2024/05/16 by Anjana Wijekoon, Wijekoon, Anjana, David Corsar +7
Computer Science · Decision Sciences · #AI in Service Interactions #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Personal Information Management and User Behavior #Recommender Systems and Techniques
paper · pdf · doi:10.48550/arxiv.2405.10446
openalex publication_date 2024/05/16 · openalex created_date 2024/05/21 · openalex updated_date 2026/07/28
The evolution of Explainable Artificial Intelligence (XAI) has emphasised the significance of meeting diverse user needs. The approaches to identifying and addressing these needs must also advance, recognising that explanation experiences are subjective, user-centred processes that interact with users towards a better understanding of AI decision-making. This paper delves into the interrelations in multi-faceted XAI and examines how different types of explanations collaboratively meet users' XAI needs. We introduce the Intent Fulfilment Framework (IFF) for creating explanation experiences. The novelty of this paper lies in recognising the importance of "follow-up" on explanations for obtaining clarity, verification and/or substitution. Moreover, the Explanation Experience Dialogue Model integrates the IFF and "Explanation Followups" to provide users with a conversational interface for exploring their explanation needs, thereby creating explanation experiences. Quantitative and qualitative findings from our comparative user study demonstrate the impact of the IFF in improving user engagement, the utility of the AI system and the overall user experience. Overall, we reinforce the principle that "one explanation does not fit all" to create explanation experiences that guide the complex interaction through conversation.