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：随着人工智能在各类应用领域变得无处不在，为提升非技术用户对系统的透明度和信任度，解释性需求显得尤为关键。尽管可解释人工智能在增强对复杂AI系统的理解方面展现出潜力，但现有的大多数可解释AI方法是为技术型AI专家而非非技术消费者设计的。因此，这些解释往往过于复杂，难以有效引导用户实现其期望的预测结果。本文介绍了当前正在开展的研究工作，旨在构建专门用于指导用户通过改进人机交互实现预期结果的可解释AI系统。文章重点阐述了研究目标与方法、从用户研究中获得的关键发现与启示，并提出了当前亟待解决的开放性问题与挑战，作者计划在未来工作中针对这些问题展开深入研究。