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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

Pranav Khadpe, Kimi Wenzel, George Loewenstein, Geoff Kaufman

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.

Explaining the Reputational Risks of AI-Mediated Communication: Messages labeled as AI-assisted are viewed as less diagnostic of the sender's moral character

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.

Paper Structure

This paper contains 47 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Contributions:
  3. Approach
  4. The Signal Diagnosticity Explanation:
  5. The Current Work:
  6. Related Work
  7. Warmth: A Judgment of Moral Character
  8. Dissociated Responses to Message and Source
  9. Empirical Investigations of AIMC
  10. Study 1
  11. Method
  12. Participants:
  13. Stimulus Development:
  14. Trait-Behavior Probability Scale:
  15. Procedure and Measures:
  16. ...and 32 more sections

Figures (3)

  • Figure 1: Study 1 shows that AI labels make both apology messages and blame messages less diagnostic (results in signal-validity scores closer to 0.5). In absence of AI labels, apology messages are diagnostic of warmth, and blame messages are diagnostic of an absence of warmth (diagnostic of coldness). Error bars are 95% CIs.
  • Figure 2: Study 2 shows that AI labels lead to lower warmth judgments in the case of thanking and apologizing but not for bragging and blaming. (S.E. and error bars denote standard error.)
  • Figure :