With Artificial Intelligence (AI) becoming ubiquitous in every application domain, the need for explanations is paramount to enhance transparency and trust among non-technical users. Despite the potential shown by Explainable AI (XAI) for enhancing understanding of complex AI systems, most XAI methods are designed for technical AI experts rather than non-technical consumers. Consequently, such explanations are overwhelmingly complex and seldom guide users in achieving their desired predicted outcomes. This paper presents ongoing research for crafting XAI systems tailored to guide users in achieving desired outcomes through improved human-AI interactions. This paper highlights the research objectives and methods, key takeaways and implications learned from user studies. It outlines open questions and challenges for enhanced human-AI collaboration, which the author aims to address in future work.

翻译：随着人工智能在各应用领域的普及，增强非技术用户透明度和信任度的解释需求愈发关键。尽管可解释AI（XAI）在提升复杂AI系统理解方面展现出潜力，但大多数XAI方法针对技术型AI专家而非非技术用户设计。因此，此类解释往往过于复杂，鲜少能引导用户实现期望的预测结果。本文展示了旨在通过改进人机交互来引导用户达成预期结果的定制化XAI系统的持续研究。文章重点阐述了研究目标与方法、用户研究的核心发现与启示，并揭示了增强人机协作的开放性挑战与待解决问题，作者计划在未来工作中予以解决。