Notion of Explainable Artificial Intelligence -- An Empirical Investigation from A Users Perspective
This work addresses the need for user-focused explainability in AI applications, particularly for recommendation systems, but is incremental as it builds on existing XAI research with empirical insights.
The study investigated user-centric explainable AI in recommendation systems through focus group interviews, finding that end users prefer non-technical, tailored explanations with on-demand information and concerns about data usage and reliability.
The growing attention to artificial intelligence-based applications has led to research interest in explainability issues. This emerging research attention on explainable AI (XAI) advocates the need to investigate end user-centric explainable AI. Thus, this study aims to investigate usercentric explainable AI and considered recommendation systems as the study context. We conducted focus group interviews to collect qualitative data on the recommendation system. We asked participants about the end users' comprehension of a recommended item, its probable explanation, and their opinion of making a recommendation explainable. Our findings reveal that end users want a non-technical and tailor-made explanation with on-demand supplementary information. Moreover, we also observed users requiring an explanation about personal data usage, detailed user feedback, and authentic and reliable explanations. Finally, we propose a synthesized framework that aims at involving the end user in the development process for requirements collection and validation.