Blissful (A)Ignorance: People form overly positive impressions of others based on their written messages, despite wide-scale adoption of Generative AI
As the use of Generative AI (GenAI) tools becomes more prevalent in interpersonal communication, understanding their impact on social perceptions is crucial. According to signaling theory, GenAI may undermine the credibility of social signals conveyed in writing, since it reduces the cost of writing and makes it hard to verify the authenticity of messages. Using a pre-registered large-scale online experiment (N = 647; Prolific), featuring scenarios in a range of communication contexts (personal vs. professional; close others vs. strangers), we explored how senders' use of GenAI influenced recipients' impressions of senders, both when GenAI use was known or uncertain. Consistent with past work, we found strong negative effects on social impressions when disclosing that a message was AI-generated, compared to when the same message was human-written. However, under the more realistic condition when potential GenAI use was not explicitly highlighted, recipients did not exhibit any skepticism towards senders, and these "uninformed" impressions were virtually indistinguishable from those of fully human-written messages. Even when we highlighted the potential (but uncertain) use of GenAI, recipients formed overly positive impressions. These results are especially striking given that 46% of our sample admitted having used such tools for writing messages, just within the past two weeks. Our findings put past work in a new light: While social judgments can be substantially affected when GenAI use is explicitly disclosed, this information may not be readily available in more realistic communication settings, making recipients blissfully ignorant about others' potential use of GenAI.
Code (0)
등록된 구현이 없습니다.
Tasks
FormSimilar Papers 제목 키워드 기반
Language Generation with Replay: A Learning-Theoretic View of Model Collapse
As scaling laws push the training of frontier large language models (LLMs) toward ever-growing data requirements, training pipelines are approaching a regime where much of the publicly available online text may be consum…
Measuring Knowledge for Recognition and Knowledge Entropy
People employ their knowledge to recognize things. This paper is concerned with how to measure people's knowledge for recognition and how it changes. The discussion is based on three assumptions. Firstly, we construct tw…
Geometry-Calibrated Conformal Abstention for Language Models
When language models lack relevant knowledge for a given query, they frequently generate plausible responses that can be hallucinations, rather than admitting being agnostic about the answer. Retraining models to reward …
Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction
A proven therapeutic technique to overcome negative thoughts is to replace them with a more hopeful "reframed thought." Although therapy can help people practice and learn this Cognitive Reframing of Negative Thoughts, c…
In-Context LearningLanguage ModelingLanguage ModellingRetrievalShiny Stories, Hidden Struggles: Investigating the Representation of Disability Through the Lens of LLMs
Modern Large Language Models (LLMs) have recently attracted much attention for their ability to simulate human behavior and generate text that reflects personas and demographic groups. While these capabilities can open u…