Mitigating Interviewer Bias in Multimodal Depression Detection: An Approach with Adversarial Learning and Contextual Positional Encoding (2025)

Abstract: Clinical interviews are a standard method for assessing depression. Recent approaches have improved prediction accuracy by focusing on specific questions posed by the interviewer and manually selected question-answer (QA) pairs that target mental health content. However, these methods often neglect the broader conversational context, resulting in limited generalization and reduced robustness, particularly in less structured interviews, which are common in real-world clinical settings. In this work, we develop a multimodal dialogue-level transformer that captures the dynamics of dialogue within each interview by using a combination of sequential positional embedding and question context vectors. In addition to the depression prediction branch, we build an adversarial classifier with a gradient reversal layer to learn shared representations that remain invariant to the types of questions asked during the interview. This approach aims to reduce biased learning and improve the fairness and generalizability of depression detection in diverse clinical interview scenarios. Classification and regression experiments conducted on three real-world interview-based datasets and one synthetic dataset demonstrate the robustness and generalizability of our model.

## introduction major depressive disorder, commonly known as depression, is a prevalent mental health condition that can have severe consequences, including emotional distress, social withdrawal, and even suicide. the world health organization (who) foot_0 reports that more than 300 million people around the world are affected by depression, which significantly affects individuals, families, and society as a whole. unfortunately, in many communities, factors such as lack of awareness and financial constraints lead to underdiagnosis and undertreatment of depression and other mental health issues @xcite . the clinical interview is the standard method for evaluating depressive symptoms @xcite . each interview consists of a sequence of questionand-answer (qa) pairs between the interviewer and the participant. in the past decade, multimodal approaches that incorporate multiple data sources, such as audio, transcribed text, and video collected during interviews, have shown improved performance compared to unimodal methods @xcite @xcite . recent multimodal studies have shown that the incorporation of the interviewer's question as an additional text modality can improve depression detection @xcite @xcite . however, the high accuracy reported may stem from models learning interviewer question patterns rather than participant responses @xcite . for example, a follow-up question such as "has your mental health improved?" may reflect previous affirmative responses, providing indirect cues about the participant's condition. some prior work also uses manually selected qa pairs to distinguish depressed individuals @xcite , often ignoring the broader conversational context. figure 1 illustrates two excerpts from the same interview. in figure 1a , the exchange reveals clear depressive cues, whereas figure 1b is less informative-making the former easier for models to flag as depressed based on surface-level signals. therefore, despite the impressive performance metrics presented in previous work, we have significant concerns about the generalizability and robustness of these models on other interview-based speech datasets and real-world clinical situations, where interview questions tend to be more generic and participants may conceal their true feelings or, in some cases, exaggerate their symptoms @xcite @xcite . it is important that models do not rely on these misleading shortcuts for discrimination. in this paper, we tackle interviewer-induced bias and shortcut learning in depression detection from clinical interviews using a multimodal architecture that combines interviewer questions and participant responses with gated fusion. we introduce dialogue-based contextual positional encoding for improved understanding of dialogue turns and a lightweight dialogue transformer to capture interview dynamics. additionally, we apply an adversarial regularization strategy that uses large language model (llm) annotated interviewer question functions as targets for an adversarial classifier, and the classifier is paired with a gradient reversal layer. as a result, our model is trained to accurately predict depression while simultaneously reducing its dependence on potentially biased interviewer questioning patterns. the code has been released. 2 2 related work ## conclusion in this work, we developed a multimodal framework to enhance the detection of depression from clinical speech interview data. the primary goal was to ensure that the model learns meaningful representations of depression from the participants' responses, rather than relying on superficial cues from the interviewers' questions or overfitting to specific, manually-probed question-answer pairs, which can lead to misleading performance improvements. the results highlight the importance of explicitly modeling and addressing potential biases that may arise during the collection of clinical data. we hope that our work contributes to building a more reliable solution for developing fair and generalizable ai systems in mental health assessment.