Key question New
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When and why do people choose to disclose or hide their use of AI in personal messages?
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Key findings
- Disclosure decisions vary across two primary dimensions: communication type (functional vs. intimate) and receiver type (distant vs. close).
- For functional tasks with distant contacts (e.g., drafting a customer complaint), AI disclosure is seen as irrelevant because outcome efficiency outweighs personal authorship.
- For functional collaboration with close contacts (e.g., trip planning), disclosure is considered permissible and easily contextualized without social penalties.
- In intimate communication with distant contacts (e.g., early dating messages), disclosure is precarious because receivers lack context and may question the sender's genuine voice.
- In intimate communication with close contacts, particularly during relationship conflicts, disclosure is improbable because senders fear it will signal reduced personal investment and erode perceived sincerity.
What it means for you
CIO / IT Executive
Allocate budget to evaluate and procure enterprise-grade writing assistants that feature granular, context-aware AI disclosure toggles for employees.
IT Manager
Draft an internal team guideline mapping out where AI use is encouraged (functional tasks with distant contacts) versus restricted (intimate messaging with close colleagues).
Business Strategist
Review customer communication workflows to identify high-volume functional touchpoints where AI drafting can be deployed without requiring transparency disclosures.
Researcher
Draft a new qualitative study proposal investigating the longitudinal impact of AI disclosure on trust erosion specifically within high-stakes interpersonal conflicts.
Policymaker
Draft a briefing note evaluating the need for consumer protection guidelines regarding voluntary AI disclosure in digital dating and personal communication platforms.
Summary
This exploratory qualitative study investigates how interpersonal contexts and relationship types shape decisions to disclose AI assistance in private messages. Drawing on five semi-structured interviews with active AI users, the authors analyze how senders evaluate the need for AI transparency across different social situations.
Problem
While formal rules dictate AI disclosure in academic and business settings, private communication relies entirely on voluntary disclosure where norms remain undefined. Communicators struggle to balance the convenience of AI assistance with the risk of being perceived as inauthentic, lazy, or insincere if disclosure occurs.
Transcript
Expert: And I'm Alex Ian Sutherland.
Host: Today, we are discussing a study presented at the International Conference on Information Systems, or ICIS, 2026, titled "Should I Tell? Navigating AI Usage Disclosure in AI-Supported Interpersonal Communication." The study is authored by Johannes Bernard, Moritz Gimnich, Christoph Weinert, and Tim Weitzel. Alex, what is the core question behind this work?
Expert: The study examines how individuals decide whether to disclose their use of artificial intelligence when sending private, interpersonal messages. While AI tools are frequently used to draft or polish text, the decision to inform the recipient remains largely voluntary in everyday communication.
Host: In academic and corporate environments, transparency is often dictated by formal policies or compliance guidelines. But in private interactions, there are no institutional rules. What tension does that create for users?
Expert: Without formal rules, communicators face a social dilemma. Messages are not merely vehicles for information; they also signal personal effort, care, and authenticity. Senders worry that disclosing AI support might lead recipients to question their sincerity or view them as taking shortcuts. At the same time, leaving AI assistance hidden risks negative reactions if the recipient finds out.
Host: How did the researchers investigate this dynamic?
Expert: They conducted an exploratory qualitative study using semi-structured interviews with five participants aged twenty-one to thirty-five. The sample included two students, a researcher, a process manager, and a pilot, all of whom had prior experience using AI in interpersonal communication. The authors analyzed the interview data using open and axial coding procedures in MAXQDA to identify recurring relational patterns.
Host: What did the analysis reveal about how people decide to disclose AI usage?
Expert: The findings show that disclosure decisions hinge on two dimensions evaluated by the sender: the communication type, which ranges from functional to intimate, and the receiver type, which ranges from distant to close. Combining these dimensions produced four distinct patterns.
Host: Let us look at the first pattern. What happens in functional communication with a distant receiver?
Expert: That covers situations like using AI to write a clear, polite complaint to a customer service agent. Here, disclosure was viewed as irrelevant. The interaction centers on task completion rather than personal authorship, so AI assistance does not alter the social meaning of the message.
Host: And what about functional communication with close receivers?
Expert: In the second pattern, such as using AI to brainstorm ideas or plan an itinerary with close friends, disclosure was seen as permissible. Because close contacts already understand the sender's capabilities and intent, sharing that AI helped with coordination carries little social risk.
Host: Moving to intimate communication, how do people handle distant contacts?
Expert: That is the third pattern, where disclosure becomes precarious. An example is using AI to help formulate a sensitive message to someone in an early dating phase. While AI provides expressive support, the lack of an established relationship means the recipient might doubt whether the message reflects genuine emotion, making disclosure fraught with uncertainty.
Host: And the final pattern involves intimate communication with close contacts.
Expert: In this fourth pattern, disclosure was seen as improbable, especially during relationship conflicts. Senders feared that revealing AI assistance would suggest a lack of personal investment, weakening the sincerity of their response and undermining the legitimacy of their stance.
Host: What are the broader takeaways here for professionals and organizations?
Expert: It highlights that transparency is not a simple binary choice between hiding or revealing technology. Even in business contexts, much of our day-to-day interaction relies on interpersonal relationships rather than rigid rulebooks. Understanding that perceived effort and authenticity vary depending on relational distance and task type helps organizations better appreciate the social complexities of AI adoption in peer communication.
Host: AI transparency operates not just between users and systems, but as a relational judgment between people. Thank you, Alex, for walking us through this study.
Expert: My pleasure, Anna.
Host: And thank you for listening to A.I.S. Insights — powered by Living Knowledge. Join us next time for more research-driven discussions.
AI usage, disclosure, interpersonal communication, transparency, user behavior