The Interplay of Data-Driven Organizations and Data Spaces: Unlocking Capabilities for Transforming Organizations in the Era of Data Spaces

Abstract

Artificial intelligence (AI)-based systems, such as AI companions, have been increasingly used to meet the needs of individuals who experience loneliness. In this current study, we sought to identify the mechanism underlying human-AI interactions in the mental health context. We use a Latent Dirichlet Allocation (LDA) approach to analyze a sample of user-generated content consisting of rich data on AI companion app's reviews over a two-year period. We extracted five positive topics (i.e., perceived humanness, perceived emotional support, perceived AI's friendship, perceived (less) loneliness, and mental health benefits) and four negative topics (i.e., perceived lack of conscientiousness, perceived incredibility, perceived violation of privacy, and perceived creepiness of AI) from our analysis. Our AI-based emotional support model suggests that these positive and negative characteristics are interrelated. Our study provides an understanding of the relationship between AI companions and human users in light of research showing the effectiveness of an AI-based intervention for mental health care.

Considering the Role of Human Empathy in AI-Driven Therapy

“…The prospect of using machine learning algorithms for automated health care responses and counseling has long been considered [ 1 ]. Such algorithms could have vast benefits ranging from increased accessibility and affordability of mental health services, reduced waiting times, and personalized treatment options, to the potential to reach underserved populations and combat the escalating loneliness epidemic [ 2 ]. Recent breakthroughs in artificial intelligence (AI) language models have elevated this vision, as evidenced by a growing body of literature indicating varying degrees of efficacy.…”