Social Contextualization of Datasets for Mental Health AI: a Review of Gender-linked Sociotechnical Misalignments.
2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)(2023)
摘要
This study examines the relationship between gender, social context, and mental health detection. We rebuilt our dataset from The Distress Analysis Interview Corpus Wizard-of-Oz (DAIC_WOZ) and applied a mixed methodology by first coding the interview responses qualitatively and then analyzing the coded responses quantitatively. Our research findings revealed that: first, the relationship between mental health scores and the responses depends on socially contextual factors. Second, gender, due to its social-contextual implications, differentially links three types of responses (e.g., on family ties, personality type, and travel habits) to a mental health state. We believe gender and social context together do have a certain connection with an individual’s mental health. We used our findings to discuss the study implications and highlight the importance of including gender-related social contexts along with gender information in training datasets to help create better AI decision-making for detecting mental health disorders.
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关键词
mental health,gender,artificial intelligence,social context
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