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

Towards Directive Explanations: Crafting Explainable AI Systems for Actionable Human-AI Interactions