Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.09645 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908927224971264 |
|---|---|
| author | Khadpe, Pranav Wenzel, Kimi Loewenstein, George Kaufman, Geoff |
| author_facet | Khadpe, Pranav Wenzel, Kimi Loewenstein, George Kaufman, Geoff |
| contents | When someone sends us a thoughtful message, we naturally form judgments about their character. But what happens when that message carries a label indicating it was written with the help of AI? This paper investigates how the appearance of AI assistance affects our perceptions of message senders. Adding nuance to previous research, through two studies (N=399) featuring vignette scenarios, we find that AI-assistance labels don't necessarily make people view senders negatively. Rather, they dampen the strength of character signals in communication. We show that when someone sends a warmth-signalling message (like thanking or apologizing) without AI help, people more strongly categorize the sender as warm. At the same time, when someone sends a coldness-signalling message (like bragging or blaming) without assistance, people more confidently categorize them as cold. Interestingly, AI labels weaken both these associations: An AI-assisted apology makes the sender appear less warm than if they had written it themselves, and an AI-assisted blame makes the sender appear less cold than if they had composed it independently. This supports our signal diagnosticity explanation: messages labeled as AI-assisted are viewed as less diagnostic than messages which seem unassisted. We discuss how our findings shed light on the causal origins of previously reported observations in AI-Mediated Communication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09645 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explaining the Reputational Risks of AI-Mediated Communication: Messages labeled as AI-assisted are viewed as less diagnostic of the sender's moral character Khadpe, Pranav Wenzel, Kimi Loewenstein, George Kaufman, Geoff Human-Computer Interaction Computers and Society Emerging Technologies When someone sends us a thoughtful message, we naturally form judgments about their character. But what happens when that message carries a label indicating it was written with the help of AI? This paper investigates how the appearance of AI assistance affects our perceptions of message senders. Adding nuance to previous research, through two studies (N=399) featuring vignette scenarios, we find that AI-assistance labels don't necessarily make people view senders negatively. Rather, they dampen the strength of character signals in communication. We show that when someone sends a warmth-signalling message (like thanking or apologizing) without AI help, people more strongly categorize the sender as warm. At the same time, when someone sends a coldness-signalling message (like bragging or blaming) without assistance, people more confidently categorize them as cold. Interestingly, AI labels weaken both these associations: An AI-assisted apology makes the sender appear less warm than if they had written it themselves, and an AI-assisted blame makes the sender appear less cold than if they had composed it independently. This supports our signal diagnosticity explanation: messages labeled as AI-assisted are viewed as less diagnostic than messages which seem unassisted. We discuss how our findings shed light on the causal origins of previously reported observations in AI-Mediated Communication. |
| title | Explaining the Reputational Risks of AI-Mediated Communication: Messages labeled as AI-assisted are viewed as less diagnostic of the sender's moral character |
| topic | Human-Computer Interaction Computers and Society Emerging Technologies |
| url | https://arxiv.org/abs/2509.09645 |