IS Implementation & Adoption
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Paper Number
1928
Paper Type
short
Description
Depression has become a global medical crisis. Barriers for effective depression treatment include shortage of medical professionals, inaccurate assessment, and social stigma towards depression. Leveraging artificial intelligence (AI) system in depression treatment is a possible way to remove these barriers. However, AI system has its limitations such as assessment bias. Hence, in this short paper, we propose integrating AI and human intelligence to create a task assembly AI-human hybrid for depression treatment. We then develop a research model to assess users’ preference with three service agents (i.e., human physicians, AI system, and AI-human hybrid) in terms of privacy concern and trust. We also argue that social stigma plays a moderating role in users’ service agent preference. Further, we examine the underlying mechanisms that form the users’ intention to use a certain service agent. This paper can have significant theoretical and practical implications for AI implementation in mental healthcare setting.
Recommended Citation
Yan, Aihua and Xu, David, "AI-human Hybrid for Depression Treatment: The Moderating Role of Social Stigma" (2021). ICIS 2021 Proceedings. 8.
https://aisel.aisnet.org/icis2021/is_implement/is_implement/8
AI-human Hybrid for Depression Treatment: The Moderating Role of Social Stigma
Depression has become a global medical crisis. Barriers for effective depression treatment include shortage of medical professionals, inaccurate assessment, and social stigma towards depression. Leveraging artificial intelligence (AI) system in depression treatment is a possible way to remove these barriers. However, AI system has its limitations such as assessment bias. Hence, in this short paper, we propose integrating AI and human intelligence to create a task assembly AI-human hybrid for depression treatment. We then develop a research model to assess users’ preference with three service agents (i.e., human physicians, AI system, and AI-human hybrid) in terms of privacy concern and trust. We also argue that social stigma plays a moderating role in users’ service agent preference. Further, we examine the underlying mechanisms that form the users’ intention to use a certain service agent. This paper can have significant theoretical and practical implications for AI implementation in mental healthcare setting.
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