There is limited research on fairness in automated decision-making systems in the clinical domain, particularly in the mental health domain. Our study explores clinicians' perceptions of AI fairness through two distinct scenarios: violence risk assessment and depression phenotype recognition using textual clinical notes. We engage with clinicians through semi-structured interviews to understand their fairness perceptions and to identify appropriate quantitative fairness objectives for these scenarios. Then, we compare a set of bias mitigation strategies developed to improve at least one of the four selected fairness objectives. Our findings underscore the importance of carefully selecting fairness measures, as prioritizing less relevant measures can have a detrimental rather than a beneficial effect on model behavior in real-world clinical use.

Word embeddings, renowned for their role as superior semantic feature vector representation in diverse NLP tasks, can exhibit an undesired bias for stereotypical categories. The bias arises from the statistical and societal biases within the datasets used for training. In this study, we analyze the gender bias in four different pre-trained word embeddings for a range of affective computing tasks in the mental health domain including the detection of psychiatric disorders such as depression, and alcohol/substance abuse. We incorporate both contextual and non-contextual embeddings, which are trained not just on general domain data but also on data specific to the clinical domain. Our findings indicate that the bias in embeddings is towards different gender groups, depending on the type of embeddings and the training dataset. Furthermore, we highlight how these existing associations transfer to subsequent tasks and might even be amplified during supervised training for patient phenotyping. We also show that a simple method of data augmentation- swapping gender words - noticeably reduces bias in these subsequent tasks. The scripts to reproduce the results are available at: https:llgithub.comlgizemsogancioglulgender-bias-mental-health.

Mood disorders in general and depression in particular are common and their impact on individuals and society is high. Roughly 5% of adults worldwide suffer from depression. Commonly, depression diagnosis involves using questionnaires, either clinician-rated or self-reported. Due to the subjectivity in questionnaire methods and high human-related costs involved, there are ongoing efforts to find more objective and easily attainable depression markers. As is the case with recent audio, visual and linguistic applications, state-of-the-art approaches for automated depression severity prediction heavily depend on deep learning and black box modeling without explainability and interpretability considerations. However, for reasons ranging from regulations to understanding the extent and limitations of the model, the clinicians need to understand the decision making process of the model to confidently form their decisions. In this work, we focus on text-based depression severity level prediction on DAIC-WOZ corpus and benefit from PHQ-8 questionnaire items to predict the symptoms as interpretable high level features. We show that using a multi-task regression approach with state-of-the-art text-based features to predict the depression symptoms, it is possible to reach a viable test set Concordance Correlation Coefficient performance comparable to the state-of-the-art systems.

First impressions play a critical role in shaping social interactions and consequently have a high impact on people’s lives. This study presents an explainable system that models apparent personality traits that influence first impressions as a function of automatically predicted arousal, valence and likeability (AVL) scores. To this end, we enrich the ChaLearn Looking at People - First Impressions (LAP-FI) dataset by annotating a portion of it for the AVL dimensions and carry out extensive uni-modal and multimodal experiments by using state-of-the-art acoustic, visual and linguistic features. We propose to use a glass-box model, namely, Explainable Boosting Machine, to model the Big Five personality traits. Our results demonstrate that personality trait impressions can be effectively predicted through the mood and likeability scores of a given video. We show that the proposed model, which is trained on only a few features, not only provides more meaningful explanations but also yields competitive performance (with a 0.09 Mean Absolute Error) compared to the state-of-the-art methods. The annotated benchmark dataset and the scripts to reproduce the results are available at: https://github.com/gizemsogancioglu/mood-project.

Acoustic and linguistic analysis for elderly emotion recognition is an under-studied and challenging research direction, but essential for the creation of digital assistants for the elderly, as well as unobtrusive telemonitoring of elderly in their residences for mental healthcare purposes. This paper presents our contribution to the INTERSPEECH 2020 Computational Paralinguistics Challenge (ComParE) - Elderly Emotion Sub-Challenge, which is comprised of two ternary classification tasks for arousal and valence recognition. We propose a bi-modal framework, where these tasks are modeled using state-of-the-art acoustic and linguistic features, respectively. In this study, we demonstrate that exploiting task-specific dictionaries and resources can boost the performance of linguistic models, when the amount of labeled data is small. Observing a high mismatch between development and test set performances of various models, we also propose alternative training and decision fusion strategies to better estimate and improve the generalization performance.

MOTIVATION: The amount of information available in textual format is rapidly increasing in the biomedical domain. Therefore, natural language processing (NLP) applications are becoming increasingly important to facilitate the retrieval and analysis of these data. Computing the semantic similarity between sentences is an important component in many NLP tasks including text retrieval and summarization. A number of approaches have been proposed for semantic sentence similarity estimation for generic English. However, our experiments showed that such approaches do not effectively cover biomedical knowledge and produce poor results for biomedical text. METHODS: We propose several approaches for sentence-level semantic similarity computation in the biomedical domain, including string similarity measures and measures based on the distributed vector representations of sentences learned in an unsupervised manner from a large biomedical corpus. In addition, ontology-based approaches are presented that utilize general and domain-specific ontologies. Finally, a supervised regression based model is developed that effectively combines the different similarity computation metrics. A benchmark data set consisting of 100 sentence pairs from the biomedical literature is manually annotated by five human experts and used for evaluating the proposed methods. RESULTS: The experiments showed that the supervised semantic sentence similarity computation approach obtained the best performance (0.836 correlation with gold standard human annotations) and improved over the state-of-the-art domain-independent systems up to 42.6% in terms of the Pearson correlation metric. AVAILABILITY AND IMPLEMENTATION: A web-based system for biomedical semantic sentence similarity computation, the source code, and the annotated benchmark data set are available at: http://tabilab.cmpe.boun.edu.tr/BIOSSES/ . CONTACT: [email protected] or [email protected].