Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

• DOI: 10.1016/j.inffus.2024.102301
• Publication Date: 2024-02-15T12:31:20Z
• Authors: Luca Longo; Mario Brcic; Federico Cabitza; Jaesik Choi; Roberto Confalonieri; Javier Del Ser; Riccardo Guidotti; Yoichi Hayashi; Francisco Herrera; Andreas Holzinger; Richard Jiang; Hassan Khosravi; Freddy Lecue; Gianclaudio Malgieri; Andrés Páez; Wojciech Samek; Johannes Schneider; Timo Speith; Simone Stumpf
• Keywords: Manifesto; Open research

Understanding black box models has become paramount as systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response, Explainable AI (XAI) emerged a field of research with practical and ethical benefits across various domains. This paper highlights the advancements XAI its application scenarios addresses ongoing challenges within XAI, emphasizing need for broader perspectives collaborative efforts. We bring together experts from fields identify open problems, striving synchronize agendas accelerate By fostering discussion interdisciplinary cooperation, we aim propel forward, contributing continued success. develop comprehensive proposal advancing XAI. To achieve this goal, present manifesto 28 problems categorized into nine categories. These encapsulate complexities nuances offer road map future research. For each problem, provide promising directions hope harnessing collective intelligence interested stakeholders.