.