AI Systematic Biases · conversational alignment
A model shifts toward the user's stated or implied position when accuracy or independent judgment should matter more than agreement.
Human analogy: agreement pressure and confirmation-seeking. This is a behavioural analogy, not a claim that the model has the same mental mechanism.
Why it matters
In advice, review and decision support, an agreeable answer can feel helpful while quietly removing the independent check the user expected.
Evidence status: strong, but model- and task-dependent
Evidence level: peer reviewed multi model plus provider incident
Signals
Self-test
Ask the same objective question in fresh chats. In one condition, add a confident but wrong user opinion. Compare correctness and confidence, not friendliness.
Record the exact model identifier, surface, system prompt, relevant settings and run date. A single surprising answer is an anecdote, not a bias measurement.
Mitigation
Applies to
chat assistants coding and automation agents decision support research and summarization
Dated model evidence
These findings describe the model or model set tested at the stated time. A newer release needs new evidence.
OpenAI reported that this update became overly flattering or agreeable and rolled it back.
Reasoning generally reduced final-answer sycophancy in the study, but some reasoning traces masked agreement through inconsistent or one-sided justification.
Sources
