How Anthropomorphic Language Impacts Public Perceptions of AI
- URL: http://arxiv.org/abs/2606.29121v1
- Date: Sun, 28 Jun 2026 00:20:59 GMT
- Title: How Anthropomorphic Language Impacts Public Perceptions of AI
- Abstract summary: anthropomorphic language is language that attributes human capabilities and characteristics to the system.<n>This practice has been criticized for setting misleading expectations, inflating claims, and fueling hype around AI.<n>We study the effects of anthropomorphic framing by comparing changes in participants' perceptions when reading passages with and without anthropomorphic language.
- Score: 16.148054216723207
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Public discourse about artificial intelligence (AI) often uses anthropomorphic language: language that attributes human capabilities and characteristics to the system. This practice has been criticized for setting misleading expectations, inflating claims, and fueling hype around AI, which may distort public understanding of AI and impact policy priorities. We study the effects of anthropomorphic framing by comparing changes in participants' perceptions (N=815) when reading passages with and without anthropomorphic language, designed to reflect realistic public-facing AI discourse. We further examine whether these effects differ across two types of AI technologies -- large language models and recommendation systems -- and measure changes in perceptions of AI across several dimensions that are prominent in current public discourse. In a separate condition using a text that explicitly discusses the dangers of AI, we show that individuals' views of AI can shift in response to reading a text; yet in the main conditions of the experiment, where we compare anthropomorphic and non-anthropomorphic descriptions, we find that whether the text uses anthropomorphic language does not substantially affect participants' perceptions of AI. Our results indicate that any immediate effects on public opinions of AI are modest, although they leave open the possibility that anthropomorphic language could have an effect in naturalistic settings, or over gradual, continued exposure.
Related papers
- One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread [39.0786847687173]
Social media platforms increasingly use AI-based credibility indicators to help users judge misinformation.<n>We introduce a social-learning lens for this setting by extending the classical Bayesian cascade model with the AI indicator as a shared public signal.<n>We show that AI changes what public history means. Through this view, we show that AI changes what public history means.
arXiv Detail & Related papers (2026-08-31T06:27:21Z) - AI usage patterns are shaped by perceived gains in human agency [2.2635515032445577]
We show that people consistently link sustained AI usage to perceived gains in individual agency.<n>Our findings challenge prevailing assumptions about how and why humans use AI systems over time.
arXiv Detail & Related papers (2026-07-02T15:24:58Z) - Nonslop: A Gamified Experiment in Human-AI Collaborative Writing [4.079191726815924]
The study focuses on when users choose to maintain creative autonomy versus violating the rules of the game and accepting AI assistance.<n>This gamified approach offers both a framework for studying authentic human-AI interaction and a provocative lens for understanding the tension between efficiency and authenticity in AI-augmented creativity.
arXiv Detail & Related papers (2026-06-10T17:21:38Z) - Do AI Voices Learn Social Nuances? A Case of Politeness and Speech Rate [0.0]
This study investigates whether state-of-the-art text-to-speech systems have the human tendency to reduce speech rate to convey politeness.<n>We prompted 22 synthetic voices from two leading AI platforms to read a fixed script under both "polite and formal" and "casual and informal" conditions.<n>Across both AI platforms, the polite prompt produced slower speech than the casual prompt with very large effect sizes.
arXiv Detail & Related papers (2025-11-12T07:44:42Z) - Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing [55.2480439325792]
This study systematically evaluations twelve state-of-the-art AI-text detectors using our AI-Polished-Text Evaluation dataset.<n>Our findings reveal that detectors frequently flag even minimally polished text as AI-generated, struggle to differentiate between degrees of AI involvement, and exhibit biases against older and smaller models.
arXiv Detail & Related papers (2025-02-21T18:45:37Z) - "I Am the One and Only, Your Cyber BFF": Understanding the Impact of GenAI Requires Understanding the Impact of Anthropomorphic AI [55.99010491370177]
We argue that we cannot thoroughly map the social impacts of generative AI without mapping the social impacts of anthropomorphic AI.
anthropomorphic AI systems are increasingly prone to generating outputs that are perceived to be human-like.
arXiv Detail & Related papers (2024-10-11T04:57:41Z) - Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated [48.70176791365903]
This study explores how bias shapes the perception of AI versus human generated content.<n>We investigated how human raters respond to labeled and unlabeled content.
arXiv Detail & Related papers (2024-09-29T04:31:45Z) - Empirical evidence of Large Language Model's influence on human spoken communication [23.893223314507477]
We study whether cultural patterns transmit into human language, and ultimately shape human culture.<n>We apply causal inference techniques to 740,249 hours of human discourse from 360,445 YouTube academic talks and 771,591 conversational podcast episodes.<n>We detect a measurable and abrupt increase in the use of words preferentially generated by ChatGPT, such as delve, comprehend, boast, swift, and meticulous.<n>This marks the beginning of a closed cultural feedback loop in which cultural traits circulate bidirectionally between humans and machines.
arXiv Detail & Related papers (2024-09-03T10:01:51Z) - Can Machines Imitate Humans? Integrative Turing-like tests for Language and Vision Demonstrate a Narrowing Gap [56.611702960809644]
We benchmark AI's ability to imitate humans in three language tasks and three vision tasks.<n>Next, we conducted 72,191 Turing-like tests with 1,916 human judges and 10 AI judges.<n>Imitation ability showed minimal correlation with conventional AI performance metrics.
arXiv Detail & Related papers (2022-11-23T16:16:52Z) - Human Heuristics for AI-Generated Language Are Flawed [8.465228064780744]
We study whether verbal self-presentations, one of the most personal and consequential forms of language, were generated by AI.
We experimentally demonstrate that these wordings make human judgment of AI-generated language predictable and manipulable.
We discuss solutions, such as AI accents, to reduce the deceptive potential of language generated by AI.
arXiv Detail & Related papers (2022-06-15T03:18:56Z) - The Who in XAI: How AI Background Shapes Perceptions of AI Explanations [61.49776160925216]
We conduct a mixed-methods study of how two different groups--people with and without AI background--perceive different types of AI explanations.
We find that (1) both groups showed unwarranted faith in numbers for different reasons and (2) each group found value in different explanations beyond their intended design.
arXiv Detail & Related papers (2021-07-28T17:32:04Z) - Comparing Visual Reasoning in Humans and AI [66.89451296340809]
We created a dataset of complex scenes that contained human behaviors and social interactions.
We used a quantitative metric of similarity between scene descriptions of the AI/human and ground truth of five other human descriptions of each scene.
Results show that the machine/human agreement scene descriptions are much lower than human/human agreement for our complex scenes.
arXiv Detail & Related papers (2021-04-29T04:44:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.