AI RESEARCH
When Consistency Becomes Bias: Interviewer Effects in Semi-Structured Clinical Interviews
arXiv CS.AI
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ArXi:2603.24651v1 Announce Type: cross Automatic depression detection from doctor-patient conversations has gained momentum thanks to the availability of public corpora and advances in language modeling. However, interpretability remains limited: strong performance is often reported without revealing what drives predictions. We analyze three datasets: ANDROIDS, DAIC-WOZ, E-DAIC and identify a systematic bias from interviewer prompts in semi-structured interviews.