Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
It is anticipated that apps based on artificial intelligence (AI) will be instrumental in mitigating the global shortage in mental healthcare. One important purpose of such apps is to encourage users’ self-help. This study is dedicated to examining the potential role of explainable AI (XAI) for mental health apps. We build on mental health literature to conceptualize potential effects of explanations in terms of patient empowerment. We implement an online experiment with a fully instantiated mental health app based on a real-world dataset. The randomized between-subject experiment is conducted with 409 participants to test the effectiveness of feature importance and counterfactual explanations on patient empowerment, intention to use, and intention to act. Our results show that the provision of counterfactual explanations alongside AI-generated predictions of depression risk in a mental health app can significantly increase users’ intention to use and empowerment.
Recommended Citation
Bottesch, Sven; Terhorst, Yannik; and Förster, Maximilian, "Unlocking Empowerment: An Empirical Study on the Impact of Explainable AI in Mental Health Apps" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 5.
https://aisel.aisnet.org/hicss-58/da/xai/5
Unlocking Empowerment: An Empirical Study on the Impact of Explainable AI in Mental Health Apps
Hilton Waikoloa Village, Hawaii
It is anticipated that apps based on artificial intelligence (AI) will be instrumental in mitigating the global shortage in mental healthcare. One important purpose of such apps is to encourage users’ self-help. This study is dedicated to examining the potential role of explainable AI (XAI) for mental health apps. We build on mental health literature to conceptualize potential effects of explanations in terms of patient empowerment. We implement an online experiment with a fully instantiated mental health app based on a real-world dataset. The randomized between-subject experiment is conducted with 409 participants to test the effectiveness of feature importance and counterfactual explanations on patient empowerment, intention to use, and intention to act. Our results show that the provision of counterfactual explanations alongside AI-generated predictions of depression risk in a mental health app can significantly increase users’ intention to use and empowerment.
https://aisel.aisnet.org/hicss-58/da/xai/5