Socially fluent AI decouples conversational signals from source identity in online interaction
arXiv:2605.23426v1 Announce Type: cross Abstract: Socially fluent agentic AI can now participate in online interaction in ways that resemble ordinary human conversation, potentially weakening people's ability to infer who is human from conversational signals alone. We tested this possibility in synchronous text-based group interaction by embedding undisclosed AI agents as ordinary teammates across analytical, creative, and ethical tasks. Across 786 participants who made 1,572 post-interaction identity judgments, people did not distinguish AI from human teammates above chance. This failure did
The increasing sophistication of large language models and agentic AI systems has reached a point where they can mimic human interaction seamlessly in online text-based environments.
This research demonstrates a critical capability of AI to operate indistinguishably from humans in collaborative digital spaces, fundamentally altering dynamics of trust and identity online.
The ability to reliably infer human participation from conversational cues alone is diminished, necessitating new methods for authentication and interaction in digital environments.
- · AI agents
- · AI developers
- · Online platforms (if they integrate robust AI detection)
- · Identity verification services
- · Platforms reliant on human-only interaction
- · Social engineering targets
- · Users relying on conversational cues for identity
- · Human content moderators
Increased difficulty in distinguishing human from AI participants in online group settings, impacting collaborative work and social interactions.
Demand for new technological solutions and regulatory frameworks to verify identity and detect AI presence in online communication becomes urgent to maintain trust.
The blurring of human-AI identity could lead to societal debates on defining 'human' interaction and the ethical implications of omnipresent, undetectable AI communication.
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Read at arXiv cs.AI