User perspectives on AI explainability in aerospace manufacturing: a Card-Sorting study

The integration of AI technologies in aerospace manufacturing is significantly transforming critical operational processes, impacting decision-making, efficiency, and workflow optimization. Explainability in AI systems is essential to ensure these technologies are understandable, trustworthy, and effectively support end-users in complex environments. This study investigates the factors influencing the explainability of AI-based Decision Support Systems in aerospace manufacturing from the end-users' perspective. The study employed a Closed Card Sorting technique involving 15 professionals from a leading aerospace organization. Participants categorized 15 AI features into groups—enhances, is neutral to, and hinders explainability. Qualitative feedback was collected to understand participants' reasoning and preferences. The findings highlighted the importance of user support features in enhancing explainability, such as system feedback on user inputs and error messages with guidance. In contrast, technical jargon was consistently perceived as a hindrance. Transparency of algorithms emerged as the highest-priority feature, followed by clarity of interface design and decision rationale documentation. Qualitative insights emphasized the need for clear communication, intuitive interfaces, and features that reduce cognitive load. The study provides actionable insights for designing AI-based DSSs tailored to the needs of aerospace professionals. By prioritizing transparency, user support, and intuitive design, designers and developers can enhance system explainability and foster user trust. These findings support the human-centric development of AI technologies and lay the groundwork for future research exploring user-centered approaches in different high-stakes industrial contexts.