Tell me more: Intent Fulfilment Framework for Enhancing User Experiences in Conversational XAI
This work addresses the need for personalized explanation experiences in XAI for users, though it appears incremental by building on existing user-centered approaches.
The paper tackles the problem of meeting diverse user needs in Explainable AI (XAI) by introducing the Intent Fulfilment Framework (IFF) and Explanation Experience Dialogue Model, which improve user engagement, AI system utility, and overall user experience through conversational interfaces.
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.