[2603.24651] When Consistency Becomes Bias: Interviewer Effects in Semi-Structured Clinical Interviews

Computer Science > Computation and Language arXiv:2603.24651 (cs) [Submitted on 25 Mar 2026] Title:When Consistency Becomes Bias: Interviewer Effects in Semi-Structured Clinical Interviews Authors:Hasindri Watawana, Sergio Burdisso, Diego A. Moreno-Galván, Fernando Sánchez-Vega, A. Pastor López-Monroy, Petr Motlicek, Esaú Villatoro-Tello View a PDF of the paper titled When Consistency Becomes Bias: Interviewer Effects in Semi-Structured Clinical Interviews, by Hasindri Watawana and 6 other authors View PDF HTML (experimental) Abstract: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. Models trained on interviewer turns exploit fixed prompts and positions to distinguish depressed from control subjects, often achieving high classification scores without using participant language. Restricting models to participant utterances distributes decision evidence more broadly and reflects genuine linguistic cues. While semi-structured protocols ensure consistency, including interviewer prompts inflates performance by leveraging script artifacts. Our results highlight a cross-dataset, architecture-agnostic bias a...