  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.
