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The rapid deployment of machine learning systems in high-stakes environments has intensified the need for transparency, accountability, and interpretability. Despite their success, most deep learning and ensemble models remain inherently difficult to interpret, leading to limited trust, regulatory challenges, and reduced usability in real-world decision-making contexts. Explainable AI (XAI) has emerged as a response to this challenge, focusing primarily on post-hoc explanation methods such as feature attribution, saliency maps, or surrogate models. However, these approaches often decouple prediction and explanation, resulting in explanations that may not faithfully reflect the true reasoning of the model.<\/p>\n
Explainable AI\u2013Guided Learning (XAI-GL) addresses this limitation by integrating explainability directly into the learning process. Instead of explaining a model after training, explanations become part of the learning objective itself. This paradigm opens new research directions where interpretability constraints and explanation quality influence model optimization, rather than being evaluated externally. This shift enables the development of systems that are not only interpretable but also inherently designed to be explainable, bridging the gap between human cognitive processes and machine learning representations.<\/p>\n
[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_tta_accordion][vc_tta_section title=”Topics of Interest (But not limited to)” tab_id=”1669785090648-608172ee-d777″][vc_column_text]This special track welcomes original contributions addressing theoretical, methodological, and applied aspects of explainable AI\u2013guided learning. Topics include, but are not limited to:<\/p>\n
[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Paper Submission Guidelines” tab_id=”1778952201377-56c357f8-bbb9″][vc_column_text]All submissions for this special track must be written in English and conform to the Springer LNCS proceedings format with the following page limits: 15 pages for papers including references. Submitted papers will undergo a \u201cdouble-blind\u201d review process, coordinated by the Program Committee. To ensure anonymity of authorship, authors must ensure that authors\u2019 names, affiliations, funding, and any other identifying information of authorship do not appear on the title page or elsewhere in the paper. Authors must provide the complete and final list of authors at the submission stage. No addition, removal, or change in the order of authors is allowed after submission.<\/p>\n
Please use one of the following templates for the LNCS (Lecture Notes in Computer Science) format: https:\/\/www.springer.com\/gp\/computer-science\/lncs\/conference-proceedings-guidelines<\/a><\/span>[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Publication” tab_id=”1699374020537-eb707e08-cc9a”][vc_column_text]<\/p>\n [\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Submissions” tab_id=”1706778996934-13a2f3b1-4aa1″][vc_column_text]All paper submissions for this special track will be via Microsoft CMT<\/strong><\/a> (under \u201cSpecial Track on Explainable AI\u2013Guided Learning”).<\/p>\n The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Important Dates” tab_id=”1669785352215-8e3485c4-ec7d”][vc_column_text]<\/p>\n *All deadlines are 23:59 Anywhere on Earth time.[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Organizer” tab_id=”1778952435278-66ec2e42-1a43″][vc_column_text]<\/p>\n\n\n
\n \u00a0\u00a0Paper Submission<\/b><\/td>\n June 25, 2026<\/span><\/td>\n<\/tr>\n \n \u00a0\u00a0Acceptance Notification<\/b><\/td>\n July 20, 2026<\/td>\n<\/tr>\n \n \u00a0\u00a0Camera Ready<\/b><\/td>\n August 25, 2026<\/td>\n<\/tr>\n \n <\/td>\n <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n