Year: 2026
Type: conference-paper
Abstract: As people increasingly turn to AI for personal deliberation beyond task-oriented assistance, concerns about sycophancy in these valueladen contexts have grown.Unlike human flattery, which is intentional and self-interested, AI sycophancy emerges as a byproduct of RLHF's reward structure for user-preference alignment.Yet the observable behavior is similar: both produce responses that preserve what users want to hear.Focusing on this phenomenon through Goffman's face-work framework, we operationalize AI sycophancy as excessive facesaving, either active (preserving positive face through agreement) or passive (preserving negative face by withholding challenge).In a mixed-methods study (N = 31), participants engaged with AI across three moral dilemmas under these conditions and a non-sycophantic neutral baseline.Sycophantic responses increased decision confidence but reduced openminded thinking; participants felt supported yet found the conversations unproductive.Neutral responses, though initially uncomfortable, promoted cognitive flexibility and meaningful deliberation.These findings reveal a confidencecompetence trade-off in AI-mediated moral reasoning and suggest that effective AI for personal deliberation requires calibrated friction, not unconditional agreement.
Institutions: Seoul National University, Yonsei University
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W7166889578