Understanding Social Requirements for Social Media Powered Artificial Intelligence (SOMPAI) for Mental Health Care

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Yoo, Dong Whi
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School of Interactive Computing
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Abstract
In the field of medicine, mental health is unique as it entirely depends on the patient’s ability to express their cognitive and emotional states, symptom progression, and interpersonal relationships. This reliance creates extra challenges for patients and results in less effective evaluations and treatments. Recent advancements in artificial intelligence (AI) have been proposed as a means to develop more objective criteria and evidence for mental health treatments, with AI technologies being viewed as the potential solution to critical issues in mental health care, such as delayed, inaccurate, and ineffective care delivery. To support mental health practices, researchers from computer science, mental health, and other related fields have collaborated on developing AI models. However, despite decades of effort, these advancements have not been successfully integrated into real-world mental health contexts. This discrepancy between AI research and mental health practices can be framed as the socio-technical gap. It represents the intellectual challenge arising from differences between what technologies can provide and what users want to achieve in social contexts. Researchers in Computer-Supported Cooperative Work (CSCW) have pointed out that a socio-technical gap exists because users possess diverse roles, tasks, and procedures in social contexts and can fluidly navigate between them. However, technologies often lack understanding and flexibility in supporting these changes. AI technologies currently face a similar socio-technical gap as they lack social requirements such as nuance, flexibility, and ambiguity tolerance. Emerging research in Human-AI Interaction has revealed more detailed accounts of the social requirements for AI models. These studies propose working closely with AI technology end-users to understand the requirements from their perspectives. Building on these research efforts, this dissertation investigates the social requirements for mental health AI technologies from the standpoint of patients and clinicians, identifying them and envisioning design implications for future AI technologies. Mental health AI technologies utilize various data types, such as electronic health records, sensor-based data, and social media data. This dissertation focuses on social media-powered AI (SoMPAI) as many mental health patients use social media for treatment and recovery purposes, including self-disclosure, help-seeking, and peer support. Social media data can be a valuable source for understanding the mental health of people who are active on social media, particularly adolescents and young adults, who are the primary target of mental health treatment. Mental health patients’ social media data contain a rich amount of written text used in psychoanalysis for several mental disorders. Social media data can also reflect patients’ social activities, which can be valuable to mental health clinicians who must infer patients’ daily social lives from their self-reports during consultations. Therefore, this dissertation aims to understand the social requirements for social media-powered AI from both clinicians’ and patients’ perspectives by utilizing human-centered design approaches and working closely with clinicians and patients to understand their expectations and concerns. This dissertation contributes to several domains: it expands the concept of social requirements and the socio-technical gap in CSCW as they relate to mental health AI technologies; it provides empirical evidence, including the perspectives of mental health patients and clinicians, on expectations and concerns regarding AI models, contributing to recent Human-AI Interaction research; and the design implications of this dissertation will help develop implementable mental health AI technologies that can support current mental health practices.
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2023-07-25
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