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

Longo, L. et al. (2024) Explainable Artificial Intelligence (XAI) 2.0: a manifesto of open challenges and interdisciplinary research directions. Information Fusion, 106, 102301. (doi: 10.1016/j.inffus.2024.102301)

Abstract

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.

• Item Type: Articles
• Additional Information: T. Speith acknowledges funding support provided by the Volkswagen Foundation grants AZ 9B830, AZ 98509, and AZ 98514 “Explainable Intelligent Systems” (EIS; https://explainable-intelligent.systems) and by the DFG grant 389792660 as part of TRR 248 (https://perspicuous-computing.science). J. Del Ser acknowledges funding support from the ‘Centro para el Desarrollo Tecnologico Industrial’(CDTI), by the European Union (AI4ES, grant no. CER-20211030), and by the Basque Government (MATHMODE, ref. IT1456-22). F. Herrera acknowledges funding support by the Spanish grant PID2020-119478GB-I00 funded by MCIN/AEI/10.13039/501100011033. R. Confalonieri acknowledges funding support from the ‘Neuro-symbolic XAI’ project (BIRD231830). J. Choi is supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00984). R. Guidotti acknowledges funding support provided by the funding schemes ERC-2018-ADG G.A. 834756 “XAI: Science and technology for the eXplanation of AI decision making” (https://xai-project.eu/), “INFRAIA-01-2018-2019 – Integrating Activities for Advanced Communities”, G.A. 871042, “SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics” (http://www.sobigdata.eu), by the European Commission under the NextGeneration EU programme National Recovery and Resilience Plan (Piano Nazionale di Ripresa e Resilienza, PNRR) – Project: “SoBigData.it – Strengthening the Italian RI for Social Mining and Big Data Analytics” – Prot. IR0000013 – Avviso n. 3264 del 28/12/2021, and M4C2 - Investimento 1.3, Partenariato Esteso PE00000013 - “FAIR - Future Artificial Intelligence Research” - Spoke 1 “Human-centered AI’, and by the Italian Project Fondo Italiano per la Scienza FIS00001966 MIMOSA. A. Holzinger acknowledges funding support by the XAI Project of the Austrian Science Fund FWF P-32554. F. Cabitza acknowledges funding support provided by the Italian project PRIN 2022 PNRR 1409/22, InXAID - Interaction with eXplainable Artificial Intelligence in (medical) Decision making. CUP: H53D23008090001 funded by MUR. W. Samek acknowledges funding support from the German Research Foundation (DFG) as research unit DeSBi (KI-FOR 5363).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Stumpf, Dr Simone

Creator Roles

Stumpf, S.: Conceptualization, Investigation, Methodology, Validation, Writing – review and editing

• Authors: Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Del Ser, J., Guidotti, R., Hayashi, Y., Herrera, F., Holzinger, A., Jiang, R., Khosravi, H., Lecue, F., Malgieri, G., Páez, A., Samek, W., Schneider, J., Speith, T., and Stumpf, S.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Information Fusion
• Publisher: Elsevier
• ISSN: 1566-2535
• ISSN (Online): 1872-6305
• Published Online: 17 February 2024
• Copyright Holders: Copyright © 2024 The Author(s)
• First Published: First published in Information Fusion 106:102301
• Publisher Policy: Reproduced under a Creative Commons licence