Cross-Cultural Considerations for Designing AI Mental Health Applications: Ten Practical Recommendations

Artificial Intelligence (AI)-powered mental health applications offer immense potential to democratize mental health services globally, especially in Low- and Middle-Income Countries (LMICs), where traditional mental health resources are scarce. However, early AI applications in mental health, particularly language-based models designed to detect psychopathology, have predominantly relied on datasets sourced from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) populations, overlooking significant demographic and cultural variations. Given significant variation across populations, not only in clinical presentations but also in the availability of resources for treatment delivery, it is crucial to culturally and contextually tailor AI mental health applications to the unique culture and context of geo-cultural populations. We propose ten recommendations for developing culturally responsive AI mental health applications.