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Introduction: Machine learning (ML) algorithms have been heralded as promising solutions to the realization of assistive systems in digital healthcare, due to their ability to detect fine-grain patterns that are not easily perceived by humans. Yet, ML algorithms have also been critiqued for treating individuals differently based on their demography, thus propagating existing disparities. This paper explores gender and race bias in speech-based ML algorithms that detect behavioral and mental health outcomes., Methods: This paper examines potential sources of bias in the data used to train the ML, encompassing acoustic features extracted from speech signals and associated labels, as well as in the ML decisions. The paper further examines approaches to reduce existing bias via using the features that are the least informative of one's demographic information as the ML input, and transforming the feature space in an adversarial manner to diminish the evidence of the demographic information while retaining information about the focal behavioral and mental health state., Results: Results are presented in two domains, the first pertaining to gender and race bias when estimating levels of anxiety, and the second pertaining to gender bias in detecting depression. Findings indicate the presence of statistically significant differences in both acoustic features and labels among demographic groups, as well as differential ML performance among groups. The statistically significant differences present in the label space are partially preserved in the ML decisions. Although variations in ML performance across demographic groups were noted, results are mixed regarding the models' ability to accurately estimate healthcare outcomes for the sensitive groups., Discussion: These findings underscore the necessity for careful and thoughtful design in developing ML models that are capable of maintaining crucial aspects of the data and perform effectively across all populations in digital healthcare applications., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Yang, El-Attar and Chaspari.)

Introduction: Intelligent ambulatory tracking can assist in the automatic detection of psychological and emotional states relevant to the mental health changes of professionals with high-stakes job responsibilities, such as healthcare workers. However, well-known differences in the variability of ambulatory data across individuals challenge many existing automated approaches seeking to learn a generalizable means of well-being estimation. This paper proposes a novel metric learning technique that improves the accuracy and generalizability of automated well-being estimation by reducing inter-individual variability while preserving the variability pertaining to the behavioral construct., Methods: The metric learning technique implemented in this paper entails learning a transformed multimodal feature space from pairwise similarity information between (dis)similar samples per participant via a Siamese neural network. Improved accuracy via personalization is further achieved by considering the trait characteristics of each individual as additional input to the metric learning models, as well as individual trait base cluster criteria to group participants followed by training a metric learning model for each group., Results: The outcomes of the proposed models demonstrate significant improvement over the other inter-individual variability reduction and deep neural baseline methods for stress, anxiety, positive affect, and negative affect., Discussion: This study lays the foundation for accurate estimation of psychological and emotional states in realistic and ambulatory environments leading to early diagnosis of mental health changes and enabling just-in-time adaptive interventions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Paromita, Mundnich, Nadarajan, Booth, Narayanan and Chaspari.)