AI-human Hybrid for Depression Treatment: The Moderating Role of Social Stigma

Aihua YAN, David XU

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of International Conference on Information Systems 2021
PublisherAssociation for Information Systems
Number of pages10
ISBN (Electronic)9781733632591
Publication statusPublished - 12 Dec 2021
Externally publishedYes
EventInternational Conference on Information Systems 2021 - Austin, United States
Duration: 12 Dec 202115 Dec 2021


ConferenceInternational Conference on Information Systems 2021
Abbreviated titleICIS 2021
Country/TerritoryUnited States


  • depression treatment
  • artificial intelligence
  • AI-human hybrid
  • privacy concern
  • trust


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