ConvXAI: Delivering Heterogeneous AI Explanations via Conversations to Support Human-AI Scientific Writing
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While various AI explanation (XAI) methods have been proposed to interpret AI systems, whether the state-of-the-art XAI methods are practically useful for humans remains inconsistent findings. To improve the usefulness of XAI methods, a line of studies identifies the gaps between the diverse and dynamic real-world user needs with the status quo of XAI methods. Although prior studies envision mitigating these gaps by integrating multiple XAI methods into the universal XAI interfaces (e.g., conversational or GUI-based XAI systems), there is a lack of work investigating how these systems should be designed to meet practical user needs. In this study, we present ConvXAI, a conversational XAI system that incorporates multiple XAI types, and empowers users to request a variety of XAI questions via a universal XAI dialogue interface. Particularly, we innovatively embed practical user needs (i.e., four principles grounding on the formative study) into ConvXAI design to improve practical usefulness. Further, we design the domain-specific language (DSL) to implement the essential conversational XAI modules and release the core conversational universal XAI API for generalization. The findings from two within-subjects studies with 21 users show that ConvXAI is more useful for humans in perceiving the understanding and writing improvement, and improving the writing process in terms of productivity and sentence quality. Finally, this work contributes insight into the design space of useful XAI, reveals humans' XAI usage patterns with empirical evidence in practice, and identifies opportunities for future useful XAI work.
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