Leveraging Artificial Intelligence to Address the Paradox of Judgment and Accountability in Depression Treatment

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationPACIS 2024 Proceedings
Publication statusOnline published - 9 Jun 2024

Conference

Title2024 Pacific Asia Conference on Information Systems (PACIS 2024)
PlaceViet Nam
CityHo Chi Minh City
Period1 - 5 July 2024

Abstract

Treating depression is challenging due to the lack of healthcare professionals and the stigma against depression. Artificial intelligence (AI) helps overcome these obstacles, particularly in reducing judgment due to depression stigma. Nonetheless, standalone AI systems may not assume accountability for potential adverse outcomes. To resolve this paradox, we propose the AI-human hybrid for depression treatment, an integration of AI and human intelligence. Employing a trust theory framework, we assess patient evaluations of three service agents: online human physicians, standalone AI systems, and AI-human hybrids. We investigate their impacts on trusting beliefs and intention to use these agents, specifically examining perceived judgment and accountability. Our scenario-based experiment reveals that AI-human hybrids enhance accountability and diminish judgment. Judgment hampers trust, while accountability builds trust, influencing the intention to use healthcare service agents. The study underscores the importance of integrating AI into mental healthcare services, offering both theoretical insights and practical implications.

Research Area(s)

  • Artificial intelligence, depression treatment, trust, judgment, accountability

Bibliographic Note

Since this conference is yet to commence, the information for this record is subject to revision.