Automated machine learning to foster AI development processes: an explorative study

Abstract

Communication barriers between citizen developers and AI experts are a serious challenge for AI system development in the age of No and Low Code (LCNC) AI development. Automated Machine Learning (AutoML) has emerged as a promising approach to democratize AI development by enabling non-technical stakeholders to participate in the creation of AI systems. However, its role in supporting collaboration between citizen developers and AI experts remains underexplored. This study examines AI experts' perceptions of AutoML's potential as a boundary object that facilitates communication and collaboration between citizen developers and AI experts. An exploratory qualitative interview study was conducted with 14 AI experts from various industries to examine their perceptions of how GUI-based low- or no-code AutoML prototypes may support collaboration. The findings indicate that AutoML can provide a shared frame of reference that supports knowledge exchange, mutual understanding, and interdisciplinary teamwork. Interviewees highlighted that AutoML-generated insights and early-stage prototypes facilitate iterative, feedback-driven development processes and help overcome data and modeling barriers. However, the effective integration of AutoML requires appropriate governance structures, including clear responsibilities, guided processes, and structured stakeholder interaction. This exploratory study highlights AI experts' perceptions that AutoML may serve not only as a technological tool but also as a potential facilitator of collaboration in AI development. However, democratizing AI development requires more than accessible tools alone and depends on well-managed communication and engagement processes. The study contributes to a deeper understanding of AutoML's role as a boundary object and highlights the need for further research into its responsible and inclusive deployment.