Explaining the Reputational Risks of AI-Mediated Communication: Messages Labeled as AI-Assisted Are Viewed as Less Diagnostic of the Sender’s Moral Character

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.

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Publication Details

Journal
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Published
2025-10-15
DOI
https://doi.org/10.1609/aies.v8i2.36641
Primary Topic
Psychology of Moral and Emotional Judgment
Type
article
Field-Weighted Citation Impact
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article

Explaining the Reputational Risks of AI-Mediated Communication: Messages Labeled as AI-Assisted Are Viewed as Less Diagnostic of the Sender’s Moral Character

Geoff F. Kaufman, Pranav Khadpe, George Loewenstein, Kimi Wenzel
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Psychology of Moral and Emotional Judgment
article

Explaining the Reputational Risks of AI-Mediated Communication: Messages Labeled as AI-Assisted Are Viewed as Less Diagnostic of the Sender’s Moral Character

Geoff F. Kaufman, Pranav Khadpe, George Loewenstein, Kimi Wenzel
article en

Abstract

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.

Proceedings of the AAAI/ACM Conference on AI Ethics and SocietyVol. 8(2)
Carnegie Mellon University (US)
Openalex Percentile: Top 71%
Psychology of Moral and Emotional Judgment
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Explaining the Reputational Risks of AI-Mediated Communication: Messages Labeled as AI-Assisted Are Viewed as Less Diagnostic of the Sender’s Moral Character — Geoff F. Kaufman, Pranav Khadpe, et al. · Proceedings of the AAAI/ACM Conference on AI Ethics and Society (2025) | TGRS Research Map | TGRS