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As systems based on opaque Artificial Intelligence (AI) continue to flourishin diverse real-world applications, understanding these black box models hasbecome paramount. In response, Explainable AI (XAI) has emerged as a field ofresearch with practical and ethical benefits across various domains. This papernot only highlights the advancements in XAI and its application in real-worldscenarios but also addresses the ongoing challenges within XAI, emphasizing theneed for broader perspectives and collaborative efforts. We bring togetherexperts from diverse fields to identify open problems, striving to synchronizeresearch agendas and accelerate XAI in practical applications. By fosteringcollaborative discussion and interdisciplinary cooperation, we aim to propelXAI forward, contributing to its continued success. Our goal is to put forwarda comprehensive proposal for advancing XAI. To achieve this goal, we present amanifesto of 27 open problems categorized into nine categories. Thesechallenges encapsulate the complexities and nuances of XAI and offer a road mapfor future research. For each problem, we provide promising research directionsin the hope of harnessing the collective intelligence of interestedstakeholders.

Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions