Explainable AI System The Proposed SmaSer (SS) Analytical Framework.pdf

The research on eXplainable Artificial Intelligence (XAI) has significantly increased, and XAI has been viewed as focused on interpretation of machine learning (ML) models and formation of explanations. However, the black-box nature of AI technical process has led to difficulties in comprehending the systematic workings, particularly in innovative smart services. This study aimed to increase transparency of the AI technical application and process through user experience (UX) investigation. This study proposes an analytical framework: SmaSer (SS), and it focuses on the process of understanding how smart services achieve user experience objectives while analyzing user needs, which, by creating co-creation tasks, drives the interaction between AI-enabled design and users. To meet this goal, a design team developed the FFRDSS systematic design framework for online-to-offline shopping services focused on fresh flower products, grounded in marketing needs, the flower industry, and the e-commerce environment. This empirical study explores the effective use of the SS framework in smart-service concept design, examines its application in AI-enabled design.