  As systems based on opaque Artificial Intelligence (AI) continue to flourish
in diverse real-world applications, understanding these black box models has
become paramount. In response, Explainable AI (XAI) has emerged as a field of
research with practical and ethical benefits across various domains. This paper
not only highlights the advancements in XAI and its application in real-world
scenarios but also addresses the ongoing challenges within XAI, emphasizing the
need for broader perspectives and collaborative efforts. We bring together
experts from diverse fields to identify open problems, striving to synchronize
research agendas and accelerate XAI in practical applications. By fostering
collaborative discussion and interdisciplinary cooperation, we aim to propel
XAI forward, contributing to its continued success. Our goal is to put forward
a comprehensive proposal for advancing XAI. To achieve this goal, we present a
manifesto of 27 open problems categorized into nine categories. These
challenges encapsulate the complexities and nuances of XAI and offer a road map
for future research. For each problem, we provide promising research directions
in the hope of harnessing the collective intelligence of interested
stakeholders.
