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

TL;DR

This study analyzes public perceptions of AI in the U.S. through metaphor responses, revealing increasing warmth and human-likeness perceptions, demographic differences, and providing a scalable framework for tracking attitudes.

Contribution

Introduces a novel metaphor-based methodology combined with language modeling to systematically measure and analyze public perceptions of AI.

Findings

Perceptions of AI as warm and competent have increased over a year.

Implicit perceptions predict trust and willingness to adopt AI.

Demographic differences influence metaphors and perceptions, affecting trust.

Abstract

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 ($r^2 = 0.21, 0.18, p < 0.001$). 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.