DAIC - WOZ : On the Validity of Using the Therapist’s prompts in Automatic Depression Detection from Clinical Interviews (2024)

Abstract: Automatic depression detection from conversational data has gained significant interest in recent years.The DAIC-WOZ dataset, interviews conducted by a human-controlled virtual agent, has been widely used for this task.Recent studies have reported enhanced performance when incorporating interviewer’s prompts into the model.In this work, we hypothesize that this improvement might be mainly due to a bias present in these prompts, rather than the proposed architectures and methods.Through ablation experiments and qualitative analysis, we discover that models using interviewer’s prompts learn to focus on a specific region of the interviews, where questions about past experiences with mental health issues are asked, and use them as discriminative shortcuts to detect depressed participants. In contrast, models using participant responses gather evidence from across the entire interview.Finally, to highlight the magnitude of this bias, we achieve a 0.90 F1 score by intentionally exploiting it, the highest result reported to date on this dataset using only textual information.Our findings underline the need for caution when incorporating interviewers’ prompts into models, as they may inadvertently learn to exploit targeted prompts, rather than learning to characterize the language and behavior that are genuinely indicative of the patient’s mental health condition.

## introduction recent advances in artificial intelligence (ai) have increased the existing enthusiasm among medical professionals and clinicians when considering the potential for ai-based solutions to make mental healthcare more accessible and to reduce the burden of psychiatric institutions @xcite . this possibility has led some psychiatrists to argue that the use of ai might result in more standardized and objective measures of mental health @xcite . consequently, the automatic analysis of clinical interviews has been recognized as a promising direction for the development of automatic solutions that will help to improve the diagnostic consistency of depression detection @xcite @xcite . the distress analysis interview corpus -wizard of oz (daic-woz) dataset @xcite stands out as the most representative multimodal resource which has been commonly used for training and validating depression classification models within a clinical setup. most existing studies leverage the participant answers for depressive assessment, varying from single-modality methods, i.e., text transcripts, speech @xcite @xcite , to multi-modal approaches (text + speech + video) @xcite @xcite @xcite . however, recent studies that incorporate therapist's prompts during training, argue that such information works as supplementary context to better extract salient cues from participant answers @xcite @xcite , reporting high classification performances. in this paper, we investigate the validity of using the interviewer's prompts from the daic-woz dataset in automatic depression detection scenarios. we hypothesize that the reported results using both interviewer and participant information may be artificially inflated by a bias induced by the interviewer, failing to generalize to real-world scenarios where such biases may not exist. the impact of over-reporting performance in the daic-woz dataset has been already pointed by @xcite due to the presence of gender bias. nevertheless, and to the best of our knowledge, this is the first work to report the existence of a strong bias in the interviewer's prompts and to show that models can effectively exploit it as discriminative shortcuts. ## the daic-woz dataset the daic-woz dataset contains clinical interviews in north american english, performed by an animated virtual (human-controlled, i.e., wizard of oz) interviewer, called ellie, designed to support the diagnosis of different psychological distress conditions. the daic-woz stands as a valuable resource frequently utilized by the nlp community, attributed to its rigorous data collection methods and the scarcity of newer data sources exploring comparable phenomena. daic-woz is a multi-modal corpus, composed by audio and video recordings, and transcribed text from the interviews. to the date, the daic-woz corpus represents a unique and valuable resource, accumulating over 1k citations since its release. 1 ellie conducts semi-structured interviews that are intended to create interactional situations favorable to the assessment of distress indicators correlated with depression, anxiety or post-traumatic stress disorder (ptsd). theoretically, the advantage of ellie over a human interviewer is the implicit replicability and consistency of the prompts and accompanying gestures. thus, ellie has a finite repertoire of 191 prompts, i.e., general questions (what are you like when you don't get enough sleep?), neutral backchannels (uh huh), positive empathy (that's great), negative empathy (i'm sorry), surprise responses (wow!), continuation prompts (could you tell me more about that?), and miscellaneous prompts ( don't know; thank you). ## methodology to assess the reliability of using ellie's prompts for automatic depression detection on daic-woz, we first examine some of the highest results reported in the recent past using this dataset, summarized in speaker partition voc. size avg. #words avg. #tokens ellie (e) train 232 190.3 (@xmath0.9) 567.2 (@xmath1.10) eval 216 184.8 (@xmath2.2) 540.7 (@xmath3.5) participant (p) train 5858 621.1 (@xmath4.2) 1606.2 (@xmath5.9) eval 3268 664.2 (@xmath6.7) 1756.3 (@xmath7.7) in particular, we will conduct an ablation experiment using two models: a strong bert-based baseline model and the graph convolutional network (gcn) model described in @xcite , which is the best-performing model that relies solely on the participant's text (see table2). the choice of these two models aims to compare the baselines against the best-performing model, as well as to analyze models with different natures, namely a bidirectional sequential model and a sequence-agnostic one. moreover, as will be described below, the gcn model has an attractive interpretability property that we will use in section 5 for the qualitative analysis. thus, by analyzing the differences between these two models, we can determine whether the observed patterns hold independently of the model's nature. the models are described as follows: ## experiments and results finally, we performed a simple voting ensemble between the two variants of each model, denoted using the "and" symbol (∧). participants are classified as positive (i.e., in the depression group) only when both variants, ellie and participant, classify them as positive. as shown in ## analysis and discussion overall, experimental results suggest that ellie's prompts contain information that the models can exploit to more easily classify the participants. this is reasonable when considering that therapists adjust their questioning patterns based on the subjects' responses and may adapt their inquiries to delve deeper into specific aspects when detecting potential depressive symptoms. to explore this possibility further, as mentioned in section 3, we leveraged the gcn-based model's ability to learn a common representation for both participant and word nodes in the same output space. firstly, we extracted the words that both gcn models learned to use to identify the depressed group, which we will refer to as keywords. 4 subsequently, we analyzed the distribution of these keywords throughout the progression of each interview to contrast the depressed group against the control group, allowing us to visualize how easily distinguishable the two groups are from the perspectives of both ellie (e-gcn) and the participant (p-gcn) models. figure 1 illustrates the distributions obtained from our analysis, highlighting the contrasting behavior of the e-gcn and p-gcn models. the p-gcn distribution exhibits variability across interviews, with no distinct pattern emerging from the distribution of keywords. in contrast, the e-gcn model displays a clear and consistent pattern, with concrete regions where keywords concentrate. that is, the participant model gathers evidence from various parts of the conversations, whereas ellie's model focuses mainly on very specific segments, i.e. specific questions, to classify the participants. furthermore, by contrasting the distributions for the depressed group against the control group, we observe that it is easier to distinguish between them using e-gcn than p-gcn. this suggests that ellie's keywords are not only more localized but also possess greater discriminatory power. note that for e-gcn, in contrast with the control group, almost all the interviews in the depressed group have colored regions, and they are mostly concentrated in a single segment that appears after halfway the interviews. 5 interestingly, most of these segments correspond to a phase in the interview where ellie begins to ask more personal questions about past experiences with mental health issues. figure 2 shows one such segment. here, we see the segment containing the only four questions that ellie's model used to classify the participant, disregarding everything else in the conversation, including the question "have you been diagnosed with depression?" note that such questions may be asked to different participants, but an affirmative answer triggered ellie to delve deeper into specific questions, questions that models could easily learned to identify and exploit to correctly classify the participants. ## conclusions our analysis reveals that the prompts posed by the interviewer, ellie, contain biases that allow models to more easily distinguish between depressed and control participants in the daic-woz dataset. by analyzing the keywords learned by the models, we discover that ellie's model tends to focus on highly localized segments of the interviews, primarily concentrated in the latter portion where more personal mental health questions are asked. in contrast, the model using participant responses alone does not exhibit such localization, instead gathering evidence from across the entire conversations. more broadly, our findings underline the need for caution when incorporating interviewers' prompts into mental health diagnostic models. interviewers often strategically adapt their questioning to probe for potential symptoms. as a result, models may learn to exploit these targeted prompts as discriminative shortcuts, rather than learning to characterize the language and behavior that are truly indicative of mental health conditions.