Call for Papers

SCOPE

In the future people will collaborate more and more with machines to solve complex problems using AI techniques. Such a collaboration requires adequate communication, trust, clarity and understanding. eXplainable AI (XAI) aims at addressing such challenges by combining the best of symbolic AI and Machine Learning including neural models, evolutionary computing and fuzzy systems. Such topic has been studied for years by all different communities of AI, with different definitions, evaluation metrics, motivations and results. In addition to technology, this involves social and legal issues as well as a wide range of real-world applications and domains. Both interpretability by design methods and post-hoc methods for explaining complex models have been proposed and investigated. Research has also redirected its emphasis on the structure of explanations and human-centered Artificial Intelligence, recognizing that the ultimate users of interactive technologies are humans. This conference aims at attracting different research perspectives to promote debate. It intends to be a major multidisciplinary and interdisciplinary forum, bringing together academics and scholars of different disciplines, interested in the study, analysis, design, modelling and implementation of interpretable and explainable AI systems. Contributions are welcome both in addressing theoretical issues and in a broad range of application fields.

CONFERENCE AREAS

Each of these topic areas is expanded below but the sub-topics list is not exhaustive. Papers may address one or more of the listed sub-topics, although authors should not feel limited by them. Unlisted but related sub-topics are also acceptable, provided they fit in one of the following main topic areas:

AREA 1: TECHNOLOGY

• XAI using Machine Learning
• Deep Learning and XAI
• Fuzzy Systems and Logic for XAI
• Knowledge Graphs in XAI
• Explainable Graph Neural Networks
• Explainable Neuro-Symbolic Reasoning
• Evaluation of Explainability
• Argumentative-Based Approaches for XAI
• Bayesian Modelling for Interpretability
• Human-Computer Interfaces Supporting XAI
• Natural Language Processing and XAI
• Ontologies Supporting XAI
• Metrics for Explanations
• XAI Benchmarking
• Evolutionary XAI Approaches
• XAI for Evolutionary Computing
• Post-Hoc Methods for Explainability
• Model-Specific vs Model-Agnostic Methods for Explainability
• Mechanistic Interpretability

AREA 2: SOCIAL AND LEGAL ISSUES

• Ethical Concerns of XAI
• Accountability and Responsibility
• Explainable Bias and Fairness of XAI Systems
• Model Accuracy and Interpretability
• Explainability Pitfalls and Problems in XAI
• Prevention/Detection of Deceptive AI Explanations
• Trust Management and Reputation
• Adversarial Attacks Explanations

AREA 3: APPLICATIONS

• Healthcare and Biomedical Sciences
• Human/AI Cooperation
• Decision-Support Systems
• Recommender Systems
• Computer Vision Applications
• Robotics and Control Systems
• Explaining Object and Obstacle Detection
• Explainable Methods for Finance
• Explainability in Transportation Systems
• Internet of Things
• Security and Privacy

KEYNOTE SPEAKERS

EXPLAINS 2026 will have several invited keynote speakers, who are internationally recognized experts in their areas. Their names are not yet confirmed.

PAPER SUBMISSION

PUBLICATIONS

IMPORTANT DATES

SECRETARIAT

EXPLAINS Secretariat
Address: Avenida de S. Francisco Xavier, Lote 7 Cv. C
             2900-616 Setúbal - Portugal
Tel.: +351 265 520 185
Fax: +351 265 520 186
e-mail: explains.secretariat@insticc.org
Web: https://explains.scitevents.org

VENUE

CONFERENCE CHAIR

Available soon.

PROGRAM CO-CHAIRS

PROGRAM COMMITTEE MEMBERS