Paper Number
ICIS2025-1865
Paper Type
Short
Abstract
Depression affects approximately 280 million people worldwide, of whom a significant segment is users of information systems (IS). Despite this, depressed people remain overlooked in IS research. It is important to examine them because they exhibit neurological differences and thereby impaired, maladaptive decision-making. These decision-making impairments are predicted to implicate human behavior—including the reliance on artificial intelligence (AI), which is reflected through the following AI outputs. Hence, we seek to address the research question—Under what conditions does depression influence human reliance on AI advice? We proposed a depression-focused research model and plan to examine it via an experimental card game. The future findings are expected to extend inclusive, mental health-aware approaches to AI research and design.
Recommended Citation
Liu, Yaru; Cui, Tingru; and Turel, Ofir, "In the Shadow of Depression: Understanding How It Shapes Human Decision-Making Reliance on Artificial Intelligence" (2025). ICIS 2025 Proceedings. 19.
https://aisel.aisnet.org/icis2025/user_behav/user_behav/19
In the Shadow of Depression: Understanding How It Shapes Human Decision-Making Reliance on Artificial Intelligence
Depression affects approximately 280 million people worldwide, of whom a significant segment is users of information systems (IS). Despite this, depressed people remain overlooked in IS research. It is important to examine them because they exhibit neurological differences and thereby impaired, maladaptive decision-making. These decision-making impairments are predicted to implicate human behavior—including the reliance on artificial intelligence (AI), which is reflected through the following AI outputs. Hence, we seek to address the research question—Under what conditions does depression influence human reliance on AI advice? We proposed a depression-focused research model and plan to examine it via an experimental card game. The future findings are expected to extend inclusive, mental health-aware approaches to AI research and design.
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