Human-AI Interaction in Low- and Middle-Income Countries: Qualitative Study of How Local Human Factors Influence AI...

Artificial intelligence (AI) is rapidly transforming health care and health research, offering new opportunities for improving efficiency, accessibility, and equity. However, the ethical, societal, and regulatory challenges of AI development and deployment are particularly pronounced in low- and middle-income countries (LMICs). While existing literature often emphasizes high-level ethical principles or technical frameworks, there is a notable gap in empirical, qualitative research that centers on human involvement and sociocultural dynamics throughout the AI lifecycle in LMIC contexts.

This study addresses this gap by exploring the role of human involvement across the AI lifecycle, examining how cultural, societal, and governance factors influence AI perceptions and expectations in LMICs.

We conducted 21 qualitative online interviews with AI researchers and innovators across MENA (Middle East and North Africa), Africa, Latin America, and Asia. A semistructured interview guide informed by the KAP (knowledge, attitude, and practice) framework was used. A thematic analysis approach was used for coding and analyzing the transcripts.

We identified five key themes: (1) the necessity of human oversight and the readiness required to support it, (2) the need for AI ethics training, (3) the importance of developing AI systems tailored to local realities, (4) the role of human-centered AI governance, and (5) the value of securing multidisciplinary teams. Findings highlight critical gaps in AI literacy, ethical governance, and interdisciplinary collaboration, emphasizing that AI solutions must be co-designed with local communities to be culturally and contextually relevant.

This study underscores the urgent need for participatory AI development in LMICs and calls for investment in AI education, ethical oversight, and inclusive governance frameworks to ensure that AI serves as a tool for social equity rather than exclusion.