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From ally to algorithm: How disagreement shapes users’ engagement with AI
- Truvace record
- TRV-2026-2571raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Randomized controlled trial
- Sources
- 1 sourcePeer-reviewed
- sha256
- a770ea9c443a…check it
Advances in AI language capabilities have led people to use it for conversations about relationships, social dilemmas, and moral questions. However, AI chatbots are sycophantic; they tend to affirm what the user says, exacerbating confirmation bias and leading to overconfidence or distorted perceptions of reality. Based on these concerns, developers started addressing sycophancy problems, creating models more likely to disagree when necessary. Here, we investigated what happens when AI models disagree with users…
The problem
In a randomized experiment, participants rated a disagreeing AI model as less able to understand human emotions and more machine-like, did not lower their confidence when it disagreed, and were less willing to use it again.
What this doesn’t fix
Participants discussed hypothetical dilemmas with an AI instructed to agree or disagree, so the findings may not carry over to real personal decisions or to models that choose when to disagree.
Sources
1 on file- Computers in Human Behavior Reportsdoi:10.1016/j.chbr.2026.101252
Cite this record
v1Truvace Impact Record TRV-2026-2571, v1: “From ally to algorithm: How disagreement shapes users’ engagement with AI.” Truvace, 2026-11-12. /record/TRV-2026-2571 (accessed on the day you copy it). sha256 a770ea9c443a8cfe…
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