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The growing attention to artificial intelligence-based applications has ledto research interest in explainability issues. This emerging research attentionon explainable AI (XAI) advocates the need to investigate end user-centricexplainable AI. Thus, this study aims to investigate usercentric explainable AIand considered recommendation systems as the study context. We conducted focusgroup interviews to collect qualitative data on the recommendation system. Weasked participants about the end users' comprehension of a recommended item,its probable explanation, and their opinion of making a recommendationexplainable. Our findings reveal that end users want a non-technical andtailor-made explanation with on-demand supplementary information. Moreover, wealso observed users requiring an explanation about personal data usage,detailed user feedback, and authentic and reliable explanations. Finally, wepropose a synthesized framework that aims at involving the end user in thedevelopment process for requirements collection and validation.

Notion of Explainable Artificial Intelligence -- An Empirical Investigation from A Users Perspective