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

Title data

Longo, Luca ; Brčić, Mario ; Cabitza, Federico ; Choi, Jaesik ; Confalonieri, Roberto ; Del Ser, Javier ; Guidotti, Riccardo ; Hayashi, Yoichi ; Herrera, Francisco ; Holzinger, Andreas ; Jiang, Richard ; Khosravi, Hassan ; Lecue, Freddy ; Malgieri, Gianclaudio ; Páez, Andrés ; Samek, Wojciech ; Schneider, Johannes ; Speith, Timo ; Stumpf, Simone:
Explainable Artificial Intelligence (XAI) 2.0 : A Manifesto of Open Challenges and Interdisciplinary Research Directions.
In: Information Fusion. Vol. 106 (2024) . - 102301.
ISSN 1566-2535
DOI: https://doi.org/10.1016/j.inffus.2024.102301

Official URL: Volltext

Project information

Project financing: Deutsche Forschungsgemeinschaft
VolkswagenStiftung
Andere

Abstract in another language

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) has emerged as a field of research with practical and ethical benefits across various domains. This paper highlights the advancements in XAI and its application in real-world scenarios and 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. We aim to develop a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 28 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.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Explainable Artificial Intelligence; XAI; Interpretability; Manifesto; Open Challenges; Interdisciplinarity; Ethical AI; Large Language Models; Trustworthy AI; Responsible AI; Generative AI; Multi-Faceted Explanations; Concept-Based Explanations; Causality; Actionable XAI; Falsifiability
Institutions of the University: Faculties > Faculty of Cultural Studies > Department of Philosophy
Faculties > Faculty of Cultural Studies > Department of Philosophy > Chair Philosophy, Computer Science and Artificial Intelligence
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
100 Philosophy and psychology > 100 Philosophy
Date Deposited: 29 Apr 2024 06:53
Last Modified: 29 Apr 2024 06:53
URI: https://eref.uni-bayreuth.de/id/eprint/89424